Simplify your analytics strategy
- 1. Simplify Your Analytics Strategy by Narendra Mulani Sachin REC Sonbhadra
- 2. 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.
- 3. Discovering real business opportunities and achieving desired outcomes can be elusive.
- 4. To overcome this, companies should pursue a simpler path to uncovering the insight in their data and making insight- driven decisions that add value.
- 5. 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:
- 6. 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
- 7. Delegate the work to your analytics technologies. Uncovering data insights doesn’t have to be difficult. Here are ways to delegate the work to your analytics technologies:
- 8. Next-Gen Business Intelligence (BI) and data visualization. At its core, next-gen business intelligence is bringing data and analytics to life to help companies improve and optimize their decision-making and organizational performance.
- 9. Data discovery. Data discovery can take place alongside outcome-specific data projects
- 10. 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.
- 11. Machine learning and cognitive computing. Machine learning is an evolution of analytics that removes much of the human element from the data modeling process to produce predictions of customer behavior and enterprise performance
- 12. Recognize that each path to data insight is unique. The path to insight doesn’t come in one single form.
- 13. 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.
- 14. To do this, companies can take two approaches depending on the nature of the business problem. First, for a known problem with a known solution
- 15. Second, 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
- 16. 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. It is possible to uncover the business opportunities in your data and increase data equity, simply.