How Finance Teams Can Embrace Analytics to Graduate from Reporters to Advisors (2024)

Finance groups have traditionally and reliably produced objective, compliance-focused reports. The tools and methods haven’t changed much over the years; databases and spreadsheets have been enough to meet expectations. But today that’s not enough: digital transformation is here, and other functional groups are embracing technology and even generating their own finance-driven analysis and guidance. Recent Hackett Group research found “finance is lagging a little bit in its adoption of digital technologies,” adding “We saw that only 42% of finance organizations are meeting their management expectations for progress on digital transformation. And only one in five finance organizations has a mature strategy in place and the associated initiatives for digital transformation.”

In other words, data has become more consumable, but finance groups continue to rely primarily on Excel pivot tables, complicated formulas, and ad hoc procedures as their primary analytical tools. For finance groups to stay relevant in the era of increased data availability and quickly advancing technology/analytic methodologies, they must evolve past these simple reporting approaches to value-add, strategic analyses backed (and scaled) by effective technology and processes including automation, visualization, and predictive analytics.

OK, so you’re convinced – what’s next? Here’s a summary of how to use sophisticated tools and techniques to provide greater insight to the business.

As usual, well-managed data is the foundation of success

Effective data management lays the foundation for valuable data analysis: an organization must capture and record its data in a manner that enables creation of the information that is used to build reports and analyses that aligns with organizational objectives. Compared to other functional groups, many finance groups are ahead of the curve on data management because they have already migrated their financial data into a mature ERP environment. If your organization’s data is still residing in spreadsheets and obsolete databases (yes, that Access database needs to be pulled and then sent to the circular file), now is the time to begin gathering, scrubbing, and normalizing.

The finance analytics maturity continuum

Here’s a primer on the broad categories of finance analytics – which spread across a maturity continuum. As you read, think about where your organization lands.

Descriptive analytics summarizes raw data to report historical performance and trends. It is often statistics-based and typically measures and monitors different areas of the business. Common examples include monthly financial results, sales data and revenue, and profitability trends and performance against key performance metrics.

Predictive Analytics identifies trends that point to future outcomes, and drivers of future outcomes, so that organizations can practice proactive decision-making. To unlock the value of predictive analytics, it is important to identify relationships within data sets by aggregating various sources such as ERP, CRM, and HR systems. Capturing these relationships allows an organization to better understand and prepare for future events developments.

Prescriptive Analytics builds on Predictive Analytics. It utilizes complex algorithms and models against many data sets (historical and real-time) to inform decision makers of expected impacts of various choices. Predictive practitioners are steeped in data manipulation and analysis; this field is populated with “data scientist” types.

Descriptive analytics: traditional, but always critical

Descriptive analysis has long been a critical component of organizations’ strategic inputs; naturally, most finance teams tout well-formed processes to support it. A recent Association for Finance Professionals study found that 94% of finance organizations regularly conduct descriptive analyses. But methodologies vary: some teams continue to leverage traditional manual (and arguably time-inefficient) processes with tabular outputs, while others are taking advantage of automated processes with visual outputs.

The value of descriptive analytics is also driven in different ways across organizations. The most useful (and influential) analytics teams provide cross-functional reporting by aggregating data from disparate systems and visualizing it through interactive dashboards using tools such as Tableau. By incorporating data indirectly related to finance such as hours worked and project progress, executives use these self-service reports as a one-stop shop to review KPIs. Teams are also focusing on driver-based planning, which uses business and operational drivers as the basis for financial planning. This mindset is key as teams look to progress into predictive and prescriptive analytics.

Predictive analytics: the differentiator

The predictive analytics software market is rapidly growing; researchers at Global Industry Analysts estimate it could reach $3.6 billion by 2020. Finance organizations create predictive analytics by incorporating and reviewing both internal and external data sources to identify drivers that impact different areas of the business, such as growth opportunities and markets, and forecasting cash flow, cost of goods, and customer demand.

Here’s an example: a manufacturing company’s predictive model uses commodity trading data to identify probable upcoming increases in materials costs. Under the finance team’s direction, the organization prepares for increased costs – perhaps by increasing current materials on hand, or by planning to slow manufacturing of the affected product, or by preemptively slightly increasing prices – and ultimately avoids a hit to its bottom line.

Another model could leverage Forex data to detect the need for an organization to strategically manage cash flow between its global subsidiaries and partners.

Taking advantage of deep analysis driven out of historical data can significantly impact an organization’s finances and operations. And the finance industry is just beginning its journey into deeper analytics.