Finance Analytics: Mature Data Management and Reporting Capabilities

Delivering new, digital finance analytics capabilities requires a clear way to align data and analytics (D&A) investments with business outcomes. Driver mapping can fulfill this need by mapping financial planning and analysis (FP&A) activities to decision makers’ information needs.

Driver maps serve a wide range of organization types, from complex enterprises, government and not-for-profit organizations, to business units, operating regions, product lines and projects. 

Driver maps serve multiple purposes, including:

• D&A strategy. A driver map visualizes the ways your business creates and delivers financial or strategic value, identifying high-value analytics projects that should be prioritized in your finance analytics strategy. Some teams have used driver maps to source feedback on analytics model design or to educate business leaders to make more financially informed decisions.

• KPI and metric selection. Though every KPI is a metric, not every metric is a KPI. Use your team’s strategic fluency and familiarity with currently reported metrics to develop a working draft of KPIs, and share it with outcome owners and decision makers. This approach eases the challenges of engaging leaders as co-creators in the process and minimizes the risk of having too many metrics.

The realities of the data provisioning process should also influence metric selection. Driver mapping helps by separating the conversation of “what needs to be measured” from “how that should be measured.” This allows the inclusion of KPIs and metrics that involve factors beyond relevance alone, such as data quality, latency, effort and timing.

Dashboards often don’t deliver on their promise of communicating insights concisely and quickly to support business decision making. Senior finance leaders recognize that current dashboards for financial analytics do not support better decision making. When dashboards fail to reach their full potential, it is because they are bloated, hard to navigate and overwhelm users with information. For effective design of dashboards, finance leaders should follow three key principles:

Differentiate dashboard capabilities and use cases from traditional management reporting.

A dashboard visually tracks, analyzes and displays key performance indicators (KPIs), metrics and key data points to monitor the health of a business, department or process.

Dashboards are different from traditional management reports as they are frequently updated, serve a broader audience on specific situations and allow user engagement with data. FP&A leaders should supplement these distinctions with concrete guidance to their analysts for determining when to use dashboards and when to use management reports. Screening criteria could include situations such as: analysis performed in response to a one-time request (management report), or time-sensitive data that requires frequent updating (dashboard).

Increasing data volumes, business complexities and growing uncertainties require a quick shift from manual to advanced and augmented analytics. This means carefully designing processes to fully leverage digital capabilities and safeguard the integrity and quality of machine outputs.

Predictive and prescriptive analytics are key to this evolution. These AI- and ML-based analytics are growing areas of investment in finance. Each requires different levels of technical sophistication, staff skills, organizational structures and FP&A maturity.

AI- and ML-driven advancements take finance analytics to the next level.

There are four major categories of advanced finance analytics:

• Descriptive analytics reports on overall and departmental performance by answering, "What happened?" It analyzes historical financial data to uncover trends and insights, helping organizations understand past performance, monitor financial health, and make informed decisions.

• Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question: Why did it happen? It is characterized by techniques such as drill-down, data discovery, data mining and correlations.

• Predictive analytics is a form of advanced analytics that examines data or content to answer the question: What will happen or, more precisely, what is likely to happen? It is characterized by techniques such as regression analysis, forecasting, multivariate statistics, pattern matching and predictive modeling. Predictive analytics has already been deployed in most organizations and can be executed by existing staff with existing technology.

• Prescriptive analytics is a form of advanced analytics that examines data or content to answer the question: What should be done, or what can we do to make x happen? It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics and machine learning. To deploy, prescriptive analytics typically requires additional resources, including staff, technical training and new technology investments.