1. Simplify Your
Analytics
Strategy
Submitted by: Saurabh Ranjan
Institute: Jadavpur University
Under the guidance of :
Prof.Sameer Mathur

2. Steps to simplify the analytics
strategy and generate insight that
leads to real outcomes:
1. Accelerate the data:
2. Next-Gen Business
Intelligence (BI) and
data visualization.
3. Data discovery
4. Analytics
applications.
5. Machine learning
and cognitive
computing.

3. 1. Accelerate the data
Fast
Data
Fast
Insight
Fast
Outcome

4.  Liberate and accelerate data by creating a
data supply chain built on a hybrid technology
environment — a data service platform
combined with emerging big data
technologies. Such an environment enables
businesses to move, manage, and mobilize
the ever-increasing amount of data across the
organization for consumption faster than
previously possible. Real-time delivery of
analytics speeds up the execution velocity and
improves the service quality of an
organization.

5. 2. Next-Gen Business
Intelligence (BI) and data
visualization.
 Next-gen business intelligence is bringing
data and analytics to life to help companies
improve and optimize their decision-making
and organizational performance. BI does
this by turning an organization’s data into
an asset by having the right data, at the
right time and place (mobile, laptop, etc),
and displayed in the right visual form (heat
map, charts, etc)

6. 3. Data discovery
 Through the use of data discovery
techniques, companies can test and
play with their data to uncover data
patterns that aren’t clearly evident.
When more insights and patterns are
discovered, more opportunities to drive
value for the business can be found.

7. 4. Analytics applications
 Applications can simplify advanced
analytics as they put the power of
analytics easily and elegantly into the
hands of the business user to make
data-driven business decisions. They
can also be industry-specific, flexible,
and tailored to meet the needs of the
individual users across organizations

8. 5. Machine learning and
cognitive computing
 Machine learning is an evolution of
analytics that removes much of the
human element from the data modelling
process to produce predictions of
customer behaviour and enterprise
performance.
 Software intelligence is helping
machines make even better-informed
decisions.

9. Managerial relevance
 A manager should understand that there are many
different elements in play when it comes to
simplifying analytics and they are always changing,
for example business goals, technologies, data
types, data sources, and then some are in a state of
flux. A manager should be willing to change the
conservative culture of company if need be and
should be willing to take chances.
 He should know whether the company have a
plethora of existing data and analytics technologies
to work with, or is it just starting out with its first
analytics project.
 Then the manager should take the above mentioned
steps to simplify the analytics.