Companies often struggle with analytics strategies, mistaking complexity for effectiveness. To simplify their approach, businesses should create a data supply chain that enables faster insights and adopt outcome-driven analytics methods. Discovery-based and hypothesis-based approaches can guide organizations in uncovering actionable insights from their data.

1. BY:-
ANKITA KUMARI
SRM INSTITUTE OF TECHNOLOGY
Simplify Your Analytics
Strategy

2. Problem!!
Companies can get stuck trying to
analyze all that’s possible and all that
they could do through analytics,
when they should be taking that next
step of recognizing what’s important
and what they should be doing for
their customers, stakeholders,
and employees. Discovering real
business opportunities and achieving
desired outcomes can be elusive.

3. Companies should pursue a
simpler path to uncovering the
insight in their data and making
insight-driven decisions that add
value. Following are steps that
we have seen work in a number
of companies to simplify their
analytics strategy and generate
insight that leads to real
outcomes.
Solution!!

4. ACCELERATE THE DATA
• Fast data = fast insight = fast outcomes.
• Liberate and accelerate data by creating a d
ata supply chain built on a hybrid t
echnology environment — a data service
platform combined with emerging big data
technologies.
• Real-time delivery of analytics speeds up the
execution velocity and improves the service
quality of an organization.

5. Using Analytical Technology
Analytics 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.
Next-Gen Business Intelligence (BI) and data
visualization is bringing data and analytics to life to help
companies improve and optimize their decision making and
organizational performance.
Data Discovery can take place alongside outcome-specific
data projects. Through the use of data discovery techniques,
companies can test and play with their data to uncover data
patterns that aren’t clearly evident.

6. PATH TO DATA INSIGHT IS
UNIQUE.
• The path to insight doesn’t come
in one single form.
• No matter what combination of
culture and technology exists for
a business, each path to analytic
s insight should be individually
paved with an outcome-driven
mindset.
• 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.
• Once insights are uncovered, the next
step is for the business, of course, to
make the data-driven decisions that
place action behind the data.

7. INSIGHT

8. • Fast data = fast insight = fast outcomes.
• By creating a data supply chain built on a hybrid technolo
gy environment a data service platform combined with e
merging big data technologies, data can be accelerated.
• The path to insight doesn’t come in one single form. Ther
e 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. MANAGERIAL
INSIGHT

10. • For a known problem area but with an unkno
wn solution, the company could take a disco
very-based approach to look for patterns in t
he data to find interesting correlations that
may be predictive
• For a known problem with a known solution t
he company could take a hypothesis-based
approach by starting with the outcome

11. THANK YOU WATCHING