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 strategy

1. Five Attributes to a Successful Big Data Strategy
Bill Busch
SSA | Enterprise Information Solutions CWP
Twitter: @agilebibill

2. 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

3. • 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

4. BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Management
Enterprise Resource Planning
Experience Design (XD)
Management Consulting
TECHNOLOGY SOLUTIONS
Business Integration/SOA
Cloud Services
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Our Solutions Expertise

5. Bill Busch
SSA | 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

6. 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

7. 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

8. 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
Situation
Role 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

9. 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

10. 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

11. 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

12. 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

13. 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.
Results
Developed 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

14. 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 Zone
Processed Data
(Future)
Refinery Jobs
Data Publishing
Message /HL7
Store
HL7
Scraping
Analytics and
Data Discovery
Data
Warehouse
Data Lake

15. 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

16. 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

17. Conclusion
• Big Data is a significant
investment
• A comprehensive plan
will go a long way to
assuring success

18. As a reminder, please submit your
questions in the chat box.
We will get to as many as possible.

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