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Is Data Analytics Suitable for Finance Decisions?

By combining predictive and prescriptive analytics, CFOs can explore how changes in different variables can affect the outcomes and even alter the relative trade-off.

When it comes to business decisions, analytics technology is being widely used, but has been limited to specific departments, rather than leveraging it across the organization.

Experts believe that for financial business decisions, a combination of predictive and prescriptive analysis can provide a useful outcome. Predictive analytics uses regression analysis, predictive modeling, forecasting, pattern matching, and multivariate statistics to determine what can happen. Whereas, prescriptive analytics uses simulation, graph analysis, complex-event processing, heuristics, recommendation engines, neural networks and even machine learning (ML) to suggest the further steps to achieve a goal.

By combining predictive and prescriptive analytics, CFOs can explore how changes in different variables can affect the outcomes and even alter the relative trade-off. Experts have observed that in industries like transportation, manufacturing and financial services, the combination of predictive and prescriptive modeling has always been a part of the processes but its role is often isolated or trapped in specific, dedicated departments. For a business to be successful, the digital era demands flexibility, collaboration and agility. Hence, the ability to predict outcomes and assess the alternatives before taking action cannot be reserved for a few departments.

But the questions remains of choosing the best method from all the techniques and selecting an optimal mix. Experts suggest either using a predefined framework or following an outcome-driven evaluation with interdependent sets of options. The decisions of analytics can be either used for supporting human decisions or even be fully automated.

For any of the choice, the CFOs need to develop the skills of their team members to improve decision making. These start at changing hiring priorities and actually hiring data scientists. But since, this role comes with a hefty price tag, organizations have resort to training their in-house staff to do data-scientist-type roles. To use predictive and prescriptive analytics consistently, CFOs must find ways to leverage their existing resources before looking to outsource.

One interesting solution that experts suggest it to tap into the business citizen community. With many user-friendly and accessible data science products, employees are obtaining skills to leverage advanced analytical capabilities without an actual degree in data science. The products can be easily understood by business analysts. Such roles of ‘citizen data scientist’ can complement the accredited data scientists and extend an organization’s analytics usage.



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By Meeta Ramnani

Meeta Ramnani

Meeta develops credible content about various markets based on deep research, opinions from experts and inputs from industry leaders. As the managing editor at Smart Market News, she assures that every piece of news and article adds to the knowledge of decision makers. An avid bike rider, Meeta, is a postgraduate from Indian Institute of Journalism and New Media (IIJNM) Bangalore, where her specialization was Business Journalism. She carries experience from mainstream print media including The Times Group and Sakal Media Group.

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