This presentation is based on the article Simplify Your Analytics Strategy by Narendra Mulani.I have made this presentation
as a part of my data internship course
2. While the interests in analytics and
resulting benefits are increasing by the day,
some businesses are challenged by the complexity and confusion
that analytics can generate.
Companies should pursue a simpler path to uncovering the insight in their
data and making insight-driven decisions that add value.
3. Accelerate the data:
Fast data = fast insight = fast outcomes.
Liberate and accelerate data by creating a data
supply chain built on a hybrid technology
environment.
4. • Next-Gen Business Intelligence (BI) and data visualization
BI turns 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) for each individual decision-maker, so they can
use it to reach their desired outcome.
When the data is presented to decision-makers in such a visually appealing
and useful way, they are enabled to chase and explore data-driven
opportunities more confidently.
5. can take place
alongside outcome-specific data projects.
When more insights and patterns are discovered,
more opportunities to drive value for the business can be found.
6. 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.
7. 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.
8. The path to insight doesn’t come in one
single form.
There are many different elements in
play,
and they are always changing —
business goals, technologies, data
types, data sources, and then some are
in a state of flux.
9. Company’s analytics journey also depends on the :
Does it have a plethora of existing data
and analytics technologies to work with,
or is it just starting out with its first analytics project?
Is it more conservative or willing to take chances?
10. Each path to analytics insight
should be individually paved
with an outcome-driven mindset
For this, companies can take two
approaches depending on the nature of the
business problem:-
11. 1st for a known problem with a
known solution.The company
could take a hypothesis-based
approach by starting with the
outcome (e.g. cross-sell/up-sell
to existing customers), pilot and
test the solution with a control
group and then scale broadly
across the customer base.
2nd, for a known problem
area, fraud for example, but
with an unknown solution,
the company could take a
discovery-based approach to
look for patterns in the data
to find interesting
correlations that may be
predictive
13. As a manager of the company,
it his duty to realise and implement
the strategies that simplify the
advanced analytics.The business
intelligence and visualising data,
analytics application along with
machine and cognitive programming
could prove to be an impetus to make
informed data-driven decisions.Also, he
must keep in mind the problem to analyse
and that it has a known solution or
doesn't have a known solution.Based on
that, insights should be made and must be
moved on for business, that is, to make the
data-driven decisions that place action behind the data.
