The document discusses ways that businesses can make advanced analytics and big data work for them. It notes that while data has become a key business asset, big data projects also carry risks if not properly managed. It recommends that businesses choose the right data, build models that optimize business outcomes, and transform company capabilities with analytical skills. Finally, it stresses that managers need creative ways to use new data sources and algorithms daily, while separating statistics experts from managers who use data-driven insights.

1. MAKING ADVANCED
ANALYTICS WORK FORYOU

2. WAYS IN WHICH BIG DATA HAS IMPACTED
BUSSINESS

3. 1. Data has become an asset to every business.

4. 2. Big data has enabled companies to collect
better market and customer intelligence.

5. 3. It can improve internal
efficiency and operations

6. BUT…

7. As with any business initiative, a big data project
involves an element of risk. Any project can fail for
any number of reasons: bad management,
under-budgeting, or a lack of relevant skills.

8. WAYSTO MAKE DATAWORK IN
FAVOUR

9. 1. Choose the Right Data
Bigger and better data give companies both more
panoramic and more-granular views of their business
environment.The ability to see what was previously invisible
improves operations, customer experiences, and strategy.

11. 2. Build ModelsThat Predict and Optimize
Business Outcomes
The most effective approach to building a model rarely
starts with the data . Instead, it originates with identifying
the business opportunity and determining how the model
can improve performance.

13. 3.TransformYour Company’s Capabilities
Even with simple and usable models, most organizations
will need to upgrade their analytical skills and literacy.
Managers must come to view analytics as central to
solving problems and identifying opportunities—to
make it part of the fabric of daily operations. Efforts will
vary depending on a company’s goals and desired time line.

15. MANAGERIAL RELEVANCE

16. Managers need to get creative about the potential
of external and new sources of data .They need transparent
methods for using the new models and algorithms on
a daily basis. By necessity, terabytes of data and
sophisticated modeling are required to sharpen marketing,
risk management, and operations.The key is to separate
the statistics experts and software developers from the
managers who use the data-driven insights. One large
industrial company, for instance, sought to better forecast
workforce needs to reflect local market variations.