five attributes to a successful big data strategy
DESCRIPTION
The veracity, variety and sheer volume of data is increasing exponentially. With Hadoop and NoSQL solutions becoming commonplace, there are many technical options for managing and extracting value from this data. Many companies create labs to experiment with Big Data solutions, only later become IT playgrounds or unstructured dumping grounds. To help avoid these pitfalls,companies with successful Big Data projects approach challenges by formulating a strategy that assures real business value is derived from their Big Data investments. In a Perficient poll, 73% of companies stated they are in the early-evaluation stage to find solutions to their Big Data problems and are only beginning to create their strategy. Join us for a webinar featuring thought-provoking best practices used by successful companies to quickly realize business value from their Big Data investments. You'll learn: The top five steps to increased business value What the top companies are doing in Big Data that you need to know Next steps to lay the ground work for a successful Big Data strategyTRANSCRIPT
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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