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For many years, corporate finance teams have been affectionately known as “bean computers.” They worked in the back rooms and their role was seen as adding up the numbers and creating a clear picture of what was going on in the business.
Nowadays, this idea is far from reality. Finance teams are increasingly becoming a vital resource to help set goals and map business strategies. Rather than focusing on what has already happened, their focus is clearly on the future.
Unfortunately, in many cases such forward thinking is hindered by a lack of tools. Armed with little more than Excel spreadsheets, finance teams struggle to get all the data they need and analyze it to determine likely trends.
While some software-as-a-service (SaaS) tools are available to help with forecasting and scenario modeling, they do not offer a complete solution. Data and reporting are typically locked in a SaaS tool, making it difficult to measure what has changed between forecasts, suppressing forward visibility.
Data also cannot be shared with the wider organization, preventing collective understanding across business functions and key stakeholders. Clearly, more needs to be done.
The evolution of finance
Corporate finance teams must evolve to become more strategic and data-centric. Real-time operational data sources need to be leveraged to open up opportunities for teams to be proactive rather than reactive.
However, data alone will not support this development. There must also be the ability to create dynamic models that can re-forecast a large number of indicators every day and provide real-time trading statistics. The bottom line is that the future of finance is data-driven strategic planning and forecasting, and therefore increased investment in data science is necessary.
Data Scientist Role
Increasingly, companies are discovering that the best way to create an effective data-driven strategy is to hire data scientists. They are recruited as finance team members who live and breathe finance, developing an understanding of day-to-day work and pain points.
Incorporating data scientists into the finance team means they can act as functional experts with data and all different aspects of finance. These include:
Financial planning and analysis
Financial Planning and Analysis (FP&A) teams are responsible for forecasting and budgeting. Learning the intricacies of their company’s cost structure, data scientists can build a variety of models that reflect FP&A’s primary requirement for accurate forecasts.
When data science drives forecasting, the company receives immediate feedback on how sales are tracking, and management can see how this is evolving over time, allowing for real-time adjustments.
Cost of goods sold
Data scientists can also build models to improve financials around cost of goods sold (COGS). Organizations that either rely on supply chains or consume external resources to deliver a product or service benefit from analyzing cost structures and margins. As customer usage evolves over time, there may be opportunities to increase profitability by switching providers or renegotiating contracts with suppliers.
By understanding product demand, both revenue and cost forecasting can be generated, illuminating opportunities to reduce costs, increase margins or adjust prices.
Research and 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 from a third-party provider. Using centralized data, data scientists can model whether a large initial investment will pay off and how long the payback period will be before it yields positive financial results.
Alternatively, data models can help determine whether an acquisition is a better choice for introducing a particular capability to the business.
Tax and checkout
Companies wishing to establish entities in new countries must be aware of the associated tax implications. Treasury teams will want to make sure entities are properly funded, while balancing costs and revenues to ensure the right level of taxation. Rather than making high assumptions, data scientists can model when and where to launch entities based on factors such as customer location, sales, and renewals, and then determine how that will impact revenue, cost, and cash flow forecasts.
Public procurement
Data scientists can also make a difference when it comes to procurement by sharing information and ensuring collaboration between procurement and teams such as IT, marketing and sales. For example, it is not uncommon for sales and purchasing teams to be completely unaware that they are each working with a common customer or supplier. Realizing this can present opportunities to negotiate better rates and terms that keep costs down.
Further reading: How data-driven insights can transform corporate purchasing
Conclusion
Ensuring that data scientists are part of the corporate finance team can bring significant benefits. By making better use of available data to enable more informed decision-making, companies can take much better advantage of future changes and opportunities.
More articles by Peter O’Connor on ConsultancyAU:
– How the cloud can help overcome the problem of data fragmentation
– Seven ways marketing analytics can add business value
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