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© Absolutdata 2014 Proprietary and Confidential
Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
www.absolutdata.com
April 30, 2014
From “I Think” to “I Know”
Institutionalizing Big Data Analytics
Anil Kaul, CEO - Absolutdata
© Absolutdata 2014 Proprietary and Confidential 2
Agenda
Speaker Intro
Absolutdata Overview
The Real Presentation
?
© Absolutdata 2014 Proprietary and Confidential 3
Anil Bio
 CEO & Co-Founder, Absolutdata
 PhD and M.S. in Marketing from Cornell University
 Experience with McKinsey and Co. deeply involved in marketing research and
marketing science
areas
 Published articles on marketing in leading journals such as McKinsey
Quarterly, Marketing Science, Journal of Marketing Research, and International
Journal of Marketing
 Invited speaker at conferences and premier business schools such as
Cornell, Columbia, Dartmouth, NYU, and Yale
 Presents at prestigious industry conferences like IIR, AMA, Esomar on analytics and
research topics
© Absolutdata 2014 Proprietary and Confidential 4
About Us
 Big Data and Analytics solutions provider
 Founded in 2001
 $20 million invested by Fidelity for expansion
 Headquartered in San Francisco and additional offices in New Delhi, London, Dubai,
and Singapore
© Absolutdata 2014 Proprietary and Confidential 5
Our Team
 Senior management from McKinsey, CPG
client, Pfizer, Mitsubishi, Nielsen, GE, HSBC, The Modellers and Citigroup
 Over 320 professionals including Data Scientists, Modelers, Statisticians, and Business
Analysts
© Absolutdata 2014 Proprietary and Confidential 6
Analytics
Data Visualization &
Reporting
Market Research Big Data
Service Offerings
© Absolutdata 2014 Proprietary and Confidential 7
Our Clients
CPG
CONSULTING
SOFTWARE
PHARMACEUTICAL
BANKING
APPAREL RETAILERS
ONLINE
HOSPITALITY
TELECOMMUNICATION
MOBILES
MEDIA & ENTERTAINMENT
OFFICE SUPPLIES
EDUCATIONAUTOMOBILECONSUMER DURABLES
© Absolutdata 2014 Proprietary and Confidential 8
What Is Big Data
Generating, storing, processing, analyzing data that was previously
ignored due to technological limitations
© Absolutdata 2014 Proprietary and Confidential 9
Why Institutionalize Big Data Analytics?
1 Based on Analysis of 31 companies with advanced analytical capabilities across BFSI, Telecom, Hi-Tech, CPG, Retail, E-Commerce, Pharma, Industrial
Products, Logistics and Media/Entertainment sectors
Source: Web search, Absolutdata Analysis
0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
Jun/04 Jun/05 Jun/06 Jun/07 Jun/08 Jun/09 Jun/10 Jun/11 Jun/12
Analytics Shakers S&P 500
Analytics Shakers1 vs. S&P 500
Companies that invested heavily in
advanced analytical capabilities
outperformed the S&P 500 and
recovered quicker from economic
downturns
Information & insight advantage is the new way to win
© Absolutdata 2014 Proprietary and Confidential 10
Scope of
Analytics
 Analytics is implemented
piecemeal across business units
 Analytics coverage across all
strategic functions & business
units like
Marketing, Finance, Recruitment,
Supply Chain, Operations, Call
Centres, Products etc
Strategic
Alignment
 Localized analytics with no real
strategic alignment
 Embed analytics in the decision
making fibre of the organization
 Strong governance & change
management
Today Institutionalized Analytics
Benefits Of Institutionalizing Big Data Analytics
Source: Absolutdata Analysis
© Absolutdata 2014 Proprietary and Confidential 11
Results
Oriented
 Limited RoI uplift
 Limited understanding in how to
utilize data for business decision
making & impact
 Strong commercial & business
impact focus
 Demonstrated capability in
delivering high Return on
Analytics Investment
Efficiency &
Effectiveness
 Low efficiency due to high
localization
 Low effectiveness due to lack of
highly skilled domain + analytics
experts
 Cost effective with an
engagement model to suit your
needs (Onshore/Offshore/ hybrid)
 Highly skilled workforce with a
combination of analytics &
industry expertise
Today Institutionalized Analytics
Benefits Of Institutionalizing Big Data Analytics
Source: Absolutdata Analysis
© Absolutdata 2014 Proprietary and Confidential 12
What Does Institutionalizing Big Data Analytics Mean?
 Analytics Trigger New/Different
Actions Across The Organization
– Long-Term Strategic Decisions
– Day-to-Day Operational
Decisions
 Analytics are used by majority of
decision makers
– Senior Levels
– Mid and Junior Levels
 Analytics are used in a timely
manner
– Before the decision is made not
after
– Accelerate Time-To-Answer
 Analytics provide a complete view
© Absolutdata 2014 Proprietary and Confidential 13
Big Data Information
InsightsUnderstanding
Enabled
Intuition
Actions
Big Data Information
InsightsActions
Enabling Direct Business Actions
Enabling Managerial Decision Making
Two Ways Big Data Is Used To Drive Actions
© Absolutdata 2014 Proprietary and Confidential 14
How to Institutionalize Big Data Analytics?
Easy Question – Difficult
and Long Answer
© Absolutdata 2014 Proprietary and Confidential 15
Building Blocks Of Institutionalizing Analytics
Source: Absolutdata Insights Hub
Data
Customer + Product +
Transaction Data
Mobile Data
Primary and 3rd party
data
A
Technology Data Loading &
Transformation
Data Storage
Tools &
Techniques
Data / Insights
Dissemination
B
People Capabilities People Model Training
E
Products Customer
Analytics
Social Media
Analytics
MIS + Ad-hoc
Reporting
Market
Research
D
Marketing
Effectiveness
Data
Visualization
Web Analytics
Partnerships
People:
Training
Partners
Data:
Demographics,
Industry Data
Technology:
Big Data Tools
F
Methodologies
Diagnostic
framework
Analytics
framework
Quality
G
Governance
Program
Management
Change
Management
Knowledge
Management
H
Engagement Model
Functions
Finance Marketing SalesOperations
C
HR Products Supply ChainStrategy
© Absolutdata 2014 Proprietary and Confidential 16
Case Study
Global Food Multinational with
presence in more than 80 countries
Analytics well entrenched in the US
but not outside of the US
© Absolutdata 2014 Proprietary and Confidential 17
PEOPLE
PROCESS
DATA
Challenges Institutionalizing Big Data Analytics
© Absolutdata 2014 Proprietary and Confidential 18
Absolutdata Answer To These Challenges
People Challenges
 Strong belief in gut instincts
 Pre-assumption of analytics as
“statistics; far from business
reality”
 Lack of understanding of the
results & implications; can’t
apply….
Confidence Building in the Power
of Analytics
 Definition of project team; key
roles
 On-job training of process, with
clear view of expected output
 Engagement of teams at key
milestones
– collect inputs vs. give updates
 Working with Business Teams as
partners in the application of
results that drive business
impact
Absolutdata Response
© Absolutdata 2014 Proprietary and Confidential 19
Absolutdata Answer To These Challenges
People Challenges
 Limited budget and high expense
 Complex, “black box “solutions
 Standard, off the Shelf Solutions
 Lack of standardized metrics for
comparisons
Adaptable, Practical & Affordable
Solutions
 Transparency of process, tool &
techniques.
 “Learn & Teach” not “Teach &
Learn ”
 Customizing of solution: “No two
countries are same & no two
brands are same”
 Innovative, cost effective Excel
and/or web-based tools that
evolve with users
 Standard Global Benchmark
database to capture key outputs
Absolutdata Response
© Absolutdata 2014 Proprietary and Confidential 20
Absolutdata Answer To These Challenges
Data Challenges
 Unstructured Capture &
Measurement of data
 Imperfect & missing data
 Lack of Faith in data
 Constantly Changing
Environment, Dynamic Markets
Building of Data & Causal factors
 Alignment of different data
streams (Shipment / Retail
Audit/ etc.) to arrive at best
representation of “Market
Reality”
 Break down of Aggregate data
into weighted weekly data
 Discussion-led creation of data
flags to capture events and key
spending time-periods
 Creative application of surrogate
variables
Absolutdata Response
© Absolutdata 2014 Proprietary and Confidential 21
What We’ve Learned
All hands in ownership and activation
 Work sessions with simulation tools engage all players and lead to effective and efficient decision
making
 Cross country knowledge and support of benchmark database fosters shared learning and cross
fertilization of ideas
 Flexible working arrangements support region and country needs - Onsite, Offshore & Hybrid
engagement models
 Support from Corporate/CI Regional managers’ is critical
 Ongoing dialogue leads to fast turnaround on projects and
accelerates forward movement
– Ownership, learning and decision making
– Weekly meetings to check in and review priorities,
timelines, next steps
 The end game is not a single project or projects. Rather, it is
an ongoing evolution to a more strategic, more data-based approach to decision making on the
local level
© Absolutdata 2014 Proprietary and Confidential 22
Key Changes Lead To Higher Quality Deliverables
 Dedicated ongoing teams:
– Senior Delivery Manager
– Internal team alignment towards building a regional focus
 Scaling up of Management and Delivery Teams
– Increase in Delivery Team size
– Ad hoc resources available for ad hoc needs
 Creation of a Process Book: A detailed user guide-line with
extensive details/checklist across every stage of the project
 Implementation of “Know your Brand/Category” initiative –
Developing overviews on brands before project kick-start call
 Development of client focused Feedback Form for ongoing
project improvement
© Absolutdata 2014 Proprietary and Confidential 23
What & How Did Our Clients Gain?
 Simple Yet Effective Solutions:
– Quick & Practical analytically driven
answers to critical business problems
– Complex analyses delivered in an easy
& business friendly format
– Consistency in data approach for
cross market benchmarking
 Analytics Evangelists
– Engagement with the business teams
on an ongoing basis through the entire
project and not just at the end
– Joint workshops with the business
team on application of analysis
– Increasing trust with the
process, output & team
 Cost Efficient World Class Talent
– Onsite as well as offshore
In 2012
 Top-line/bottom-line impact due to our
projects – Increase in revenue by 50 Mn in
just 5 countries
 Large-scale strategic business decisions
based on our presentations to the Board/
CXO level executives
 Increase in the client’s own analytics
team size (i.e. evidence of increasing
traction with the whole concept of
analytics in the client organization)
© Absolutdata 2014 Proprietary and Confidential 24
“Biggest Growth Impact” Award
in 2011
Two 2012 Amazing Analytics
Awards
Success Stories:
Identified opportunities to grow margin by $50 Million
without any additional investment”
- Central Insights Director, Global
We finally ended up at +1.1% vs. previous year!
- Marketing Director, Poland
Absolutdata has won numerous
Client awards, including:
‘this is the richest analysis I have ever seen at our company ’,
- Director, Analytics & Foresights Western Europe
Achievements
Name
Designation
Phone:
Email:
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From 'I think' to 'I know'

  • 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 30, 2014 From “I Think” to “I Know” Institutionalizing Big Data Analytics Anil Kaul, CEO - Absolutdata
  • 2. © Absolutdata 2014 Proprietary and Confidential 2 Agenda Speaker Intro Absolutdata Overview The Real Presentation ?
  • 3. © Absolutdata 2014 Proprietary and Confidential 3 Anil Bio  CEO & Co-Founder, Absolutdata  PhD and M.S. in Marketing from Cornell University  Experience with McKinsey and Co. deeply involved in marketing research and marketing science areas  Published articles on marketing in leading journals such as McKinsey Quarterly, Marketing Science, Journal of Marketing Research, and International Journal of Marketing  Invited speaker at conferences and premier business schools such as Cornell, Columbia, Dartmouth, NYU, and Yale  Presents at prestigious industry conferences like IIR, AMA, Esomar on analytics and research topics
  • 4. © Absolutdata 2014 Proprietary and Confidential 4 About Us  Big Data and Analytics solutions provider  Founded in 2001  $20 million invested by Fidelity for expansion  Headquartered in San Francisco and additional offices in New Delhi, London, Dubai, and Singapore
  • 5. © Absolutdata 2014 Proprietary and Confidential 5 Our Team  Senior management from McKinsey, CPG client, Pfizer, Mitsubishi, Nielsen, GE, HSBC, The Modellers and Citigroup  Over 320 professionals including Data Scientists, Modelers, Statisticians, and Business Analysts
  • 6. © Absolutdata 2014 Proprietary and Confidential 6 Analytics Data Visualization & Reporting Market Research Big Data Service Offerings
  • 7. © Absolutdata 2014 Proprietary and Confidential 7 Our Clients CPG CONSULTING SOFTWARE PHARMACEUTICAL BANKING APPAREL RETAILERS ONLINE HOSPITALITY TELECOMMUNICATION MOBILES MEDIA & ENTERTAINMENT OFFICE SUPPLIES EDUCATIONAUTOMOBILECONSUMER DURABLES
  • 8. © Absolutdata 2014 Proprietary and Confidential 8 What Is Big Data Generating, storing, processing, analyzing data that was previously ignored due to technological limitations
  • 9. © Absolutdata 2014 Proprietary and Confidential 9 Why Institutionalize Big Data Analytics? 1 Based on Analysis of 31 companies with advanced analytical capabilities across BFSI, Telecom, Hi-Tech, CPG, Retail, E-Commerce, Pharma, Industrial Products, Logistics and Media/Entertainment sectors Source: Web search, Absolutdata Analysis 0% 50% 100% 150% 200% 250% 300% 350% 400% 450% Jun/04 Jun/05 Jun/06 Jun/07 Jun/08 Jun/09 Jun/10 Jun/11 Jun/12 Analytics Shakers S&P 500 Analytics Shakers1 vs. S&P 500 Companies that invested heavily in advanced analytical capabilities outperformed the S&P 500 and recovered quicker from economic downturns Information & insight advantage is the new way to win
  • 10. © Absolutdata 2014 Proprietary and Confidential 10 Scope of Analytics  Analytics is implemented piecemeal across business units  Analytics coverage across all strategic functions & business units like Marketing, Finance, Recruitment, Supply Chain, Operations, Call Centres, Products etc Strategic Alignment  Localized analytics with no real strategic alignment  Embed analytics in the decision making fibre of the organization  Strong governance & change management Today Institutionalized Analytics Benefits Of Institutionalizing Big Data Analytics Source: Absolutdata Analysis
  • 11. © Absolutdata 2014 Proprietary and Confidential 11 Results Oriented  Limited RoI uplift  Limited understanding in how to utilize data for business decision making & impact  Strong commercial & business impact focus  Demonstrated capability in delivering high Return on Analytics Investment Efficiency & Effectiveness  Low efficiency due to high localization  Low effectiveness due to lack of highly skilled domain + analytics experts  Cost effective with an engagement model to suit your needs (Onshore/Offshore/ hybrid)  Highly skilled workforce with a combination of analytics & industry expertise Today Institutionalized Analytics Benefits Of Institutionalizing Big Data Analytics Source: Absolutdata Analysis
  • 12. © Absolutdata 2014 Proprietary and Confidential 12 What Does Institutionalizing Big Data Analytics Mean?  Analytics Trigger New/Different Actions Across The Organization – Long-Term Strategic Decisions – Day-to-Day Operational Decisions  Analytics are used by majority of decision makers – Senior Levels – Mid and Junior Levels  Analytics are used in a timely manner – Before the decision is made not after – Accelerate Time-To-Answer  Analytics provide a complete view
  • 13. © Absolutdata 2014 Proprietary and Confidential 13 Big Data Information InsightsUnderstanding Enabled Intuition Actions Big Data Information InsightsActions Enabling Direct Business Actions Enabling Managerial Decision Making Two Ways Big Data Is Used To Drive Actions
  • 14. © Absolutdata 2014 Proprietary and Confidential 14 How to Institutionalize Big Data Analytics? Easy Question – Difficult and Long Answer
  • 15. © Absolutdata 2014 Proprietary and Confidential 15 Building Blocks Of Institutionalizing Analytics Source: Absolutdata Insights Hub Data Customer + Product + Transaction Data Mobile Data Primary and 3rd party data A Technology Data Loading & Transformation Data Storage Tools & Techniques Data / Insights Dissemination B People Capabilities People Model Training E Products Customer Analytics Social Media Analytics MIS + Ad-hoc Reporting Market Research D Marketing Effectiveness Data Visualization Web Analytics Partnerships People: Training Partners Data: Demographics, Industry Data Technology: Big Data Tools F Methodologies Diagnostic framework Analytics framework Quality G Governance Program Management Change Management Knowledge Management H Engagement Model Functions Finance Marketing SalesOperations C HR Products Supply ChainStrategy
  • 16. © Absolutdata 2014 Proprietary and Confidential 16 Case Study Global Food Multinational with presence in more than 80 countries Analytics well entrenched in the US but not outside of the US
  • 17. © Absolutdata 2014 Proprietary and Confidential 17 PEOPLE PROCESS DATA Challenges Institutionalizing Big Data Analytics
  • 18. © Absolutdata 2014 Proprietary and Confidential 18 Absolutdata Answer To These Challenges People Challenges  Strong belief in gut instincts  Pre-assumption of analytics as “statistics; far from business reality”  Lack of understanding of the results & implications; can’t apply…. Confidence Building in the Power of Analytics  Definition of project team; key roles  On-job training of process, with clear view of expected output  Engagement of teams at key milestones – collect inputs vs. give updates  Working with Business Teams as partners in the application of results that drive business impact Absolutdata Response
  • 19. © Absolutdata 2014 Proprietary and Confidential 19 Absolutdata Answer To These Challenges People Challenges  Limited budget and high expense  Complex, “black box “solutions  Standard, off the Shelf Solutions  Lack of standardized metrics for comparisons Adaptable, Practical & Affordable Solutions  Transparency of process, tool & techniques.  “Learn & Teach” not “Teach & Learn ”  Customizing of solution: “No two countries are same & no two brands are same”  Innovative, cost effective Excel and/or web-based tools that evolve with users  Standard Global Benchmark database to capture key outputs Absolutdata Response
  • 20. © Absolutdata 2014 Proprietary and Confidential 20 Absolutdata Answer To These Challenges Data Challenges  Unstructured Capture & Measurement of data  Imperfect & missing data  Lack of Faith in data  Constantly Changing Environment, Dynamic Markets Building of Data & Causal factors  Alignment of different data streams (Shipment / Retail Audit/ etc.) to arrive at best representation of “Market Reality”  Break down of Aggregate data into weighted weekly data  Discussion-led creation of data flags to capture events and key spending time-periods  Creative application of surrogate variables Absolutdata Response
  • 21. © Absolutdata 2014 Proprietary and Confidential 21 What We’ve Learned All hands in ownership and activation  Work sessions with simulation tools engage all players and lead to effective and efficient decision making  Cross country knowledge and support of benchmark database fosters shared learning and cross fertilization of ideas  Flexible working arrangements support region and country needs - Onsite, Offshore & Hybrid engagement models  Support from Corporate/CI Regional managers’ is critical  Ongoing dialogue leads to fast turnaround on projects and accelerates forward movement – Ownership, learning and decision making – Weekly meetings to check in and review priorities, timelines, next steps  The end game is not a single project or projects. Rather, it is an ongoing evolution to a more strategic, more data-based approach to decision making on the local level
  • 22. © Absolutdata 2014 Proprietary and Confidential 22 Key Changes Lead To Higher Quality Deliverables  Dedicated ongoing teams: – Senior Delivery Manager – Internal team alignment towards building a regional focus  Scaling up of Management and Delivery Teams – Increase in Delivery Team size – Ad hoc resources available for ad hoc needs  Creation of a Process Book: A detailed user guide-line with extensive details/checklist across every stage of the project  Implementation of “Know your Brand/Category” initiative – Developing overviews on brands before project kick-start call  Development of client focused Feedback Form for ongoing project improvement
  • 23. © Absolutdata 2014 Proprietary and Confidential 23 What & How Did Our Clients Gain?  Simple Yet Effective Solutions: – Quick & Practical analytically driven answers to critical business problems – Complex analyses delivered in an easy & business friendly format – Consistency in data approach for cross market benchmarking  Analytics Evangelists – Engagement with the business teams on an ongoing basis through the entire project and not just at the end – Joint workshops with the business team on application of analysis – Increasing trust with the process, output & team  Cost Efficient World Class Talent – Onsite as well as offshore In 2012  Top-line/bottom-line impact due to our projects – Increase in revenue by 50 Mn in just 5 countries  Large-scale strategic business decisions based on our presentations to the Board/ CXO level executives  Increase in the client’s own analytics team size (i.e. evidence of increasing traction with the whole concept of analytics in the client organization)
  • 24. © Absolutdata 2014 Proprietary and Confidential 24 “Biggest Growth Impact” Award in 2011 Two 2012 Amazing Analytics Awards Success Stories: Identified opportunities to grow margin by $50 Million without any additional investment” - Central Insights Director, Global We finally ended up at +1.1% vs. previous year! - Marketing Director, Poland Absolutdata has won numerous Client awards, including: ‘this is the richest analysis I have ever seen at our company ’, - Director, Analytics & Foresights Western Europe Achievements

Notas do Editor

  1. Add team support