The protection of financial performance has always focused on numbers, and today, big data and automation are enabling finance leaders to take key performance indicators to a higher level. While the acceleration of new data has generated more opportunities to improve KPIs, managing that information and converting it into clear and actionable insights has proven to be challenging.

The challenges are particularly severe in businesses with data frameworks spreading across multiple systems. These tend to include gaps in data and inconsistencies in the form and quality of stored information. To utilise the best data-driven performance, finance businesses must first focus on ensuring the necessary information is captured and that any data plans fit with their key financial strategies and overall business goals. This process boils down to data governance and establishing who owns the data model.

Before considering what insights and value can come from the data, a fair way of getting that data into systems and governing for effective use needs establishing. There is great potential in leveraging data and analytics to enhance financial performance, but without clarity and truth, businesses can potentially get stuck in a constant cycle of continuous reconciliations and inaccurate data integrity that reduces the overall value of data to a business.

Governance needs to be the initial priority before considering the insights and value that can be extracted from data.
Traditionally, the IT department would have the bulk of responsibility for the data area, but lacking a complete understanding of fiscal KPIs can result in inaccuracies and unproductive work. Finance needs to have some form of ownership of the data model, along with the IT section. Finance has a strong understanding of the definitions and calculations of financial data. The capability of leveraging financial data can enable businesses to progress and keep time spent and costs to a minimum.

While most businesses are still in the early stages, automation is becoming a vital element in finance processes, such as leveraging technology to scan invoices and automating other accounts payable processes. For example, Workday combines weekly employee engagement reports with attrition data, then implements AI and predictive analytics to create adaptive planning financial forecasts.

The entire process takes time, and finance businesses should acknowledge that automation is challenging to integrate. If the information fed in at the beginning is poor, it is more likely to end with poor results. Companies need to invest time in ensuring they have the correct measures at the beginning of the process to allow everything further down the line to be clear and of high quality.