MAGAZINE

Building an Analytics First Organization

Thought Paper

IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

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$10

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$30

$40

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2011 2012 2013 2014 2015 2016 2017

$11.8

$18.6

$28.5

$38.4

$45.3

$50.1

Big Data Market Forecast 2011 - 2017 (in $ US billion)

AnalyticsApproach

Intention

Initiative Information

InsightInfluence

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

Easy-to-integrate Presentation LayerActionable Dashboards User Interface for real time,

easy to consume insights

Data Augmentation(Multiple data sources - Internal)

Consumption Layer

Hardware, Software Architecture

IndustryTrends

BusinessScenarios

BusinessRules

HybridAlgorithm

Data Management(Integrate, Clean, Create Attributes, Transform)

Customizable Algorithms

ResuableFramework

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

The agility comes with your ability to get the better tool or model better suited to the job -- going through that TEST-LEARN-ADAPT CYCLE quickly

- Vince Jeffs (Pegasystems)

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

Domain Analysis

Technology

Alignment, Addressable& ComprehensiveDomain research to ensure comprehensiveness and alignment with business need

Accurate, Transparent & DefensiblePredictive analytics to ensure accuracy, clarity, and defensibility

Granular, Accessible & TimelyTechnology tools to make insights accessible and actionable across the organization

Building an Analytics First Organization www.bridgei2i.com

INFORMATION INSIGHT IMPACT

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

Sustain

SimplifySolve

DataVisualization

Expertise

PredictiveAnalytics

Algorithms

DomainExpertise &Frameworks

Building an Analytics First Organization www.bridgei2i.com

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IntroductionIn today’s digital age, most organizations are aware of the multitude of benefits that analytics has to offer, including enhanced decision-making, increased customer satisfaction and retention, decreased operational expenditure, reduced risk and fraud, etc. According to IDC, the global big data and business analytics market was valued at $122 billion in 2015 and will likely reach $187 billion in 2019. The figure below illustrates the tremendous growth of the big data segment alone.

Source: Wikibon

Clearly, more enterprises worldwide are increasingly adopting big data analytics. However, many enterprises seem to think of analytics as a quick fix for their business needs. This idea is far from the truth; investing in analytics is, in fact, only the beginning of a journey. Achieving business goals with the aid of analytics requires an effective analytics deployment strategy. Enterprises such as Google, Amazon, Netflix, and Starbucks have been successful in these endeavors and were, therefore, able to establish themselves as juggernauts in their respective domains.

At a recent panel discussion hosted by BRIDGEi2i at CYPHER 2016 (an analytics summit), Soumen De, EGM at General Motors Technical Centre (India), explained the concept of Five I’s that they deployed for defining their analytics journey.

Intention: The application of analytics should be driven by a clear business objective or intent.

Information: Factors such as availability and quality of data are critical to the strategy. Defining a data collection and storage system is a must.

Insights: Data science capabilities must be leveraged to make sense of the collected data, be it structured or unstructured. This includes developing algorithms and choosing the right tool for text mining, clustering, etc.

Influence: The post-analytics insights should be contextualized with respect to business interests, and the stakeholders must be influenced into acting upon the insights.

Initiative: All the efforts should converge on the action - making the right business decisions for achieving business objectives.

This five I’s framework may be unique to GM India but it is crucial for businesses to build their own blueprint for an analytics first culture, one where data and analytics drive business strategies and decision-making for the organization. This thought process is what separates data-driven organizations, such as Google, Amazon, Netflix, and Starbucks, from others.

Analytics First Culture

Organizations that have successfully formulated the right analytics approach not only realize significant ROI from analytics investments but also witness sustained business growth. Achieving the desired business results has allowed these organizations to expand their analytics footprint to facilitate a wide array of business undertakings. They have evolved into organizations that don’t just deploy analytics as a strategy but use analytics to drive strategy. Once you reach that stage, analytics becomes pervasive and you start to see incremental value across the board. This is the basis of an analytics first culture.

USE CASE: Let’s take Starbucks as an example. Starbucks is the largest coffee chain in the world with net revenues of more than $16 billion. The company has been quite vocal about the widespread use of analytics across its business operations, and the results are for everyone to see. Starbucks employs big data analytics right from understanding customer preferences to determining the best locations for new stores.

To elaborate further, let us consider one of its star locations as an example – Harvard Square. Starbucks opened four stores at this location within a one-mile radius. Logic would have one believe that this will hurt the profits of the individual stores due to self-cannibalization. However, Starbucks used analytics insights to determine the optimal store locations and customized the menu offerings of each store. End result? Each of these stores is bustling with customers and more dollars in the balance sheet. This is how analytics first organizations stand out. They don’t use analytics as needed; analytics is integral to everything they do as a company.

We have established the premise that fostering an analytics-first culture has significant benefits. However, it comes with its own set of challenges. Most enterprises today have deployed analytics to some extent but the real problem lies in adoption. Without a clear analytics strategy, users don’t see apurpose and that’s a surefire recipe for failure. The problem then compounds to a perceived lack of value from analytics and difficult questions related to analytics RoI. There are a number of organizations that lose their way despite investing heavily in analytics. This eventually leads to downsizing of analytics investments and using analytics reactively as a rear view mirror (historical data analysis).

Coming to solving this puzzle; how should organizations approach building an analytics first philosophy? While there could be many nuances based on industry type, operating model, customer lifecycle, etc., the common thread is the importance of adopting a deployment model that will ensure sustained value from analytics.

Analytics Deployment Models

Enterprises need to get past discussions pertaining to how important analytics is for achieving business objectives. Instead, the focus needs to be on how value can be generated from analytics deployment. BRIDGEi2i’s whitepaper The Last Mile of Analytics outlines value generation from analytics in great detail. Deployment models have evolved significantly over the past decade from business process outsourcing days to knowledge process outsourcing to present day hybrid models.

Some of the traditional deployment models are:

Centralized: A single central unit with a resource pool that caters to the needs of all departments

De-centralized: A dedicated unit for each department

Functional: A dedicated unit for functions with unique needs

With evolving business dynamics and technology taking center stage, successful companies like Amazon and Google have redefined the approach and adopted “the hybrid deployment model”. The hybrid deployment model brings the right mix of services, capabilities, and technology enablement for enterprises.

Hybrid Deployment Model

Today boardroom conversations revolve around narratives such as business impact, return on investment, operational efficiency, and innovation. Traditional analytics deployment models have not been able to keep up with changing business needs. The hybrid approach infuses agility into the system and enables better monetization of enterprise data. With this approach, organizations can build an analytics ecosystem that aligns with their objectives, drives better adoption, and delivers better RoI. The five I’s approach adopted by GM India is a great example of the hybrid model. The blueprint details the business intent, as well as a complete understanding of the data ecosystem, the systems needed for generating insights, outline of strategic efforts for management sponsorship, and the close looping of the process by making analytics actionable.

Source: Analytics – A Hybrid Approach (BRIDGEi2i Whitepaper)

Some of the benefits of hybrid solutions are as follows:

• Reduction in implementation time

• Flexible in terms of customization

• Accurate and granular recommendations that are easily accessible

• Easy adoption without the need for making changes to the core platform

• Strong customer support

USE CASE: Red Roof Inn, a hotel chain with more than 450 establishments in the US, is able to factor in new customer demands with great agility and derive actionable insights from relevant data. For example, its business units discovered potential business opportunities after a detailed study of data related to historical weather information, travel patterns, and flight cancellation. They noticed that around 3% of flights are canceled every day, leaving about 90,000 passengers stranded. The hotel chain devised a marketing strategy to send targeted advertisements to these passengers’ mobile devices. The chain started sending well-timed advertisements containing personalized messages, which subsequently led to a broadened customer base. Overall, the strategy led to a 10% year-over-year growth in business.

With the hybrid model, analytics permeates the organizational DNA and people start to tap into the power of analytics, not reactively but as a way of doing business. The journey does not end with using analytics to solve a business problem once or discovering potential opportunities. The impact comes from the ability to sustain the success. That’s where establishing a Centre of Excellence (CoE) is critical. A CoE can help build a strategic roadmap and processes for effective analytics execution and ensuring all the learnings are leveraged as best practices. This may include improving data collection and management practices, implementing and driving adoption of the right technology enablers, facilitating change management, etc.

With the right mix of capabilities, processes, and tools, the hybrid model enables analytics first organizations to extract the most of their analytics investments.

Stakeholders for Analytics Success

The synergy between analytics teams, IT organization, and business units is of paramount importance for an analytics first approach. The IT organization needs to play a pivotal role in the building, managing, and governance of a robust data ecosystem. CxOs need to be on the same page when it comes to employing analytics for crucial business decisions.

The hybrid deployment model also entails breaking large tasks into smaller projects that can be individually deployed, tested, and developed. Such kind of flexibility will allow for quicker insights. Further, data scientists, predictive modelers, and engineers need to keep abreast of emerging technologies and trends. They should be able to anticipate how these changes or advances will affect business and make necessary changes to analytics operations as and when required.

Analytics Maturity Assessment

Organizations need to have an analytics maturity assessment process in place to determine where they stand in terms of analytics implementation and understand how to get the most value from their data. Maturity assessment primarily involves:

• Understanding the effectiveness of deployment, governance, infrastructure, prioritization, and roles of stakeholders

• Tracking success rates with respect to set benchmarks at department and organizational levels

• Updating analytics models as per changing business needs

Significance of Managed Analytics

Analytics projects, in general, are complex with a lot of moving parts and technicalities. According to Gartner, big data and analytics projects have high failure rates (>50%). The lack of a hybrid analytics approach can be attributed to this high failure rate.

Every enterprise should have the capabilities required for leveraging data to achieve the highest possible levels of business success. Lack of technical expertise should not be a hindrance to implementing analytics strategies, and this is where managed analytics comes into the picture.

Organizations that lack advanced analytics expertise and a dedicated data team internally can take the managed analytics route to leverage the best of both worlds - focus on core business strategy and leave the analytics expertise and technology deployment to the experts.

BRIDGEi2i uses a combination of advanced analytics capabilities, domain expertise, and technology enablers to deliver sustainable impact. It also uses a unique impact-based engagement model: the “Solve, Simplify & Sustain” approach. Solve: Domain expertise and frameworks are used to understand business challenges. Also, business taxonomy and data sources are defined.

Simplify: Interactive and context-aligned visualizations are generated to derive insights in line with business needs.

Sustain: A CoE monitors the effectiveness and performance of the analytics operations and suggests enhancements with respect to the current technological landscape and business demands, thereby sustaining an analytics first culture.

Conclusion

Rapid technological advances and changing customer demands are among the factors that will continue to intensify market competition. Enterprises in various industry verticals will, therefore, need to nurture and sustain an analytics first culture to build, improve, or facilitate business operations and stay relevant.

Evolving into an analytics first organization is a strategic journey, which requires support from different organizational levels, especially the executive level. The business unit, the analytics team, and the IT team within an organization need to work together to build a sustainable analytics-driven culture. Moreover, analytics is not a quick fix to business woes; significant ROI is realized when analytics operations are fine-tuned over time.

Although there are standard analytics deployment models to choose from, the adoption of a hybrid or flexible deployment model will allow for quick changes without negatively affecting the overall analytics process. These changes may include the addition or removal of stakeholders, incorporation of new models or platforms, etc.

Organizations that do not possess the expertise required to build an analytics first culture can seek managed analytics services. These services can provide organizations with the required resources and guidance that will aid in setting up the most suitable analytics process. Organizations can, therefore, embed analytics into their DNA and achieve accelerated business outcomes.

About BRIDGEi2i Analytics Solutions

BRIDGEi2i provides business analytics solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems, and make data-driven decisions across pan-enterprise processes to create sustainable business impact.

For more details contact us: enquiries@bridgei2i.com

India Office Umiya Business Bay, Tower 2, 2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bangalore-560037 Phone: +91-80-67422100

U.S. Office42808 Christy St., Suite 226, Fremont, CA, 94538, Phone: +1-650-666-0005

An organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage

- Jack Welch