Analysis of "Making Advanced Analytics Work for You" by Dominic Barton and David Court

1. Making Analytics Work

2. Is Big Data the Magic Wand?
For several years now, we’ve seen Big data transform
businesses worldwide. Big data is attracting serious
investment from technology leaders and the tide of private-
equity and venture-capital investments in big data continues
to swell.
Even after knowing the power of Big data, some companies
are afraid to wield it. They are wary of making substantial
investments in big data and advanced analytics.

3. Why do some companies back off from
Big Data?
• Some are convinced that their organizations simply aren’t
ready to use the data they have, to develop transformational
business models.
• Perhaps they’ve lost piles of money on data-warehousing
programs that never meshed with business processes
• Their current analytics programs are too complicated or
don’t yield insights that can be put to use

4. The Transformational path
The trends that have been observed from companies who have
successfully used analytics to develop businesses are three-fold. By
following this path, we avoid getting stuck with the data and simply
asking what it can do for us.
Data
Sourcing
Model
Building
Organizational
Transformation

5. How to make analytics work?
• Identify, combine, and manage multiple sources of data.
• Have the capability to build advanced analytics models for
predicting and optimizing outcomes.
• Management must possess the muscle to transform the
organization so that the data and models actually yield better
decisions.

6. The Rules of the Data Sleuths

7. • Choose the right data: Data has exploded exponentially over
the past few years and its easy to get lost in it. Organizations
need to use creative ways to identify usable data you already
have, and exploring surprising sources of information.
• Source Data creatively: Companies can impel a more
comprehensive look at information sources by being specific
about business problems they want to solve .

8. • Take IT support: Current IT structures aren’t viable for
processing of unstructured data. A short-cut to this problem
is quickly identifying and connecting the most important
data for use in analytics, followed by a cleanup operation to
synchronize and merge overlapping data.

9. Build Models that Predict and Optimize
Business Outcomes
Data are essential, but performance improvements and
competitive advantage arise from analytics models that allow
managers to predict and optimize outcomes. It is a simple 2
step process:
• Identifying the business opportunity
• Determining how the model can improve performance.

10. Analytic modeling: The Risks and the Rewards
• A hypothesis-led modeling generates faster outcomes and
also roots models in practical data relationships that are
more broadly understood by managers.
• Although advanced statistical methods indisputably make for
better models, statistics experts sometimes design models
that are too complex to be practical.
• Companies should ask, “What’s the least complex model that
would maximize performance improvement?”

11. Analytics = ~!@$##*?

12. A sword is only as sharp as the person wielding it. Many Big
Data ventures fail as key decision makers don’t believe the
analytics model’s results and had little familiarity with how it
worked.
What we really need to undergo is an organizational change to
empower each employee with analytics and make them Data
savvy.
Transform Your Company’s Capabilities

13. The 3 step transformation
Develop business
relevant analytics
that can be put to
use
Embed analytics
into simple
tools
Develop
capabilities
to exploit big
data

14. Bringing it all together!!
To harness the power of Big data and analytics to its fullest, companies
need to:
• Invest on creating reliable and relevant sources of data for processing.
• Build advanced analytics models to predict and optimize business
outcomes.
• Empower employees to understand analytics and take decisions
based on them , bring about a change in organizational culture.

15. THANK YOU!!