Five Attributes to a Successful Big Data Strategy

Bill BuschSSA | Enterprise Information Solutions CWP

Twitter: @agilebibill

Perficient is a leading information technology consulting firm serving clients throughout

North America.

We help clients implement business-driven technology solutions that integrate business

processes, improve worker productivity, increase customer loyalty and create a more agile

enterprise to better respond to new business opportunities.

About Perficient

• Founded in 1997

• Public, NASDAQ: PRFT

• 2013 revenue $373 million

• Major market locations throughout North America• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus,

Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis,Los Angeles, Minneapolis, New Orleans, New York City, Northern California, Philadelphia, Southern California,St. Louis, Toronto and Washington, D.C.

• Global delivery centers in China, Europe and India

• >2,100 colleagues

• Dedicated solution practices

• ~85% repeat business rate

• Alliance partnerships with major technology vendors

• Multiple vendor/industry technology and growth awards

Perficient Profile

BUSINESS SOLUTIONSBusiness IntelligenceBusiness Process ManagementCustomer Experience and CRMEnterprise Performance ManagementEnterprise Resource PlanningExperience Design (XD)Management Consulting

TECHNOLOGY SOLUTIONSBusiness Integration/SOACloud ServicesCommerceContent ManagementCustom Application DevelopmentEducationInformation ManagementMobile PlatformsPlatform IntegrationPortal & Social

Our Solutions Expertise

Bill BuschSSA | Enterprise Information Solutions CWP

• Bill leads Perficient's enterprise data practice and specializes in business-enabling BI solutions.

• Responsibilities: • Executive data strategy• Roadmap development• Delivery of high-impact solutions that enable organizations to leverage enterprise

data• Bill has spent the last 15 years in executive leadership roles in business intelligence, data

warehousing, information/data architecture and analytics. His most recent achievement is as visionary and leader of Perficient’s Big Data Lab, an environment that enables Perficient to conduct state-of-the art Big Data research and development.

Speaker

Agenda

• Challenges with Big Data• Big Data Strategy• 5 Attributes of a Big Data Strategy

– Business Case– Architecture– Skill Development– Governance– Big Data POC

• Questions and Answers

69%Higher revenue per

employee

20% Companies realize cost

savings from tool rationalization

Why Approach Big Data Strategically?

A Strategic Approach Will:

• Align the company stakeholders

• Communicate value creation

• Get IT to stop playing and start creating business value with Big Data technologies

• Establish a complete people,process, and technology aligned plan

• Prioritize business cases to those that attainable and create real business value

• Drive changes to delivery and governance that typically limit Big Data value

• Define Big Data’s role within an enterprise data architecture

BUT…….BUT…….

95% Failure rate of Big Data projects

77%High performing companies will

strategically leverage analytics vs. only 33%

of low performing companies

Big Data Business Cases

• Business Focused Benefits

– Optimization– Prediction

• IT Business Case– Benefits

• Cost savings /avoidance• Additional capability

– Analytics and Data Discovery– Data Warehouse Augmentation– Data Hub/Data Lake

• Consider using a layered business case

• Do not use a business case that can easily solved with an existing DW

Case Study

SituationRole of big data was not defined within the organization. Financial transaction processing company chose a parameterized reporting that was solved using traditional EDW at minimal cost

Results

Role of big data was not defined within the organization was delayed because the business case

Lessons Learned

• Choose a use case that cant be easily solved with a traditional system

• Established industry use cases are easiest to support

• Do not put all your Big Data eggs in one business case

Business Case: Plan For Benefits Analysis

• Benefits analysis is a process by which business benefits are quantified (usually in $)

• Upfront ROI on big data cases is difficult to specify

• Benefits analysis can be the key to continued funding

• Specify a process and responsibility for Benefits Analysis in your strategy

Setting Expectations

Case Study

Situation

Google analyzed over 500 million web searches a day and correlated this to disease data for flu.

Results

Google’s overestimated the number of flu occurrences for the between 2011-2013 by a factor of nearly two.

Lessons Learned

• Predictive modeling is applied science and is difficult

• Many times, you will need more data• Understand changes in source data

• Cost savings tend to come from larger implementations

• Business cases built on analytics must realize the scientific research component• Studies build on each other• Understanding why a model has

failed can have value• Test & learn cultures lend

themselves to big data analytics

• Providing a capability that is leveraged by people

• Focus the organization on delivering a tool/capability vs a business process delivering ROI

Skill Development

“It's all to do with the training: you can do a lot if you're properly trained.”

Queen Elizabeth II

• Strategy should realistically access the skills of the organization to leverage the Big Data environment

• More than tool based training – do you have the data scientists and statisticians in-house

• Consider establishing analytical user-groups to drive organizational learning

• Plan to develop IT’s delivery and support skills

– Includes training on new delivery processes

Architecture

“The mother art is architecture. Without an architecture of our own we have no soul of our own civilization.”

Frank Lloyd Wright

Specify the complete architecture

Ingestion/Extraction/Job Control

Data Storage Areas

Refinery & Data Preparation

Security

Metadata

Analytical, Data Discovery, BI, Model Execution Tools

HW Platform (Best of Breed vs. Appliance)

Hadoop Distribution /Targeted Release

Architecture Data Ingestion

Case Study

Situation

Large financial services company wanted to time to detect fraud. It was taking weeks and sometimes months to source new data.

ResultsDeveloped a custom, metadata driven solution that allowed new data feeds to be added by just modifying metadata. This reduced time to deliver data feeds to less than a week.

Lessons Learned

• The light transformation requirements of Big Data ELT allow for metadata configured ELT.

• Significant opportunity to reduce costs & quickly create business value.

Perficient has seen a pattern of companies not addressing:

– Hand-coding point to point data integrations of Sqoop, Flume, Pig, Map Reduce, Java, etc. is repeating the sins of the past

– Metadata configured ingestion is not that expensive and quick to develop

– Comprehensive view of data integration

• CDC of source systems• Transformations to standardize data

format• Supportability of the final system• Integration with current batch

– Do not forget network infrastructure

Architecture Data Storage Options

Plan for the Big Data environment to consist of many different data storage areas

Analytics ExtractsAnalytics

ExtractsAnalytics Extracts

Consolidated Data

Delta Data

Discovery and Analytics

Sandbox Analytics Writeback

Standardized Reference Data

Scrubbed Data

Receiving ZoneProcessed Data

(Future)

Refinery Jobs

Data Publishing

Message /HL7 Store

HL7 Scraping

Analytics and Data Discovery

Data Warehouse

Data Lake

Governance

• Governance must be addressed at the onset of a Big Data project

• Delivery and support processes must change to enable

• Security -- Need to know vs. need not to know

• Data governance must be exception based

• User classification (tools and data access)

• Create save swimming pool for data scientists

• Involve business!

“Those who expect to reap the blessings of freedom must, like men, undergo the fatigue of supporting it.”

Thomas Paine

POC Imperative

Case Study

Situation

A Fortune 100 company conducted a Big Data POC. The major work effort was to load over 100+ tables chosen by IT.

Results• Project ran behind when data quality

issues were not considered of timelines and resources.

• Prioritized business cases were not identified due to the pure IT focus of the project

Lessons Learned• Set up POC to drive architecture

standards & business case prioritization

• Focus scope of POC to predefined use cases

Consider a POC as a part of the strategy:

– Work through architectural details/challenges

– Provide a plan based on real-world experience

– Test BI/Data Discovery Tools

– Provide sizing information

– Business use-case validation/prioritization

Conclusion

• Big Data is a significant investment

• A comprehensive plan will go a long way to assuring success

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