How can data improve the role of finance in business, despite demanding times?

Finance is under pressure to deliver tangible business value. Data (and using it correctly) brings completely new insights to a business and provides a real competitive advantage. Data comes from all directions from within and outside the organization – such as business units, operations, customers, supply chain partners and other stakeholder interactions.

The key to future business success is to first capture this continuous flow of business intelligence data. Next, processes like data visualization and modeling can strengthen decision making for finance and business stakeholders.

In a digital business, a foundational data framework can be the equivalent of a satellite navigation system. The more up to date the data, the faster and smoother the journey to the desired destination. All business strategies should have a desired destination and a timescale within which to get there.

Demanding times: the future can’t be predicted, but it can be modeled

Unfortunately, there can be many challenges on the journey to success, and so real-time recalibration is vital. Why? Because even the most sophisticated supercomputers can’t predict the future. They can’t factor in environmental, political and economic discourses that will continue to test the efficiency and profitability of businesses.

COVID-19, the war in Ukraine and the global cost of living crises are high-profile recent examples of situations beyond our control. More locally, firms continue to be challenged by anomalies no amount of data modeling could have planned for.

The Texas Big freeze in 2021, for example, saw public transport systems shut down. Staff costs still had to be paid while income was zero. Damaged vehicles not only had to be repaired but insurance premiums were adjusted, increasing the total cost of running the service moving forward.

Ongoing in the UK, the 2022 national train strike is predicted to have cost the economy in excess of £100 million – unplanned costs have to be factored into immediate spending and future planning whether these costs are easily identifiable or not.

In all these instances, as in normal times, data is one of the most valuable assets of any organization. Unlocking its value can be a catalyst to positive business outcomes, even when the odds are stacked against success.

For example, at a time when flight risks are high, data visualization can help to

• improve operational efficiencies,
• support business expansion, and

maintain employee engagement.

The total cost of an unplanned event is hard to establish without purpose-driven financial planning and forecasting. Traditional finance processes don’t have the agility to start to support these situations whereas intelligent, automated and frictionless finance processes do.

The answers to most business questions are in the data, but do you have the means to find them?

Agile data and analytics can help improve profits by supporting better operational decisions, more accurate investment and expansion choices, and quicker identification of potential risks. Further, they replace repetitive tasks with insights and move finance on from transaction processing to business analysis with the help of predictive models.

Gillian Nolan, international CFO at Alight Solutions explained, “The future of business is built on real-time financial information. We can’t guess. As a result, we’re seeing the democratization of financial data so it can be used to answer business leaders’ questions and strategy can be adapted faster than competitors.”

Traditionally, financial data has been the domain of financial analysts and data scientists. In the digital world, data can’t be the preserve of specialists and presented monthly, quarterly or annually, it needs to be available. All stakeholders should have ready access to digestible business intelligence via real-time data platforms.

Changing roles in the finance team?

This doesn’t mean people are out of the picture. The roles of the finance team will just change. Financial analysts, for example, will continue to be the data literate, understanding what the data does and doesn’t say. They will still build the data sets and model from these, but now communicate the business opportunities /or threats regularly in simple terms to a wider business community.

With this clarity, the vast amounts of data gathered daily can be used to solve business problems, spot trends and make decisions to support new ideas using a mix of statistics, databases, data visualization and machine learning.

Start by building the data foundation layer

One of the greatest obstacles to success is the technology platform and the quality of data. The higher the number of systems and data sets the harder it is to make sense out of data. Governance, handling, validation, extracting, and the standardization of data are key.

Improving infrastructure will make it easier to add AI, machine learning,, and predictive analytics tools. These will further enable executives to make real-time decisions and model what will happen and what needs to be done much more easily.

There is no denying that global data collection is a challenge and opening it up is not without its contests. Steps to be considered are to build a user-friendly business intelligence reporting system and ensure consistent data that supports a decentralized structure.

The setting up of a frictionless finance function will be a tough test for finance teams, but the business outcomes should make the investment worth it. Data bring finance closer to the business in demanding times. And there is help out there.