Many big data projects fail, learn why and how to prevent that from happening to your project.

1. Creating Big Data Success with the
Collaboration of Business and IT Teams
By Edward Chenard

2. 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
edward@echenard.com
Twitter: Echenard
Slideshare: Echenard

3. Everyone is Jumping on to Big Data

4. 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

5. Big Data at Most Companies
IT
Business

6. 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.

7. No One Team can Handle Big Data
Alone

8. Current state of big data collaboration
Business
(Strategy)
Analyst
IT
(Insights)
(Systems)

9. 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

10. Everything is
about discovery

11. 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.

12. Everyone
Discovers

13. Data Discoverers
“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

14. 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

15. How We Need to Look at Discovery
Horizon
Future
Soon
Present
Past

16. Discovery is the leading
emerging interaction category
of the Age of Insight

17. Complex ecosystems:
multi-channel experiences
everyware
environments
Service models
Reactive data
dynamic perspectives

18. Activity Centered Thinking

19. How Different Functions See the Same
Issue
“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

20. 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)

21. 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.

22. 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‟

23. Modes are the verbs of
discovery scenarios.

24. 9 distinct modes
Locate
Verify
Monitor
Compare
Comprehend
Explore
Analyze
Evaluate
Synthesize

25. Where to Start

26. The Business Value
Framework
Initiatives
Customer
Acceptance
Business value
Customer
Acceptance
Focus on
Customers
Perceived Value
Business value
Focus on
Internals
Ease of Data Collection
Prerecorded
Customer
Needs
Timeliness
Ease of Implementation
Ease of Data
Collection
Value Perceived
Customer’s Wallet Share
Ease of Implementation
Production
Flexibility
Different
Products
Automated and
prompted
Customer’s
Wallet Share
More Products
Production
Efficiency

27. How work gets structured
Vision &
Goals
Clearly articulated vision for
personalization and
recommendations, precisely defined
goals with how to measure. Defined
scope of the product.
Governance
Execution
Market strategy, customer
segmentation, prioritization, org
focus, measurement and incentive
systems
Production
process, flexibility at
scale, efficiency, relationship
management, benchmarking
, metrics, initiatives

28. Framing Collaboration
Value (Shared): Show me the money!?!
-
Measurable Results
Multi-Channel Case Studies
Strategy: Where are you
headed?
IT: What Tools and Why
-
MapReduce, Hadoop
Cassandra, The Cloud
Pig, Hive,
HDFS
Big Data
Collaboration
-
Buy vs. Build
Open source options
Alignment with Analytical Infrastructure
Speed to Market
Privacy Considerations
Analyst: Who, How, Where?
-
Data Scientist vs. Statistician
Where to find talent?
Retain, Train
Offshore vs. Onshore
University involvement
28

29. Always Remember:
Data, Insights, Actions
Listen
Share
Engage
Innovate
Perform
• Listen to the data streams
• Share the data with the rest of the organization
• Engage to the data to find the insights
• Innovate new ideas from the insights gained from the data
• Perform insightful actions from the data to create better customer
experiences

30. Collaboration
helps to achieve
where others
fail.

31. Thank You!
• Edward Chenard
– Twitter: Echenard
– Email: edward@echenard.com
– Blog: CrossChannelPrairie.com

32. Resources
• Why Do Big IT Projects Fail So Often?
(http://www.informationweek.com/strategic-cio/executive-insights-andinnovation/why-do-big-it-projects-fail-so-often/d/d-id/1112087?)
• A statisticians view of big data (http://www.slideshare.net/kuonen/astatisticians-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-driveorganization)
• The Language of Discover (http://www.slideshare.net/moJoe/designingbig-data-interactions-using-the-laguage-ofdiscovery?utm_source=slideshow&utm_medium=ssemail&utm_campaig
n=download_notification)
• Images by Emma Kim, Jason Maehl, David Spurdens, Evgenia Shadrina
and Hill Street Studio