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Many big data projects fail, learn why and how to prevent that from happening to your project.
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Creating Big Data Success with the Collaboration of Business and IT Teams
By Edward Chenard
Edward Chenard- Started big data at Best Buy, was working in big data at GE before it was called big data.- Set up one of the first hadoop clusters in Retail and the Midwest.- Won tax innovation credits for my work on big data- Tekne finalist for big data innovation- Set up big data, data science and data visualization teams- Managed teams as large as 300 with product portfolios of over $4B- I spend my time in cold places
Twitter: Echenard
Slideshare: Echenard
Everyone is Jumping on to Big Data
The Reality of Big Data• As many as 3/4 of big data projects fail according to one Gartner study. • The third is that 39 percent of the failure of Big Data project is attributed to the
fact the data is siloed and there’s not a lot of cooperation in gaining access to that data. Now that is the oldest problem in the history of IT. - Infochimps
• 1. They focus on technology rather than business opportunities.• 2. They are unable to provide data access to subject matter experts.• 3. They fail to achieve enterprise adoption.Terradata's top three reasons why big data projects fail.
• Lack of alignment. Business and IT groups are not aligned on the business problem they need to solve but instead are tackling it from a technology perspective. Lack of true commitment from business stakeholders also makes alignment harder to achieve. Peter Sheldon - Forrester Analyst
Big Data at Most Companies
Big Data
IT Business
How a typical big data project takes place
• Someone hears about big data and then seeks funding.
• Other teams want to own it. Months of fighting takes place over ownership.
• The opportunity is either lost or the mission of the project gets altered.
• Teams work in silos, poor communication takes place as teams spend more time playing CYA. Achievement: Project failure with cost over runs, deadlines missed and lack of focus.
No One Team can Handle Big Data Alone
Current state of big data collaboration
Business(Strategy)
IT(Systems)
Analyst(Insights)
What does Big Data Really Mean to Business
“The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it's going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for
elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.”
Hal Varian
Everything is
about discovery
Why a focus on collaboration?
• Projects fail for simple reasons, lack of understanding the need for better collaboration and then knowing how to implement that collaboration, helps to ensure success.
• Failure does not need to be an option• Big data is the future of how we live and work,
but only if we get it right. Big data can be bigger than ecommerce in terms of impact on how we live.
Everyone Discovers
“The Data Discoverers looks a lot like you and me, but what’s different is their preoccupation with personal data.
They are relentlessly digital, they obsessively record everything about their personal lives, and they think that data is sexy. In fact, the bigger the data, the sexier it becomes.
Their lives - from a data perspective, at least - are perfectly groomed.”
data as a lifestyle
Data Discoverers
Data Discoverers are setting the trend in what will be common place in just a few short years.
More people will want to use their data and the consumerization of data and technology will continue.
As this trend goes, only organization that learn to merge the various disciplines of strategy, analytics and IT, will be successful
data as a lifestyle
Data Discoverers
How We Need to Look at Discovery
Horizon
Past
Discovery is the leading emerging interaction category of the Age of Insight
Complex ecosystems:
multi-channel experiences
Service models
everyware environments
Reactive datadynamic perspectives
Activity Centered Thinking
“Understand the quality performance of a system so I can better determine if I need to replace it.” - IT
“Understand a portfolio's exposures to assess portfolio-level investment mix.” - Strategy Manager
“I need to understand the customer trends in the data so I can better create models.” - Analyst
How Different Functions See the Same Issue
Identifying Modes
“Understand the quality performance of a system so I can better determine if I need to replace it.” - IT
“Understand a portfolio's exposures to assess portfolio-level investment mix.” - Strategy Manager
“Understand the customer trends in the data so I can better create models.” - Analyst
Mode = ‘Comprehend’ (understand)
Comprehending
‘To generate insight by understanding the nature or meaning of an item or data set’
e.g. “I need to analyze and understand consumer-customer-market trends to inform brand strategy & communications plan” – Director, Brand Image
Each Team has the same goal, to understand, what they may want to understand is often different but not exclusive or limit to the other team’s need to understand.
Identifying Modes
“I need visibility into the systems my colleagues are using in order to maximize the network ROI for the company.” - IT
“I need to identify customers/marketers/dealers failing & at risk of de-branding based on performance problems.” - Strategy
“I need to identify the best customer/consumer/region targets for our brand/products.”- Analyst
Mode = ‘Explore’
Modes are the verbs of discovery scenarios.
Locate
Verify
Monitor
Compare
Comprehend
Explore
Analyze
Evaluate
Synthesize
9 distinct modes
Where to Start
The Business Value Framework
Timeliness Automated and prompted
More Products Production Efficiency
Ease of Data Collection
Business value
Ease of Implementation
Customer Needs
Perceived Value
Pre-recorded Different
Products
Production Flexibility
Customer’s Wallet Share
Customer Acceptance
Value PerceivedEase of Data Collection Customer’s Wallet Share
Business value
Initiatives
Ease of Implementation
Customer Acceptance
Focus on Customers
Focus on Internals
How work gets structured
Vision & Goals
Governance
Execution
Clearly articulated vision for personalization and recommendations, precisely defined goals with how to measure. Defined scope of the product.
Market strategy, customer segmentation, prioritization, org focus, measurement and incentive systems
Production process, flexibility at scale, efficiency, relationship management, benchmarking, metrics, initiatives
Framing Collaboration
28
Big Data Collaboration
Value (Shared): Show me the money!?!
Analyst: Who, How, Where?
IT: What Tools and Why
Strategy: Where are you headed?
- Measurable Results- Multi-Channel Case Studies
- MapReduce, Hadoop- Cassandra, The Cloud- Pig, Hive,- HDFS
- Buy vs. Build- Open source options- Alignment with Analytical Infrastructure- Speed to Market- Privacy Considerations
- Data Scientist vs. Statistician- Where to find talent?- Retain, Train- Offshore vs. Onshore- University involvement
Always Remember: Data, Insights, Actions
Listen
•Listen to the data streams
Share
•Share the data with the rest of the organization
Engage
•Engage to the data to find the insights
Innovate
•Innovate new ideas from the insights gained from the data
Perform
•Perform insightful actions from the data to create better customer experiences
Collaboration helps to achieve where others fail.
Thank You!
• Edward Chenard – Twitter: Echenard– Email: [email protected]– Blog: CrossChannelPrairie.com
Resources• Why Do Big IT Projects Fail So Often? (
http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/why-do-big-it-projects-fail-so-often/d/d-id/1112087?)
• A statisticians view of big data (http://www.slideshare.net/kuonen/a-statisticians-view-on-big-data-and-data-science#!)
• Using Big Data to Create a Data Driven Organization (http://www.slideshare.net/echenard/using-big-to-create-a-data-drive-organization)
• The Language of Discover (http://www.slideshare.net/moJoe/designing-big-data-interactions-using-the-laguage-of-discovery?utm_source=slideshow&utm_medium=ssemail&utm_campaign=download_notification)
• Images by Emma Kim, Jason Maehl, David Spurdens, Evgenia Shadrina and Hill Street Studio