Cognizant

Business Analytics Architecture

Table of ContentsIntroduction..............................................................................................................................................................2

What is Business Analytics?................................................................................................................................2

Why business Analytics is important?.................................................................................................................3

Role of Analytics in business..................................................................................................................................5

Business Analytics - Industry focus areas...............................................................................................................7

Business Analytics Reference Architecture.............................................................................................................8

Data Layer...........................................................................................................................................................9

Why data layer is so important?......................................................................................................................9

Data Layer Systems.......................................................................................................................................10

Structured Data..............................................................................................................................................10

Unstructured Data..........................................................................................................................................10

Analytics Access Layer.....................................................................................................................................10

Historical Analysis.........................................................................................................................................11

Current Analysis............................................................................................................................................12

Future Analysis (Analytics)...........................................................................................................................12

Performance Analytics Applications (Current and Future)...............................................................................13

Delivery Medium...............................................................................................................................................13

Security..............................................................................................................................................................14

High Level Analytic Capabilities – Industry Agnostic..........................................................................................16

How do we Implement Business Analytics Solutions?.........................................................................................17

Why Cognizant?....................................................................................................................................................18

References :...........................................................................................................................................................19

Introduction Businesses Analytics have evolved rapidly over the last few years. On one side there have been many technological advancements and generation of lot of data, on the other side it has given in the following challenges:

Too much data, but no insight. Delay in availability of information when needed. Too many variety of data to be integrated – structured, logs, web, email, machine data, social media

feeds, and blogs across different sources with the right set of rules.

This mandates the need for a good enterprise Information system to be able to answer the following questions:

What has happened? What are the factors that led it to happen? When and where has it happened? What is happening now? Do we see a trend or pattern emerging? What factors can influence a better outcome for business? After making the changes, are the desired outcomes as expected?

What is Business Analytics? The true encashment of data as an asset lies in delivering the right set of information through right visualization to the right internal and external users. This enables realization of business objectives by the right set of information visualization techniques, measuring and monitoring the business drivers via these information sets. Any impediment here results in improper, inaccurate and untimely visibility into vital metrics and key performance indicators. This leads to losing market share (customers), poor performance (financial, operational), inability to service external demands for information (regulatory/compliance) and losing competitive advantage. Therefore, it is extremely essential that overall integration, presentation and delivery of information is required in a process, architecture and technology. This combination is what we refer to as “Business Analytics”

Business Analytics is about discovering and delivering facts, insights and patterns to business users. All this is based on an integrated enterprise data set. Business Analytics have two main characteristics. It is based out of data and converts the data to information, relevant knowledge and intelligence useful to the business. Analytics tools can help us visualize and make the right business choices.

Why Business Analytics is important? We spend valuable time and money every day in ensuring that appropriate business decisions are based upon solid, accurate data and information. Analysts typically spend 80% of their time retrieving and manipulating data and only 20% of their time using and analyzing the information for decision making.

The table below highlights the current state and desired state from an Analytics standpoint for most of the organizations.

Current State

Time SpentAcquiring Information

Desired State

Time SpentAnalysis and Making Decisions

Extrapolate or guess at missing data Consistent information across organization

Extraction processes are inconsistent and require incremental changes

Extraction is planned, organized, structured and primarily automated

Resulting numbers don’t “tie”Ability to view data from many angles - slice and dice, consolidated system and drill down to location, etc…

Filtering of data done manually at many levels Drill down to pinpoint problems/opportunities

Data integration is time consuming and leaves limited time for analysis

Efficiently arrive at sound business decisions based on facts validated through analysis

Businesses should invest in Business Analytics applications for the following reasons: High visibility Competitive advantage Better and faster decisions Industry leaders rely on analysis of data High rate of return Operational efficiency

High VisibilityBusiness Analytics applications provide a very high level of visibility of business performance. A company could visualize its strengths, weaknesses, opportunities and threats far better. Good business analytics solutions provide visibility at operational level. For example, the senior management could look at its operating margins based on finance, drill it down to operational drivers which drive the expense and further drill down to factors which contribute to that expense drivers.

Competitive AdvantageBusiness Analytics solutions convert the data to information, knowledge and intelligence. Companies that heavily invest on Business Analytics solutions will have the correct information through the correct people and at the correct time. Cutting edge knowledge over their peers in the industry provides a competitive advantage as well.

Better and Faster Decisions

Companies that have heavily invested in Business Analytics base their decision on data. Data driven decisions are rational, better and faster. Data driven operational decisions could be easily automated. Since the executives will be free from the operational decision making, they can spend their valuable time more on analysis and jobs which add value to the organization. Better decision making throughout the organization provides the ability to respond to changing market trends.

Top Performers go for Business Analytics

A recent survey conducted by IBM and MIT Sloan School of Management Review across 3000 executives spread over 100 countries and 30 industries found that top performers in the industry use the Business Analytics five times more than their peers in the industry. Top performers agree on the fact that business information and analytics makes a huge difference on their decisions and more and more decisions are driven by data. (3)

Operational Efficiency

Business Analytics also results in operational efficiencies in the organization leading them to be more nimble at information management and deliver the following results:

Delivers a scalable reporting platform where business users are able to run canned and ad hoc reports. Eliminates the cost of custom programming for each report; eliminates business user dependency on

programmers for data access. It also delivers the following benefits:

o Aggregated data not available in transactional systemso Trend analysis — by calendar year, quarter, month, and day of weeko What-if scenario analysiso Report schedulingo Various output and display formats , such as Excel, PDF and graphs/chartso Different manifestations via mobile, tablet and desktop.

Return on InvestmentNucleus Research stated in their survey that organizations driven by analytics garner high return on investment when compared to their peers. Higher return on investment was achieved across industries as well as government sector. According to Nucleus Research, every dollar spend on analytics, gives a return of 10.66 dollars. (4)

Role of Analytics in business The core principle of business remains the same irrespective of the industry. Every business works on the following principles and Business Analytics helps to achieve them:

Maximize its revenue Minimize its operating expense Maximize return on long term assets Manage its risk better.

Business Analytics provides business performance visibility at three levels. (1) Financial performance level

o Revenue Growth, Operating Margin (%), Operating Margin (%), Risk Exposure Index etc. Operational Drivers Level

o Revenue Dollar, Overhead Cost Index, Staff Productivity Index, Operational etc. Operational Factors level

o Pricing, Inventory Management, Investment, Risk Assessment etc.

Financial performance level visibility is at macro level and operational level visibility is at the micro level. At each level the focus will be on revenue maximization, expense reduction, maximize return on long term assets and manage risk. At each level analysis on history, current and future is very important. Business Analytics helps to do such analysis. A good Business Analytics solution provides a good visibility at all three levels and helps to make better decisions.

Figure 1: Visibility Diagram

Financial level performance provides information about financial indicators. Financial Planning and Analysis (FP&A) function manages this level visibility. The most common analysis is Budget vs. Actual, profit and loss analysis at a particular level. A mere financial visibility is just not enough. Operational drivers drive the

operational efficiency, which helps the financial performance to achieve its goals. Operational factors contribute to the operational drivers which in turn helps the business to achieve its goals.

Figure 2 provides an idea about three levels of visibility (1). Please note that Figure 2 is a common representation and not an industry specific representation. Verticals and clients could build the visibility diagram based on industry and business requirement. Specific mapping such as Operational Drivers to Factors is not done in this diagram since it is beyond the scope of this document and the diagram.

Figure 2: Different Levels of Business Analytics

Business Analytics - Industry Focus Areas

Industry Focus areas

Banking Integrated enterprise view of risk and finance and a single view of the customer

Federal Performance optimization, open government, fraud, and risk management

Insurance Customer transformation, sales force effectiveness, distribution strategy, underwriting, and claims excellence

Life Sciences Physician targeting, managed markets, safety analytics, and generic drug competition

Healthcare Healthcare reform, fraud, quality of care improvement, and compliance

State Government

Performance optimization, cost reduction, fraud management, and workforce planning

Business Analytics Reference ArchitectureBusiness Analytics Reference Architecture focusses on integration and transformation of the data, conversion of the data to information, knowledge, business intelligence and useful analytics. This document does not discuss about “Data in” (Data Write) systems. Performance Analytics (Planning) is an exception to this.

Figure 3 provides a high level overview of Business Analytics Architecture

Figure 3: Business Analytics Reference Architecture Diagram

Business Analytics architecture has the following main components: Data Layer (Read)

o Structured Data , Unstructured Data Analytics Access

o History , Current and Future Analysis Performance Analytics

o Financial Planning Application Delivery Medium

o Intranet, Email, Mobile, Internet, File Transfer etc. Security

o Object level and Data Level Security

Data Layer A good Business Analytics solution starts with the Data Layer. Most of the business rules are applied in the Data Layer.

Why is Data Layer important?

Foundation and StructureWhen building a house, we begin with the foundation followed by the structure, walls and finally the painting. Simultaneously, in a Business Analytics system, Data Layer corresponds to the foundation and the structure. Most of the major work is done in the Data Layer.

Data Layer is the repository of data integrated from various sources which could be internal as well as external. Data sourced and stored could be structured or unstructured. It holds a huge volume of historical data for doing analysis and designed for Data Read. Data layer provides the consistent results and provide the single version of Truth.

Integrate Relevant Data The source system contains huge volume of structured data. Social networks contain huge volume of unstructured data. We do not need all the data that comes to the Data Layer. Chances are that there might be duplication of the same data in multiple places and the grain of such data could be in too detail in the source systems. Such kind of data may not be required or helpful in decision making.

When data is acquired in the Data Layer, duplication of data has to be prevented and the data should be summarized to the required level and stored. This will provide a single version of the ‘Truth’. In the case of unstructured data, all the data is not relevant for business. The first level of separation of the unwanted data happens while acquiring in the Data Layer.

Build for InsightData acquired is not presented in the same form in the Information Access layer. Acquired data is cleaned, conformed, business rules are applied and transformed. Data models are built in the Data Layer to suite the requirements of the business function. The conversion of Data to Knowledge happens in the Data Layer. Data is kept in a highly suitable form (de- normalized) for analysis.

Critical for SuccessIn some cases the Source System may not have the required data for the information. Data has to be derived out of the existing data. For example, a company that wishes to look at its profit and loss by product line level, capture its revenue in the Source System at the product level and captures the expenses in the source system at a higher level than the product level. To derive the expenses at product line an accounting allocation has to be done. Performance analytics application cannot handle the huge volume of data. The allocation process is done in the Data Layer using allocation business logic.

Another company has built its Data Layer for each functional area like data silos. In this case, cross functional analysis cannot be done easily here.

The types of Data Gap mentioned above have to be carefully addressed while designing the Data Layer. A good Data Layer is very important for the success of analytics. Any gap in the Data Layer cannot be filled in the Analytics Access Layer. So business may not be able to do the critical analysis required for success.

Data Layer SystemsData Read systems can be categorized into two major systems based on the structure of the data.

• Structured Data (Data warehouse \ Data Mart)• Unstructured Data (Social Media)

Structured Data

Data Warehouse\ Data Mart

Data Warehouses\ Data Mart source most of the data from internal systems and provide insight on strengths and weakness of a business. It contains data for historical and current analysis. Data Warehouse contains huge volumes of historical data and is capable of tracking the history with the context at that point of time. In a Data Warehouse, data from various source systems are integrated, cleaned, transformed and kept in a structured form, which is easy to read. Typically a Data Warehouse \ Data Mart contains structured and semi structured data.

Unstructured DataLots of business relevant information originates in the unstructured format such as text, email, call logs, web logs, social media feeds, blogs, tablets, sensors and mobile data. Businesses have recently discovered that there are valuable insights that can be unlocked from these data sets to accurately drive business decisions. Unstructured data could lead to the following trials: The timeliness of delivery: Is this information delivered immediately to the right business group for

visibility? The action ability of insights: Is the information sufficient to enable a business decision for a better

outcome?

Analytics Access LayerUsers access the data through the Analytics Access Layer. Even though some business logic is applied in the Data Layer, majority of the business rule is applied in the Analytics Access Layer. Data conversion of information, knowledge, business intelligence and useful analytics happens in this layer.

Figure 4 provides an overview of Analytics Access Layers.

Figure 4: Analytics Access Diagram

Historical Analysis Historical Analysis answers things that happened in the past. It is similar to the rear view mirror in a car. Historical Analysis normally answers the what, when and where of a query. Canned reports and ad hoc analysis is used for Historical Analysis.

Canned ReportsIT builds the Canned Reports based on the requirement of the user. Typically, canned reports are list or cross tab reports build with prompts and provides low level information. Most of the canned reports are very detail in nature.

Ad hoc Analysis Ad hoc analysis provides the end user ability to slice and dice the data. They can drill down the data to a detailed level. Ad hoc capability provides better ability to analyze the business. IT builds a user friendly data model for Ad hoc Analysis. It benefits both the users as well as IT. Users can get more analytical capability and the burden on IT comes down since they do not have to build the canned reports and maintain them.

Current Analysis Current Analysis projects the current status. It is similar to the car windshield while driving. Key Performance Indicator (KPI) dashboards, business score cards are used to get the current analysis.

Business Score CardsScore cards are strategic tool for the top management to monitor business performance. Business works to achieve a particular target or goal. Business score card monitors the performance of the business. In a business there is always the comparison of the current performance against the predefined target. Scorecards are the best tools, which is strategic in nature and focuses on indicators and trends.

Key Performance Indicator (KPI) Dash BoardsKPI dashboards help to monitor business performance in key areas. It monitors a specific functional area and measures the current value against the expected value. A KPI dashboard is tactical in nature and focusses more on Measure Values.

Future Analysis (Advanced Analytics) Future Analysis (Advanced Analytics) uses statistical and complex mathematical techniques to see a pattern or trend to predict the future. The functionality is similar to the GPS used while driving the car to understand the traffic ahead. Advanced Analytics uses the predictive model to derive knowledge out of data. Secondly, it uses the derived knowledge for further action or decision making (6).

Analytics uses structured data from Data Warehouses as well as unstructured data. Banks generally use it for fraud detections, and City Planning uses it for traffic pattern analysis. Retail companies use it to understand consumer buying pattern, and Energy companies use it gauge the energy utilization pattern.

Future Analytics focusses on the following areas: Why it happened? What will happen in future if the current trend continues? What will be the future outcome?

How can we change the outcome?

The following are the most commonly used analytics:

Predictive AnalyticsBased on historical known trends and current transaction data, it predicts the trends in future. For example, a weak U.S Baseball Team “Oakland A” hired undervalued players purely based on their past performance data. Predictive analytics predicted statistically that they will do very well and will return good value for their money. Oakland A finished that year league in second place, with very less spending when compared to other players who were more costly. The story of the movie Money Ball is based on Predictive Analytics. (5)

Data MiningData Mining focusses on exploring unknown trends from huge volume of data. For example, while using our credit or debit card in a new city, the Bank sends us an alert to confirm the transaction. Through Data Mining, Banks get to know where we spend our money generally. As soon as they detect that our card is being used in a different place, it triggers an alert to detect any fraud.

Simulation Simulation replicates a business problem, process, and system. Based on what if analysis, we can predict the future behaviour or understand potential bottlenecks. For example, Business Simulation software could be used to analyse a particular strategy, or factors that affect business, and predict the final outcome. Business school students play a similar business game as a part of curriculum.

Segmentation AnalyticsSegmentation Analytics focusses on consumer needs to determine how the market could be divided into different segments. Segmentation could be based on geography, demographics, or behavioural. For example, the retailers in the neighbouring areas of Hispanic store specific brands of beers in their grocery stores.. This is to address a specific market segment based on demographics.

Risk AnalyticsRisk Analytics focusses on risk identification, assessment, mitigation and monitoring. It is heavily done in financial industry. For example, Banks do the credit risk analysis for a loan based on the client’s credit history, ability to pay the loan, current economic condition and collateral.

Performance Analytics Applications (Current and Future)Performance Analytics Applications monitor the business performance against pre-defined goals. Business performance management, corporate performance management, enterprise performance management are synonym to Performance Analytics.

Performance Management involves three main activities: Set up goals Collect important measures and monitor against set goals Take action to improve the future outcome

Performance Analytics Application is used in financials, sales, project management and Human Resources. But it is heavily used in Financial Planning and Analysis (FP&A) , which is the first level of visibility to top management. Normally FP&A monitor the performance based on Profit & Loss (P&L) and Budget or Forecast (Future). As the period progresses, management will monitor the actual P&L measures (Current) against

Budget or Forecast P&L. Depending on the variance, management will take action to increase revenue or decrease the expense.

Performance Analytics Application is a write-back application. Users can write-back data in these applications. A performance application stores the data in files or in database, which is later loaded into the Data ware house and the subsequent reports accessed through the analytics access layer..

Delivery MediumInformation is delivered to users through one of the following mediums:

Integrated Corporate Portal / IntranetAnalytics access is done through a web portal. This portal can be a common corporate portal like the SharePoint or application specific portal like SAP or Cognos. Business users can access the portal while they are within the corporate network.

Email Reports, dashboards, score cards can be delivered via corporate email. But most of the corporate emails have limited size to accommodate heavy file attachments, therefore reports with less file size only can be delivered via email. If the report is available in a portal, users can access such report by emailing the path to the portal.

Mobile Analytics access could be done through handheld mobile devices such as iPad, iPhone, and Black Berry etc. Users can access these reports offline as well as read the interactive version on their mobile devices. Normally the content delivered in mobile will have less data volume because of the limitations in the hardware. Dashboards are normally delivered through the mobile platform.

File SystemThe output of a report could be saved as Excel, PDF, CSV, or Text Format in a file system, which is shared by group of users. Users can access the files through a web portal also. Normally reports delivered in a file system are consumed by another application as data source.InternetWe can access the Analytics access through the internet. Some application in Analytics Access Layer can be used by users who are outside the company network, such as Bank or Insurance clients, vendors, suppliers etc. They can access information of their need through the internet or in a de-militarized zone outside the corporate network.

Security Information security means securing the sensitive information. Sensitive information is sensitive data like Social Security Number or a specific report. If personal information like social security or credit card is misused in wrong hands it can cause a huge problem to the consumer in the form of identity theft/ fraud. Consumers can file legal lawsuits against the company which is supposed to safeguard such sensitive data of its customers. Information security is applied at two levels:

Authentication to use the Business Analytics Authorization to use specific object or data within Business Analytics.

Authentication Authentication is a mechanism through which the user identity is conformed to access the business analytics application. For example, a company may have thousands of employees but all the employees are not

authenticated to access the business analytics application. Only a group of users are authenticated to access the application.

Authorization Authorization is next level of security. Authorization follows authentication. Authorization gives the right to the user to access specific object or data within the object.

Authorization is applied at two levels: Object level security secures the objects in the Data Layer or Analytics Access Layer. Data Level Security secures the data in Data Later or Analytics Access Layer.

Object level SecurityObject could be a database table, view, report, folder, dashboard, metrics, analytics model. Object level security is applied in the database or in BI & Analytics applications based on the requirement. For example, a user from the finance team will be allowed to use objects (reports, dashboards, database tables) which are specific and required for his work. He cannot access objects pertaining to marketing or human resources.

Data Level Security“All the sales Professionals should be able to run the sales report. But they must see only their sales area data”. The above business scenario requires a data level security. Data level security could be applied in database layer or in the BI application Layer. Based on the user login credentials, the users can view data limited to their area. Data level security could be at the row or column level. Note: Server or network security is beyond the scope of this document. High Level Analytic Capabilities – Industry AgnosticThe table below represents the modes of unstructured and structured data, its analytic capabilities and impacts.Data Analytics Capability Value

Email Text AnalyticsSentiment Analytics

Customer experience management

Call logsWeb logsSystem logs

Sentiment AnalyticsCustomer Interaction Analytics

Customer experience managementEffective operations management

Social media feedsBlog feeds

Sentiment AnalyticsMarketing AnalyticsText Analytics

Monitor the marketing, brand and service effectiveness

Device dataSensor data

Usage AnalyticsPropensity Analytics

Better consumer tracking and offer management

CRM MarketingSales ForceCustomer ServicePricingChurn

Better consumer tracking and offer management

Human Resources Workforce Analytics Effective management of your human talent.Finance Regulatory reporting Manage assets, revenues, risks and liabilities for a stable

Asset ValuationRevenue recognitionRisk AnalyticsAsset analytics

financial performance.

Supply Chain Inventory analyticsPlanning & ForecastingSourcing & ProcurementLogistics & Distribution

Effect channel management and delivery of product distribution to end consumer.

Product Product MarginProduct distribution

Effective productive management for better profitability and revenue

How do we Implement Business Analytics Solutions?The following steps are involved in the implementation of Business Analytics:

Analyse the Current PositionFigure 5 maps the current Business Analytics visibility and maturity.

Figure 5: Business Analytics Position Diagram

Visualize the Future PositionFuture position is basically envisioning your Business Analytics. Please mark the positions also in Figure 5.

Do the Gap AnalysisAnalyze the Gap between the current and future positioning. Gap Analysis should focus on information gap as well as technical gap.

Build the Road MapThis phase will address the current gap and guide us to the future.

Implement the Road MapPrioritize your requirements and implement the road map.

Re-evaluate the ImplementationAfter a period of time, evaluate the implementation, evaluate the social and technological changes that affect your business model and if required re-align your road map. Re-evaluation is a continuous process.

Why Cognizant?Cognizant is a business technology solution company. The following competencies have given us an edge over our competitors:

Business Driven A client manger will be placed onsite to ensure seamless alignment with your business process. Senior delivery manager will help the client manager from near shore or offshore. We have a Two-in-One box client engagement model. Our focus for our client is to increase their revenue, reduce their operating cost, maximize the return on long term assets and help them manage their risk better.

Vertical CompetenceWe specialize in twelve major industry sectors and can understand the business process chain in these industries and business requirements at enterprise, functional and sub-functional level. Many of our senior people originate from the businesses we serve, so we are able to offer deep experience across almost all major industries.

Skilled Workforce We have trained and certified consultants in each of the technical area of Business Analytics. Our architects and consultants are spread across the world. We are born global and will help you to design, implement a Business Analytics solution based on business function and technical requirements.

Delivery CompetenceSo far we have delivered 4900 projects for 700 blue chip companies.

o Our Business Analytics solutions leverage Cognizant’s proprietary PLATINUM information management solution. PLATINUM can deliver tangible performance improvements and automate processes at every stage of your solution: conception, design, development and deployment.

o Our internal Cognizant 2.0 system unites our entire workforce, business partners and clients. This enables us to share our knowledge and expertise in real time and able to produce better results.

StandardsOur culture is values based, you are assured of the highest ethical standards of integrity, transparency and corporate governance.

Success storiesWe have lot of success stories in Business Analytics space. The following list will speak of our work.

Industry Category Solution Delivered Value to ClientLife Sciences Profitable Promotion Mix Increase ProfitPharmaceutical giant Better Value from Marketing

InvestmentsIncrease Revenue and Market Share

Insurance Sales Effectiveness Increase RevenueInformation, Media and Entertainment

Customer Centric Focus Increase customer satisfaction and Revenue

Oil and Gas Control its Drilling and Production in Real Time

Manage Operation Better and effective cost control

Retail Increase Revenue without additional Promotional spending

Increase Revenue and Profit

Reference:1. The performance Manager, Proven Strategies for turning information into Higher Business Performance by Roland Mossiman, Patrick Mosimann and Mug Dussalt.2. Identifying the Path to Profitability by EVAN STUBBS3.http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03371usen/GBE03371USEN.PDF4.Nucleus Research. Research Note: Analytics Pays Back $10.66 for EveryDollar Spent. Document L122, November 2011, page 1.5.http://practicalanalytics.wordpress.com/predictive-analytics-101/6.http://en.wikipedia.org/wiki/Analytics