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November 2013

BIG ANALYTICS
THE GOOD & THE VALUE
About Think Big Analytics

¨ 

Formed in 2010 to help clients launch and scale-out Big Data solutions

¨ 

Services incl...
Think Big Integrated Value

Integrated Value
Advisory
¨ 
¨ 

¨ 

¨ 

Understand true
business needs
Evaluate suitabili...
Big Analytics

Ÿ 
Ÿ 
Ÿ 
Ÿ 

New data
Yielding new opportunities
Enabled by new approaches
With supporting organization...
New Data

CONFIDENTIAL

|

5
Nontraditional Formats

Ÿ  Unstructured data != text
- Call logs
- Raw video
- Satellite photos

CONFIDENTIAL

|

6
Exhaust

Ÿ  Byproduct data
Ÿ  Driving interest in the Internet
of Things

Ÿ  Our machines tell a story about
us

CONFID...
Data about Data

Ÿ  Data usage patterns
Ÿ  Driving next generation
organizations
- Data access patterns as KPI
- Systems...
New Opportunity

CONFIDENTIAL

|

9
Fingerprinting

Ÿ  Unintentional patterns define us
- ATM rhythm
- Botnet synchronization

Ÿ  More connected world expos...
Dark Data Insights

Ÿ 
Ÿ 
Ÿ 
Ÿ 
Ÿ 

It’s back from the dead!
Audit data
Fund manager predictions
Employee logs
Archit...
New Approaches

CONFIDENTIAL

|

12
Unstructured Analysis

Ÿ  Non-traditional structures
- Path models
- High dimensionality

Ÿ  Text
- POS
- Classification...
Deep Learning

Ÿ  MapReduce built for
- Bootstrapped models
- Partitioning data by complex
logic

Ÿ  Backpropagation is ...
Challenges Incorporating Data
Science

CONFIDENTIAL

|

15
Organizational Integration

Ÿ  Traditionally under engineering
Ÿ  Integrated with data creators,
not data consumers

Ÿ ...
Success Loops

Ÿ  We take BI for granted
- Analysts find novel patterns
- Business sees new trends
- Statistics is balanc...
Successful Incorporation of Data
Science

CONFIDENTIAL

|

18
Partnership

Ÿ  Business is a partner, not a
customer

Ÿ  New insights, capabilities, and
products are not born in a
vac...
Cross Functional Teams

Ÿ  Data science is a process, not
a job role

- Engineering
- Research
- Statistics
- Business
- ...
Measurement

Ÿ  Requires KPI/KRI
Ÿ  Performance metrics
- Direct actions
- Create purpose

CONFIDENTIAL

|

21
Client Success

CONFIDENTIAL

|

22
Example Client Phase 1

Ÿ  First phase: Big Analytics
execution

Ÿ  New methods of Botnet
detection

Ÿ  Led to patent

...
Example Client Phase 2

Ÿ  Further analysis
- Improvement of Botnet models

Ÿ  Expansion of cross functional
Big Analyti...
Example Client Phase 3

Ÿ  Cross-Functional Analytic
Organization

Ÿ 
Ÿ 
Ÿ 
Ÿ 

Governance
Ownership and accountabili...
Questions?
www.thinkbiganalytics.com
www.linkedin.com/in/danmallinger
@danmallinger

CONFIDENTIAL

|

26
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Big Analytics: Building Lasting Value

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From the Predictive Analytics Innovation Summit
Video here: https://www.youtube.com/watch?v=PdKUt0zK0UY

With the avalanche of data about operations, customers, and products, leading companies are utilizing Big Analytics to better understand historical patterns and predict what may come next to create sustained competitive advantage. Dan Mallinger, who leads Think Big Analytic's data science team, will focus on practical examples of where companies are implementing new analytics approaches over big data. Dan will discuss how these efforts differ from traditional analytic approaches, the organizational and business impact, and how our clients are creating new value in areas such as marketing, services, sales and product development.

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Transcript of "Big Analytics: Building Lasting Value"

  1. 1. November 2013 BIG ANALYTICS THE GOOD & THE VALUE
  2. 2. About Think Big Analytics ¨  Formed in 2010 to help clients launch and scale-out Big Data solutions ¨  Services include Big Data strategy, training, engineering and data science ¨  ¨  Management Background: Quantcast, Cambridge Technology, Oracle, Sun Microsystems, Accenture Blue chip clients, including: Ø  Internet Transactions Security Global #1 Ø  Retail 2 of Global Top 5 Ø  Banking 4 of Global Top 1; Financial Services 2 of Global Top 5 Ø  Asset Management Global #1 Ø  Disk Manufacturing Global #1 Ø  Social Networking Global #1 CONFIDENTIAL | 2 2
  3. 3. Think Big Integrated Value Integrated Value Advisory ¨  ¨  ¨  ¨  Understand true business needs Evaluate suitability of new technologies ¨  Provide perspective on market ideas ¨  Ensure engineering and analytics support business goals Help establish realistic and attainable objectives Drive client-specific innovation Implement ¨  ¨  ¨  Understand technology preferences and limitations Assess talent skills and development needs Develop deep knowledge of the data and tools CONFIDENTIAL | 3
  4. 4. Big Analytics Ÿ  Ÿ  Ÿ  Ÿ  New data Yielding new opportunities Enabled by new approaches With supporting organization CONFIDENTIAL | 4
  5. 5. New Data CONFIDENTIAL | 5
  6. 6. Nontraditional Formats Ÿ  Unstructured data != text - Call logs - Raw video - Satellite photos CONFIDENTIAL | 6
  7. 7. Exhaust Ÿ  Byproduct data Ÿ  Driving interest in the Internet of Things Ÿ  Our machines tell a story about us CONFIDENTIAL | 7
  8. 8. Data about Data Ÿ  Data usage patterns Ÿ  Driving next generation organizations - Data access patterns as KPI - Systems access as employee engagement CONFIDENTIAL | 8
  9. 9. New Opportunity CONFIDENTIAL | 9
  10. 10. Fingerprinting Ÿ  Unintentional patterns define us - ATM rhythm - Botnet synchronization Ÿ  More connected world exposes more fingerprints - Mobile installs and settings + NFC - Sensory data at shopping mall displays CONFIDENTIAL | 10
  11. 11. Dark Data Insights Ÿ  Ÿ  Ÿ  Ÿ  Ÿ  It’s back from the dead! Audit data Fund manager predictions Employee logs Architectural records CONFIDENTIAL | 11
  12. 12. New Approaches CONFIDENTIAL | 12
  13. 13. Unstructured Analysis Ÿ  Non-traditional structures - Path models - High dimensionality Ÿ  Text - POS - Classification Ÿ  Images - Object recognition - Time differentials CONFIDENTIAL | 13
  14. 14. Deep Learning Ÿ  MapReduce built for - Bootstrapped models - Partitioning data by complex logic Ÿ  Backpropagation is hard Ÿ  Feature learning isn’t (always) CONFIDENTIAL | 14
  15. 15. Challenges Incorporating Data Science CONFIDENTIAL | 15
  16. 16. Organizational Integration Ÿ  Traditionally under engineering Ÿ  Integrated with data creators, not data consumers Ÿ  Disconnected from business priorities CONFIDENTIAL | 16
  17. 17. Success Loops Ÿ  We take BI for granted - Analysts find novel patterns - Business sees new trends - Statistics is balanced by domain knowledge - Integration of actors aware of feasibility, cost, and impact Ÿ  Where does your data scientist sit? CONFIDENTIAL | 17
  18. 18. Successful Incorporation of Data Science CONFIDENTIAL | 18
  19. 19. Partnership Ÿ  Business is a partner, not a customer Ÿ  New insights, capabilities, and products are not born in a vacuum CONFIDENTIAL | 19
  20. 20. Cross Functional Teams Ÿ  Data science is a process, not a job role - Engineering - Research - Statistics - Business - Salesmanship Ÿ  Successful Big Analytics blends skills, perspectives, and pushes boundaries CONFIDENTIAL | 20
  21. 21. Measurement Ÿ  Requires KPI/KRI Ÿ  Performance metrics - Direct actions - Create purpose CONFIDENTIAL | 21
  22. 22. Client Success CONFIDENTIAL | 22
  23. 23. Example Client Phase 1 Ÿ  First phase: Big Analytics execution Ÿ  New methods of Botnet detection Ÿ  Led to patent CONFIDENTIAL | 23
  24. 24. Example Client Phase 2 Ÿ  Further analysis - Improvement of Botnet models Ÿ  Expansion of cross functional Big Analytic team - Tool selection - Training - Early win identification - Self-selected group CONFIDENTIAL | 24
  25. 25. Example Client Phase 3 Ÿ  Cross-Functional Analytic Organization Ÿ  Ÿ  Ÿ  Ÿ  Governance Ownership and accountability Process Roadmap CONFIDENTIAL | 25
  26. 26. Questions? www.thinkbiganalytics.com www.linkedin.com/in/danmallinger @danmallinger CONFIDENTIAL | 26
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