The COVID-19 pandemic has created a series of ongoing challenges for CFOs that is reshaping their finance teams. Finance teams are now assessing revenue, costs and cash flow on a weekly, daily and even near-real time basis now to help guide current and future business decisions.
From the longer ‘past quarter’ view to a more immediate pulse of the business, CFOs are being pushed to be more strategic and do so at an increasing pace. Financial planning and analysis have become more important than ever as organizations seek the best path to survive and even thrive during the crisis. And while this fight continues, may CFOs and their teams are wondering – what happens next and how do we prepare for post-pandemic finance?
Advanced analytics such as predictive forecasting and machine learning (ML) have long been touted for Corporate Finance teams. Why? At its core, giving Finance teams the capability to combine macroeconomic factors like GDP and consumer preferences with internal data to create better forecasts, increase collaboration and help drive decision-making brings the vision of Finance Transformation to reality.
And while not yet widely accepted like the move to the cloud for the financial close and planning processes, adoption of advanced analytics was already on the rise according to the Dresner Advisory Services in its 2019 Data Science and Machine Learning Market Study. From less than 40% of responding organizations that were using or actively exploring Machine Learning in 2016 to about 50% in 2019, the report shows a steady increase. The current economic uncertainties and rapidly changing business requirements will likely be a catalyst to drive that number up significantly.
While IT and Business Units have led the way in adopting ML — far outpacing Finance according to the research – the recent crisis demonstrates that Finance teams must also have rapid-response forecasting and decision-support solutions to help manage through uncertainty.
Read More: Is Covid-19 a Wake Up Call for InsurTechs?
In fact, finance teams were already evaluating advanced analytics according to a global survey that OneStream Software conducted last year. While only 14% of the respondents to the OneStream survey said they were currently using ML technology, another 35% were considering or planning to use it. That might not seem like a high number, but it corresponds closely to predictive analytics, where 16% are currently using the technology and 40% are evaluating it. Given that predictive analytics solutions have been widely available on the market for several years while ML is still emerging, those numbers are impressive for a Pre-COVID 19 study. As organizations manage through the crisis and re-asses their processes, we expect Finance teams will find a newfound interest in predictive analytics and ML.
Intelligent process automation was the top ML use case for companies surveyed in the OneStream study, but we believe sales and revenue forecasting will become an even greater driver as organizations seek to model scenarios and plan for a future that is difficult to predict and potentially volatile. Top use cases identified by the OneStream study in 2019 were:
- Intelligent Process Automation – 41%
- Sales/Revenue Forecasting – 34%
- Anomaly Detection – 32%
- Demand Planning – 32%
- Sales/Marketing Optimization – 30%
As Finance teams prepare to make the leap into advanced analytics, here are 3 key areas to consider for the journey ahead:
Increasing Forecast Accuracy
In the OneStream survey, when asked their current level of forecast accuracy 46% of respondents – a near majority — weren’t sure. For those who did know, 22% responded with 6 – 10% forecast accuracy. Another 17% claimed 3 – 5% accuracy, while only 6% claimed 1 – 2% forecast accuracy.
Interestingly, 48% of respondents said they were satisfied with the accuracy of their forecasts, although 83% said that improving forecast accuracy would have a medium to high impact on their business. When asked what factors were impacting their ability to produce more accurate forecasts, the primary responses included:
- Lack of line management input and accountability – 39%
- Lack of access to necessary data – 38%
- Ineffective software tools – 32%
- Poorly developed calculations and driver – 29%
- Unable to react quickly enough to Industry/market volatility – 29%
We are seeing advances in both predictive and ML solutions that will address several of these issues. The ability to react quickly to market volatility will emerge as the top priority, we believe. But perhaps the biggest obstacle is managing the complexity of ML solutions and making them more accessible – especially for finance teams. Smart organizations are getting proactive and finding ways to manage the complexity in house.
Availability of Data Scientists
Given the sophistication of ML models, organizations need to have trained data scientists on board to handle data preparation, manage the ML projects and help users interpret the results. A surprising 34% of the respondents to the OneStream survey said they had data scientists on staff while 3% were in the process of hiring. Another 12% were considering hiring data scientists. That brings the number to 49% who are at least actively considering as of last year, well before the crisis hit. From these results, it appears that data scientists have very promising opportunities ahead.
Interestingly, 41% of the respondents said the data scientists were part of the IT team, with 33% in Finance and 25% in Operations.
ML Integrated as Part of Finance Technology Platform
While data scientists can help cut through the complexities of machine learning, Finance teams still require easier access to sophisticated models and integrated solutions. The Dresner Advisory Data Science and Machine Learning market study from 2019, for example, found that 41% of those surveyed expect enterprise performance management (EPM and also known as corporate performance management or CPM) vendors to package AI and ML capabilities as part of the overall solution. Among the respondents, those from finance teams were the most likely to expect integrated, pre-packaged ML solutions. In fact, only a small number of organizations surveyed said they plan to build their own AI and ML capabilities for their finance platform.
As the CFO and finance teams become more strategic within organizations, we are seeing a growing need to assess, model and predict future scenarios to support agile decision-making. Predictive analytics and machine learning promise significant benefits for the finance team and have been gaining steady traction over the past five years as the technologies advance. The current crisis is a catalyst that will likely accelerate adoption of these technologies.
But first, these solutions must become more accessible to a broader range of users across the Finance team and beyond. Organizations require solutions capable of leveraging the sophistication of their data science teams but also with the scale and practicality to actually make a difference in business planning processes. And despite all the promise of automation, you can’t just push a button to make that happen overnight. Finance leaders have to do what they do best and lead the charge.