Top 4 Questions CFOs Should Ask Their Data Analytics Teams

Last Updated: October 25, 2021

Deloitte recently revealed significant gaps between data, decisions and financial performance in its Analytics Advantage Survey. The study revealed that the greatest benefit of data analytics teams is better decision-making, according to 49% of business leaders; yet a mere 9% reported better financial performance as the greatest benefit. If business decisions are made with financial performance in mind, however, then there may be a significant gap between data analytics and finance.

Enterprises incorporating data-driven processes into strategic decisions are working to close this gap. They recognize the impact on greater organizational goals – that is, correlating data-backed decisions with financial results. To that end, many enterprises have implemented one of two effective approaches to better manage data analytics processes across the organization – enlisting a chief analytics officer to join the C-suite or creating a cross-functional data analytics team to bridge IT with information end-users.

data analytics teams

Both of these approaches aim to bridge the gap between data and finance, but that burden does not fall on data analytics teams alone. As data is increasingly implemented across more business functions, finance executives can do more to ensure their organizations are leveraging data analytics to their full potential and delivering the most value to the organization.

Here are the top questions CFOs should be asking their data analytics teams:

  1. What economic changes or unplanned events could deter financial performance?
    Expanding on the future-focused insights approach, finance executives need to be armed with all of the possible outcomes of a strategic decision to minimize uncertainty. Therefore, ask the data analytics teams to model several different outcomes based on possible unplanned events that could occur outside of an organization’s own four walls. Economic volatility and events like BREXIT cannot always be accounted for, but a forward-thinking approach to forecasting will help minimize the risk of external factors negatively impacting a company’s bottom line.
  2. Which markets will provide the best ROI in the next 3-5 years?
    The decision to enter or exit a market can be reactionary, based on performance trends, gut feelings or other influences, if executives are not careful in exactly what kind of answers they request from their data analytics teams. Keep insights focused on the future, rather than the past or even the present. This practice will ensure the organization gets ahead of competition in capitalizing on future demand and market opportunities – and that data analytics teams stay focused on insights that will deliver the most value.
  3. What areas of business do we need to support with more data-driven insights?
    Many enterprise data analytics teams spend the majority of their time answering ad-hoc requests and questions focused on the past. Because of this traditional challenge, however, this team understands better than anyone else in the organization which functions need more fact-based information. With machine learning and artificial intelligence tools available to help them become more efficient in their roles, data scientists have earned their seat at the table. The opportunity to eliminate timely data gathering and analytics processes allows these teams to finally focus on delivering real-time insights that matter most – and educate the C-suite on where accurate business intelligence is most needed.
  4. What do I not want to hear?  
    Data analytics teams are challenged with answering executives’ questions – and not just any questions, the tough questions. But sometimes, it is the unasked questions that yield the most actionable results. When an executive decision leads to less than favorable conditions, for example, data-driven analysis can uncover why. It is the analytics teams’ responsibility to inform and educate the C-suite where they went wrong. Therefore, it is important to empower data analytics teams to focus on the factors influencing poor performance when answering “why did this happen?” so that lessons learned are not repeated, and to make it clear that bad news should be reported.

CFOs must have a clear understanding of threats and opportunities ahead, which is dependent on their ability to gain real-time, actionable intelligence from their data analytics teams. By improving the accuracy of financial forecasts and demand predictions, companies gain a competitive advantage from the ability to mitigate threats to performance.  Download our playbook, Breaking the CFO Paradox to learn the five steps finance must take to close the data to decisions gap.