The document discusses advanced analytics and how companies can exploit analytics for competitive advantage. It notes that data is becoming an important part of differentiation and companies using big data see 5-6% higher performance. To exploit analytics, companies must identify and manage multiple data sources, build predictive models, and ensure management can transform insights into decisions. Choosing the right data, getting necessary IT support to continuously provide data, building hypothesis-led models, and transforming company capabilities are also discussed as important steps for a successful analytics strategy.

1. ADVANCED ANALYTICS

2. WHAT IS IT AFTER ALL?

3. CURRENT SCENARIO
➤ Data is driving new business models
➤ serious investment on data from tech giants
➤ Data will become important part of competitive
differentiation
Difference of 5-6% between
companies using big data
and those not
Andrew McAfee and Eric Brynjolfsson
of MIT
Some companies are
unsure, skeptical and lazy
Some others lost loads of
money trying to save data
without any application as
such

4. HOW TO EXPLOIT ANALYTICS
1. Identify, combine and manage multiple sources of data.
2. Capability to build advanced analytical models for
predicting and optimising data
3. Management must possess muscle to transform- data
models must yield decisions.
4. Lastly,
A. a clear strategy to use data analytics to compete
B. deployment of right technology architecture and
capabilities

5. ALSO….!
➤ Desired impact must drive integrated approach to:
1. data sourcing
2. data building
3. organisational transformation
➤ Sufficient time and energy must be invested
➤ Align managers for support of mission

6. CHOOSING THE RIGHT DATA
➤ Volume of information growing rapidly
➤ More powerful, less costly softwares - high accessibility
➤ Bigger and better data - granular, panoramic views
➤ Source and process data creatively
➤ More comprehensive look of information required
➤ One question to ask:
What would we do if we have all the decision we needed?

7. GET NECESSARY IT SUPPORT
➤ Legacy IT structures may hinder new types of data
sourcing
➤ Most of them were built to deliver flow in batches and
hence cant give data continuously
➤ Prioritise short term requirements
➤ Identify data —> clean up operation —>merge
overlapping data —> work around missing information
➤ Scaling computer power to meet demands cost-
effectively

8. BUILD MODELS TO PREDICT AND OPTIMISE BUSINESS
➤ Most effective approach does not start with data
➤ It starts with identifying the problem
➤ Simple data mining - inconsistent results - dozens of tests - 0
benefit
➤ Hypothesis led modelling leads to faster outcomes.
➤ All data models have inherent risk.
➤ Simple question to ask :
Whats the least complex model which will improve performance?

9. TRANSFORM COMPANIES CAPABILITIES
➤ Generally, people at top decision making positions:
Don’t believe in the model
Don’t know how it works
➤ Mismatch between existing culture and emerging tactics.
➤ Tools seem to be designed for experts rather than for people
on front line.
➤ Using big data needs thoughtful organisational change
and 3 areas of action can get us there

10. WAY FORWARD
➤ Conversations between model designers and managers
➤ Pricing analytics and resulting scenario tools need to
complement existing processes.
➤ Transparent methods for daily usage.
➤ separate statistics experts, technology experts from decision
taking managers.
➤ Simple tools to deliver complex analytical solutions.

11. MANAGERIAL RELEVANCE
➤ Upgrade analytical skills and literacy.
➤ Analytics central to solving problems and identifying
opportunities.
➤ Field and forum approach - learn by doing
➤ multi-faceted approach including training, incentives, role
modelling.
➤ promotion analytics
➤ targeted efforts yielding constant opportunities

12. CREDITS
➤ Google images
➤ wikipedia
➤ Making advanced analytics work by Dominic Barton and
D.Court
➤Thank you