For many years, corporate finance teams have been affectionately referred to as “bean counters.” Working painfully in the back rooms, their role was seen as keeping track of the numbers and forming a clear picture of what was going on within a company.
Nowadays, 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 focused 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 data they need and analyze it to determine likely trends.
While there are Software as a Service (SaaS) tools available to help with scenario forecasting and modeling, they do not offer a complete solution. Data and reports are usually locked in the SaaS tool, making it difficult to measure what has changed between forecasts, thus suffocating forward-looking information.
Additionally, data cannot be shared across the organization, which precludes a collective understanding across business functions and with key stakeholders. It is clear that more needs to be done.
A financial evolution
Corporate finance teams need to evolve and become more strategic and data-driven. Real-time operational data flows should be leveraged to provide opportunities for teams to be proactive rather than reactive.
However, data alone will not fuel this development. It must also be possible to create dynamic models capable of reproducing a multitude of indicators on a daily basis in order to provide commercial information in real time. The bottom line is that as 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
Increasingly, companies are finding that the best way to build 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 issues.
Integrating data scientists into 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 predictions.
When data science powers forecasting, a business receives immediate feedback on how revenue is tracked and management can see how it is changing over time, allowing for 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 switching suppliers or renegotiating supplier contracts.
By understanding product demand, it is possible to generate both revenue and cost forecast, highlighting opportunities to reduce costs, increase margins, or adjust prices.
Some companies may also wish 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 payback period will last before it produces positive financial results.
Alternatively, data models can help determine if an acquisition is a better option for bringing specific capacity 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 that 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, and then determine the impact on revenue forecast, 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. Doing so 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 corporate finance team can provide significant benefits. By making better use of the data available to enable more informed decision-making, businesses can be in a much better position 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