How Finance Teams Can Embrace Their Predictive Future With Data Analytics
I recently caught up with Rishi Grover, Chief Solutions Architect at Vena Solutions, to examine the way analytics technologies are helping corporate finance teams move from traditional, manual ways of analyzing their numbers to a more automated, data-driven role in driving company-wide decisions. Rishi’s expertise in enterprise forecasting, analytics and regulatory reporting software stems from his involvement in implementing hundreds of successful financial software solutions at Vena and in his prior role as Director of Enterprise Solutions at Clarity Systems and IBM. Rishi holds a Bachelor of Applied Sciences from the University of Toronto, specializing in Computer Engineering and Communication Systems.
insideBIGDATA: How is data analytics used in corporate finance today? How has the use of analytics evolved over time in finance?
Rishi Grover: The use of analytics in finance has evolved over time, especially in terms of the amount and breadth of data available, but the most important aspect of that evolution is what CFOs and their teams can now do with their data.
Bringing together data that historically wasn’t collected or even readily available to the finance department — analytics today now helps finance departments better understand the past, present and the future of their own function and that of their organization as a whole.
A typical finance example of using analytics to understand the past and present is comparing current period actuals with previous budgets and forecasts, then examining variances and the root causes behind them. The same technologies can then help an entire organization forecast future results more accurately and, ultimately, make better business-wide decisions.
insideBIGDATA: Does the use of advanced analytics make enterprise finance teams smarter? What benefits do analytics offer finance teams and organizations as a whole?
Rishi Grover: The real value of analytics technologies in finance emerged as developers and data scientists started building products with the business decision-maker in mind, not something for the IT department to own and manage. That doesn’t mean technology alone makes finance professionals “smarter.” Like any tool, analytics technologies can be very powerful, but they’re only as strong as the users at the switch – in this case with the right combination of finance expertise, analytical mind-set and strategic vision.
By applying their expertise and thinking effectively, finance teams can use advanced analytics to help change the entire trajectory of their organization for the better. They can help organizations choose the right path forward – for example – by integrating internal financial and operational data, external benchmarking metrics, and advanced predictive modelling algorithms. In doing so, you can start to see trends earlier, perform what-if scenarios more efficiently, and respond more quickly and well informed.
insideBIGDATA: Does data analytics increase data integrity? Or, is it vice versa?
Rishi Grover: Even the best technology and expertise won’t deliver great results if the quality and integrity of the data isn’t there from the beginning. Having said that, performing data analytics can help determine where the data integrity issues are, and what internal processes or other changes need to be made to close the loop.
In this sense, data integrity and data analytics work hand in hand — just by getting started, you’re likely to improve the quality of the data you’re working with over time.
insideBIGDATA: How do predictive or prescriptive analysis fit into all this – both in finance and across the organization?
Rishi Grover: The most successful companies get so good at using data that they can move into predictive and prescriptive analytics that redefine what their future holds. By “predictive,” I’m talking about analytics helping companies create not just more accurate forecasts, but forecasts that can improve themselves automatically through machine learning and a constantly growing data set of actual performance metrics.
Prescriptive analytics, as the name implies, gives companies suggestions on where analysis should occur, where investments should be allocated, and other possible areas that would require human intervention and analysis.
Neither of these analytics opportunities should be limited to financial data, however. This is something that can extend across the whole organization and works best when forecasts are based on financial and non-financial, internal and external data.
insideBIGDATA: What do finance teams need to do now to prepare for the future of analytics? What does that future hold?
Rishi Grover: Analytics is still a relatively new concept for most departments, but finance can take advantage of tools readily available now to increase their competitiveness in the future. My advice is for finance to keep learning about these technologies, understand their use cases in other industries or departments, especially those more quickly adopting analytics today. Collaborate with your peers to find innovative ways it can be used in your finance department or across your organization.
Next, invest in the breadth and quality of the data. Collect as much data as possible, even if it may not seem relevant today. Ensure the data is as clean and structured as possible, and ensure all data sources can be integrated to a single environment (now or in the future) to take full advantage of advanced analytics.
Finally, begin to reimagine financial processes in a world where predictive technologies can intuitively help to create accurate forecasts systematically, where machine learning updates the predictive models based on actual results, and where artificial intelligence helps identify the best areas to invest in for growth. That’s what the future of finance is starting to look like … fast.
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