How data scientists can reshape the corporate finance function
For many years, corporate finance teams have been affectionately referred to as the “bean counters”. Working in back rooms, their role was seen as counting numbers and forming a clear picture of what was going on within a company.
Today, this perception is far from reality. Increasingly, finance teams are becoming a vital resource that helps set goals and develop business strategies. Rather than focusing on what has already happened, their attention is clearly on the future.
Unfortunately, in many cases, such forward thinking is hampered by a lack of tools. Equipped with little more than Excel spreadsheets, finance teams struggle to get all the required data and analyze it to determine likely trends.
While there are software-as-a-service (SaaS) tools available that help with forecasting and scenario modeling, they do not offer a complete solution. Data and reports are typically locked into the SaaS tool, making it difficult to measure what has changed between forecasts, stifling forward-looking information.
Additionally, data cannot be shared across the organization, prohibiting a collective understanding across business functions and with key stakeholders. Obviously, there is still a lot to do.
A financial evolution
Enterprise finance teams need to evolve and become more strategic and data-driven. Real-time operational data streams should be leveraged to provide teams with opportunities to be proactive rather than reactive.
However, data alone will not be enough to fuel this evolution. It must also be possible to create dynamic models capable of re-predicting a multitude of indicators on a daily basis in order to provide real-time trading information. The bottom line is that since the future of finance is data-driven strategic planning and forecasting, what is needed is increased investment in data science.
The role of the data scientist
More and more, companies are discovering that the best way to establish an effective data-driven strategy is to hire data scientists. They are integrated as members of the finance team who live and breathe finance and thus develop an understanding of day-to-day work and pain points.
Embedding data scientists within the finance team means they can act as functional experts with data and with all different aspects of finance. These include:
Financial planning and analysis
Financial Planning and Analysis (FP&A) teams are responsible for forecasting and budgeting. Data scientists who learn the intricacies of their company’s pricing structure can create a series of models that reflect FP&A’s core requirement for accurate forecasts.
When data science powers forecasting, a business receives immediate feedback on how revenue is being tracked and management can see how it’s changing over time, enabling real-time adjustments.
Cost of Goods Sold
Data scientists can also create models to improve financial data around cost of goods sold (COGS). Organizations that rely on supply chains or consume external resources to deliver a product or service benefit from analyzing cost structures and margins. As customer usage changes over time, opportunities may exist to increase profitability by changing vendors or renegotiating vendor contracts.
By understanding product demand, it is possible to generate both revenue and cost forecasts, thereby highlighting opportunities to reduce costs, increase margins or adjust prices.
Some companies may also want to perform a research and development (R&D) assessment to determine whether it makes sense to develop something in-house or continue to purchase it from a third-party vendor. Using centralized data, data scientists can model whether a large initial investment will pay off and how long that period of payback will last before it produces positive financial results.
Alternatively, data models can help determine if an acquisition is a better option for bringing a specific capability in-house.
Taxation and Treasury
Companies looking to launch entities in new countries should be aware of the associated tax implications. Treasury teams will want to ensure entities are properly funded, while balancing costs and revenues to ensure the right levels of taxation. Rather than making high-level assumptions, data scientists can model when and where to launch entities based on factors such as customer location, sales, and renewals, then determine what the impact is on forecasts. income, costs and cash flow.
Data scientists can also make a difference in procurement by sharing information and ensuring collaboration between the procurement function and teams such as IT, marketing and sales. For example, it’s not uncommon for sales and procurement teams to be completely unaware that each is working with a common customer or supplier. Realizing this can present opportunities to negotiate better rates and terms that reduce costs.
Further reading: How data-driven insights can transform business purchasing
Ensuring that data scientists are part of the company’s finance team can provide significant benefits. By making better use of available data to enable more informed decision-making, businesses can be much better placed to capitalize on future changes and opportunities.
Other articles by Peter O’Connor on ConsultancyAU:
– How the cloud can help overcome the challenge of data fragmentation
– Seven ways marketing analytics can add business value