How data scientists can reshape the corporate finance function

01 December 2021 Consultancy.com.au 5 min. read
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For many years, corporate finance teams have been affectionately known as ‘bean counters’. Toiling away in back rooms, their role was perceived to be tallying up numbers and forming a clear picture of what’s been happening within a business.

These days, that perception is a long way from reality. Increasingly, finance teams are becoming a vital resource that helps to set goals and map out corporate strategies. Rather than focusing on what has already happened, their attention is clearly on the future.

Unfortunately, in many instances, such forward thinking is hampered by a lack of tools. Equipped with little more than Excel spreadsheets, finance teams struggle to obtain all the required data and analyse it to determine likely trends.

Peter O’Connor, Vice President Sales, Snowflake

While there are some software-as-a-service (SaaS) tools available that assist in forecasting and scenario modelling, they don’t offer a full solution. Data and reporting are usually locked within the SaaS tool, which makes it difficult to measure what’s changed between forecasts, thus stifling forward-looking insights.

Also, data can’t be shared with the broader organisation, which prohibits a collective understanding across business functions and with key stakeholders. Clearly, more needs to be done.

A finance evolution

Corporate finance teams need to evolve and become more strategic and data driven. Real-time feeds of operational data need to be harnessed to open up opportunities for teams to be proactive rather than reactive.

However, data alone will not fuel this evolution. There must also be the ability to create dynamic models that can re-forecast a multitude of indicators on a daily basis to deliver real-time business insights. The bottom line is that, as the future of finance is data-driven strategic planning and forecasting, what is required is an increase in investment in data science.

The role of the data scientist

Increasingly, companies are discovering the best way to establish an effective data-driven strategy is to hire data scientists. They are being brought onboard as finance team members 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 the various aspects of finance. These include:

Financial Planning & Analysis
Financial Planning & Analysis (FP&A) teams are responsible for forecasting and budgeting. Data scientists who learn the intricacies of their company’s pricing structure can build a series of models that reflect FP&A’s primary requirement for accurate forecasting.

When data science powers forecasting, a company will receive immediate feedback on how revenue is tracking and management can see how it’s evolving over time, which enables real-time adjustments.

Cost of goods sold
Data scientists can also build models to improve financials around the cost of goods sold (COGS). Organisations that either rely on supply chains or consume external resources in order to deliver a product or service benefit from analysing cost structures and margins. Since customer usage evolves over time, opportunities may exist to increase profitability by switching providers or renegotiating vendor contracts.

By understanding product demand, it’s possible to generate both a revenue and a cost forecast, illuminating opportunities to lower costs, increase margins, or adjust pricing.

Research & Development
Some companies may also want to conduct 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 provider. Using centralised data, data scientists can model whether a large upfront investment will pay off and how long that payoff period will be before it yields positive financial results.

Alternatively, data models can help determine whether an acquisition is a better option to bring a specific capability in-house.

Tax and Treasury
Companies looking to launch entities in new countries need to be aware of the associated tax implications. Treasury teams will want to make sure entities are properly funded, while balancing cost and revenue to ensure the right levels of taxation. Rather than make 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 what the impact is on forecasting revenue, costs, and cash flow.

Procurement
Data scientists can also make a difference when it comes to procurement by sharing information and ensuring collaboration happens between the procurement function and teams such as IT, marketing, and sales. For example, it’s not unusual for the sales and procurement teams to be completely unaware that each is working with a common customer or vendor. Realising this may present opportunities to negotiate better rates and terms that lower costs.

Further reading: How data-driven insights can transform corporate purchasing

Conclusion

Ensuring data scientists are a part of the corporate finance team can deliver significant benefits. By making better use of available data to enable more informed decision making, companies can be much better placed to capitalise on future changes and opportunities.

Other articles from Peter O’Connor on ConsultancyAU:
How the cloud can help overcome the data fragmentation challenge 
Seven ways how marketing analytics can add business value