The document discusses the importance of simplifying analytics strategies for companies to enhance insights and outcomes. Key recommendations include accelerating data processes, delegating tasks to analytics technologies, exploring data discovery techniques, using user-friendly analytics applications, and implementing machine learning for personalized customer interactions. Ultimately, a tailored, outcome-driven approach to analytics is essential for organizational success.

1. “Simplify Your Analytics Strategy”
-by Narendra Mulani

2. Companies can get stuck trying to analyse all that’s possible and
all that they could do through analytics, when they should be
recognizing what’s important — for their customers,
stakeholders, and employees.

3. Which is why, they should simplify their analytics strategy and
generate insight that leads to real outcomes:-

4. 1. Accelerate the data: Fast data = fast insight = fast outcomes.

5. 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.

6. Real-time delivery of analytics
speeds up the execution velocity &
improves the service quality of an
organization.

7. 2. Delegate the work to your analytics technologies.

8. For example, a financial services
company applied BI and data
visualization to see the different
buckets of risk across its entire
loan portfolio.

9. After analysing its key data and
displaying the results via
visualizations, the firm identified the
areas in the U.S. where there were
high delinquency rates, explored
tranches based on lenders, loan
purposes, and loan channels, and
viewed bank loan portfolios.

10. 3. Data discovery

11. Through the use of data
discovery techniques, companies
can test and play with their data
to uncover data patterns that
aren’t clearly evident.

12. 4. Analytics applications

13. 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.

14. For example, an advanced analytics app can help a store manager optimize his
inventory and a CMO could use an app to optimize the company’s global marketing
spend.

15. 5. Machine learning and cognitive computing.

16. As an example, a retailer combined
data from multiple sales channels
(mobile, store, online, and more) in
near real-time and used machine
learning to improve its ability to
make more personalized
recommendations to customers, to
target customers more effectively
and boost its revenues.

17. No matter what combination of culture
and technology exists for a business,
each path to analytics insight should be
individually paved with an outcome-driven
mindset.

18. Jahanvi Khedwal
National Institute of Technology, Raipur
jahanvikhedwal.nitrr@gmail.com