Breakthroughs in the application of complex calculations to large volumes of data have enabled machine-learning methodologies to revolutionize business processes in nearly every industry. Some of the more recognized examples of machine-learning applications include personalized Netflix recommendations and related product modules from online retailers such as Amazon and Nordstrom.
However, there are less sexy yet equally impactful machine-learning examples, which include revenue management solutions used in hotels that incorporate these methodologies into an algorithmic engine to help produce pricing and inventory recommendations.
Unlocking the potential of machine learning for the office of finance remains a hot topic for financial planning and analysis (FP&A) leaders, industry analysts, and technology vendors alike. Even more specifically, continuous chatter surrounds the ways that machine learning can improve future FP&A processes and how finance leaders can prepare for deploying advanced analytics within their organizations.
Machine learning: What it is and what it can do for finance
Machine learning is one piece of a larger analytics puzzle that includes predictive analytics, artificial intelligence, and data mining. Although machine-learning techniques can be applied by different technologies in different industries for different objectives, the common denominator for finance in the machine-learning equation is data.
For finance, machine-learning technology can provide FP&A teams with more opportunities to uncover and analyze business drivers from external and internal data that can help finance leaders make—and influence—more insightful business decisions.
As an example, machine-learning methodologies applied within a financial planning platform can analyze weather, social media, and historical sales data to quickly discern their impact on sales. This information can then be used to help finance create more reliable and continuous forecasts and, in turn, a more accurate financial outlook.
Six questions to ask before deploying advanced analytics and machine learning
Getting the most out of back-office technology is a critical focus. Before jumping head-first into actively rolling out an advanced analytical approach in finance, here are six considerations that FP&A executives can evaluate to help build out their organization’s machine-learning roadmap.
- Is the data in your organization clean? Data is the foundation on which the future of finance will be built. It’s important to recognize that data cleanliness doesn’t exclusively apply to the function of finance; it’s a critical activity for all business units throughout the organization.
- Is data governance in place throughout your organization? If there are any data issues present, finance should focus on resolving them early on to avoid any downstream waste. These system leaks need to be identified and resolved to maintain data integrity.
- How familiar are you with external, ambiguous, and unstructured data? Get to know your data and start to explore how external indicators can help deliver better insight to the business. This is precisely where advanced analytics and machine-learning technologies can help.
- Do you have toolsets that facilitate Connected Planning? This involves adopting dynamic enterprise planning technology and collaborative business processes that can link business units across the enterprise with one another in real time. This allows insights generated by machine-learning in one function (for instance, the supply chain) to inform plans in an adjacent function (such as finance) quickly and reliably.
- How much time have you allocated for reconstructing end-to-end business planning logic? It takes time to establish a fresh approach to unravelling and reforming business planning logic. Establish a reasonable timeframe to achieve this and challenge the use of more convenient lift-and-shift strategies.
- How are you going to build capabilities for better business partnering? Transforming finance means more than solving for the technicalities of automation and analytics—it’s also about empowering and freeing up time for the internal finance team to execute change. Consider technologies and processes that can best support these initiatives.
As the office of finance looks to embrace machine-learning processes, one final thing that leaders should keep in mind is that there are human elements that advanced technology simply cannot anticipate. Finance teams will need to validate information and inject human judgement, such as market insight and unpredictable events, to develop a holistic and reliable planning and forecasting process.
Looking ahead, advanced analytics and machine-learning processes will augment human judgment in ways that we cannot foresee today. Finance leaders can begin to prepare for that future by thinking through and addressing these six questions.