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.
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.
Data Must be Usable
Data from texts, video, audio, and other similar types of data are in an unstructured form when the data is initially captured, he said. The process of conversion from a raw state into a processed format creates value because it becomes usable for insights and decision-making.
Intuition vs. Data
“The real substitute for data is intuition,” he said. Insight for design-making can come from data or from intuition, so in companies where data literacy is poor, intuition will prevail over data in decision-making. Users no longer need to rely on intuition when they realize they can rely on better decisions made with good data.
Top Three Best Practices for Analytics
- 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.
- 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.
- 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.
In most places, he said, analytics initiatives are run like projects, with a fixed start and end date and a specific purpose. The focus and the resource commitment inherent in project-based thinking is good, but Southekal recommends also thinking about analytics as a potential data product.
“LinkedIn is a data product. Bloomberg Solutions is also a data product. You can even build a report which gives you a sales margin and call it a data product.” The objective of building data products is to have scalable and long-term solutions, instead of a short-term solution that ends with the project. “Analytics as a strategic endeavor has to be a long-term initiative, so you have to treat analytics as a data product delivery mechanism, not just as a project initiative.”
How to Build a Data Product
He suggests considering the business as a network of customers, employees, vendors, and partners; look at business as an end-to-end value chain. Rather than seeing procurement, for example, as a single line of business, take into account the entire value chain within procurement. This process helps identify all of the players and what their value propositions are, “And will also identify where the value leaks are in the whole chain,” he said. The solutions created to fix those leaks are potential revenue-generating products.
Analytics Best Practices
Southekal recently published a book, Analytics Best Practices. He said that the book offers prescriptive and practical guidance that can be used in a variety of settings. His goal was to address the four pillars of analytics — Data Management, Data Engineering, Data Science, and Data Visualization — with ten best practices, and to do so by focusing on concepts rather than on specific tools or platforms. “It’s practical, it’s complete, it’s neutral.”
DBP Institute helps companies get the most out of their digital technologies and data by implementing new solutions or by optimizing existing solutions, he said. They work primarily in higher education and corporate settings, as well as offering analytics education and training online and at conferences.
Recipes for Success
Because of the effects of the COVID-19 pandemic, many companies are turning to data and digital technologies as key enablers. Southekal created a reference architecture document he calls The Reference Architecture for Digital Enablement (TRADE) to help companies with their digital enablement and analytics initiatives. Analytics is ultimately about data, he said, but to capture data, you need mechanisms for data storage, processing, and integration. “I’ve collected ‘recipes’ for best practices, and now when I work with customers, I bring TRADE, my implementation cookbook.”
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