摘要: Data has three main functions that provide value to the business: To help in business operations, to help the company stay in compliance and mitigate risk, and to make informed decisions using analytics. “Data can have an impact on your top line as well as your bottom line,” said Dr. Prashanth Southekal, CEO of DBP-Institute in a recent interview with DATAVERSITY®. “Just capturing, storing, and processing data will not transform your data into a business asset. Appropriate strategy and the positioning of the data is also required,” he said. Southekal shared best practices for analytics and ways to transform data into an asset for the business.

 


Analytics

Lack of Analytics Success

Gartner predicts that by 2022, 90 percent of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency. “Given that the organizations across the world are looking at ways to glean insights from analytics and make good decisions today, not many companies are very successful in analytics,” he said. According to a recent McKinsey survey, most companies understand the importance of analytics and have adopted common best practices, Southekal remarked. Yet fewer than 20 percent have maximized the potential and achieved advanced analytics at scale. With this in mind, Southekal compiled a list of analytics best practices, using his experience working with successful analytics projects, projects with challenges, and those that fail.

Top Three Best Practices for Analytics

1.Improve Data Quality: Southekal defines analytics as the process of gaining insight by using data to answer business questions. Unfortunately, Data Quality is very poor in most business enterprises, he said, and poor-quality data cannot provide reliable insights. Data Quality will continue to remain poor under the current business paradigm, where businesses are constantly evolving — both internally and externally — in response to changing market conditions. Mergers and acquisitions require internal and external changes to often disparate data sources and systems. “Data Quality is a moving target and you can’t assume that if your data is good today, it will continue to be good, even after two years.” One option is to wait for the quality to improve over time, but in order to move forward in the immediate future, Southekal suggests creating a work-around with data sampling, acquisition, and blending of data from external sources, as well as investments in feature engineering.


2.Improve Data Literacy: More companies are recognizing that Data Literacy is critical to their future success with digital technologies and data analytics. Poor data literacy ranks as the second largest barrier to success among Gartner’s survey of Chief Data Officers, he said, who feel increased responsibility to ensure that data is easily available to stakeholders to use for all their daily operations. Building a data culture and investing in data literacy can show great benefits.


3.Monetize Data: “Go beyond insights and make the picture a little bit bigger by talking about data monetization,” he said. One effective way to monetize data is to look at data products. Also, monetizations entails reducing expenses, mitigating risk, and creating new revenue streams with data products.

 



 

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