Wayne Eckerson and Dr. Rado Kotorov take a journey through the behind-the-scenes characteristics of a great analytics program in this Information Builders Innovation Session presentation.

1. Succeeding with Analytics
Mastering People, Process, and Technology
Wayne W. Eckerson
Principal Consultant, Eckerson Group
Dr. Rado Kotorov
Chief Innovation Officer, Information Builders

2. © Eckerson Group LLC
What’s in a Name?
2
Data
Warehousing
(Early 1990s)
Business
Intelligence
(Early 2000s)
Performance
Management
(Mid 2000s)
Big Data/
Analytics
(2010s)

3. © Eckerson Group LLC
What’s in a Name?
3
DATA INSIGHTS ACTION
Data
Warehousing
(Early 1990s)
Business
Intelligence
(Early 2000s)
Performance
Management
(Mid 2000s)
Big Data/
Analytics
(2010s)

4. © Eckerson Group LLC
What’s in a Name?
4
DATA INSIGHTS ACTION
DATA ACQUISITION
ETL, data modeling, data
quality, data warehousing
IT ENVIRONMENT
Reports, analysis, dashboards,
predictive analytics
DATA DELIVERY
BUSINESS ENVIRONMENT
Data
Warehousing
(Early 1990s)
Business
Intelligence
(Early 2000s)
Performance
Management
(Mid 2000s)
Big Data/
Analytics
(2010s)

5. © Eckerson Group LLC
• Understand the past
– “Which campaigns provided
the best return?”
• Optimize the present
– “Which packaging provides
the best lift in sales?”
• Predict the future
– “Which customers are likely to
respond to this offer?”
Why Analytics?

6. © Eckerson Group LLC
• Right people
• Right tools
• Right architecture
• Right organization
Analytics Success Factors

7. © Eckerson Group LLC
Right People
CONSUMERS
• Data Consumer
• Data Explorer
• Data Analyst
• Data Scientist
• Statistician
PRODUCERS
• Report Analyst
• Data Engineer
• Business Developer
• App Developer

8. © Eckerson Group LLC
TOOLS
Data
Consumer
Data
Explorer
Report
Analyst
Data
Analyst
Data
Scientist
Data
Enginee
r
KPI dashboards
Ad hoc reports
Report authoring
Ad hoc discovery
Predictive modeling
Data development
Right Tools
• KPI dashboards = Interactive KPI reporting; locked down data
• Ad hoc reports = Modifiable reports that expose data model to data explorers or search BI
• Report authoring = Create reports from scratch from semantic layer
• Visual discovery = Connect to data sources, mash the data, and visualize results
• Predictive modeling = Data science tools
• Data development = SQL, ETL and other tools
Lines of
Support
Top
Down
Bottom
up
Casual Users Power Users
ExposeonDemand

9. © Eckerson Group LLC
Source
Data
(Relational and
NoSQL, internal,
external, devices)
On Premise, Cloud, or Hybrid
Data
Hub
(Integrated
Tables)
Move
Stream
Flatten
Link
Business
Views
(Subject-specific,
physical or
virtual models)
Landing
Areas
(Raw Tables)
Analytic
Applications
(Reports, dashboard,
embedded analytics,
custom applications)
Data Consumer (50%)
Pointandclick
Search
Data Explorer (40%)
Drag/Drop/Edit
Adhocreports
Design
Conform
Create
Refine
Data Analyst (8%)
Connect,discover,
mash,visualize
Data Scientist (2%)
Connect,prepare,
model,score
RDBMS, Hadoop, Spark, Cloud PaaS
Systems
Analysts
100/0
Data
Engineers
80/20
Business
Developers
30/70
Data Pipelining, DW Automation, Streaming
DB Views, DV tools APIs, SDKs, IDEs
KEY:
Data/Business
skills
ARCHITECTUREDEVELOPERSBUSINESSUSERSTOOLS
Analytic Platforms, App Development
App
Developers
20/80
Data Mining
Toolkit
Statistician
Top-Down: Silver ServiceBottom-up: Self Service
KEY:%=
%ofBusinessUsers
Enterprise GovernanceGrassroots Governance
Operations
Data
Catalog
Data
Prep Data
Prep

10. © Eckerson Group LLC
Right Organization
Director/VP
BI tools specialists
Data architects
ETL developers
BA Team
Business Analytics Council
Executive Committee – Business unit sponsors
Facilitated by BA Head
Working Committee – Business Unit Analyst Managers
Facilitated by BA Head and Relationship Managers
Analytics Forum – Business unit analysts and data scientists
Facilitated by BI Relationship Managers
Analytics Dir/VP
Statisticians/Data Scientists
Business Units
Analyst Managers
Business analysts
Report analysts
Source System
– Owners
- DBAs
BA Relationship
Managers
BA Liaisons
1 per unit 1 per system

11. 40+ Years of Sustained InnovationMaking Money with Analytics
Dr. Rado Kotorov
Chief Innovation Officer

12. Not Your Physical Assets; Data Is the Most Strategic Asset
Infonomics is the new management science about how to manage data assets

14. Bitcoin is data!

15. From Data Management to Data Monetization
You maximize the business value of your information investments through complete
information management and pervasive business intelligence and analytics.

16. IB Platform is Built for Data Monetization
The methodology for data monetization is aligned with our technology
Decision
Management
Applications
Value
High
Low
Raw Data Integrated and
Enriched Data
Analyzed
Information
Repurpose
Information for
Other Revenue
Generation
Use Cases
Land of
Opportunities
Analytical
Tools
IT Tools InfoApps™
(Analytic Applications)
Customer
Facing
InfoApps™
Data Assets Data Monetization
Managing The Data Value Chain

17. Where Do We See The Opportunities?

18. Opportunity 1: Operationalize Analytics
When analytics is implemented, operationalization is never being considered
 Only 22% of employees get BI
 90% of BI projects fail to
produce ROI
 The secret to success is in
empowering the 78%

19. Why are the 78% so important?
"In the study of organizations,
the employee must be the
focus of attention, for the
success of the structure will be
judged by his performance
within it".
Herbert Simon, Nobel Prize winner for studies in
decision-making in 1978
19

20. Money Are Made in Operations
20
Insights create opportunities, but only operationalization turns insights into value

21. Empower the Mechanics to Save Money
21
An InfoApp from Information
Builders available in 14 different
languages for 60,000+ users in
14,000 dealerships. Helps
employees make on the job
repair-or-replace decisions and
save Ford $60 million per year.

22. Opportunity 2: Big Data Leads to More Customers
22
People discuss only big data analytics, but what about getting more customers?

23. Empowering 260,000 Small Advertisers
23
Yellow Pages provides advertisers
information to measure the
return on their advertising dollars
and track the success of their
campaigns:
 Approximately 52 billon rows
(nine TB) of raw data per day
 Response rate 2 to 10
seconds

24. Empowering 350,000 Merchants with Spend Analytics
24

25. Opportunity 3: Empower the Consumer With Analytics
25
Why not consumerise and commoditize BI and analytics ?

26. Billions of Documents Are Distributed to Consumers
26
Every document is an opportunity to consumerise BI and analytics
20
Billion
10
Per
month
137
Pages
71%
No
HTML
10%
Provide
Linking
14%
Navigatbe
PDF

27. Static PDF Leaves the Consumer Powerless
27
All static PDFs are a missed opportunity to consumerise BI and analytics
98%
static

28. PDF (No analytics) vs ADF (For In-document analytics)
28
IB Analytical Document Format allows to put the analytics in every document
Each PDF file encapsulates:
(1) a complete fixed-layout,
(2) fonts & images
(3) text, data, and charts
(4) Requires a reader to render file
Each ADF file Encapsulates:
(1) a complete fixed-layout,
(2) fonts & images
(3) text, data, and charts
(4) Requires a reader to render file
(4) Interactive Analytic Engine
(5) Responsive design to fit any device
PDF (Portable Document Format) ADF (Analytical Document Format)

29. Distributing 2 Million 401K Statements Monthly
29
Huge savings, high adoption, increased customer satisfaction
Principal Financial Group:
A $2.80 to print, stuff, mail,
support
17% switched to eStatements
within three months

30. Thank you
30