Finance Analytics: Use Data for Better Business Decisions

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.

• Metric and KPI 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.

According to 56% of senior finance leaders, dashboards are not currently able to support better decision making. Rather than serving as an adaptation of more extensive and in-depth management reports, an effective dashboard communicates key insights concisely and quickly to support finance decision making.

To best support your decision making, align the finance organization’s thinking to what dashboards are meant to do and how to best design them. Start with the following actions:

• Differentiate dashboard capabilities and use cases from 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. Outline your strategic vision for dashboards, which should include the following characteristics:

  • More frequently updated data. Dashboards update automatically (though not in real time), making them much more current than management reports, which only provide a snapshot of backward-looking performance.

  • Narrow use cases. Dashboards serve specific missions, such as tracking performance or explaining the root causes of performance issues, while management reports give a comprehensive view of a company’s financial situation.

  • Broader audience. Both dashboards and management reports inform the decisions of senior leaders, but in practice, dashboards have a wider reach and can be used by anyone who is interested in the dashboard’s specific KPIs.

  • Interactivity. Dashboards are dynamic tools requiring active user engagement, while management reports are static.

  • Unique features. Dashboards offer the ability to get a bird’s-eye view of the data and drill down into specific metrics and their drivers, automated alerts, and data sharing/collaboration features absent from management reports.

• Align the dashboard to decision makers’ thought processes. Require dashboards to center around one specific business question or problem, excluding any metric irrelevant to that question or problem. Once the question or problem is established, require analysts to undergo a decision-mapping process to understand how a user thinks through a given business problem, and adjust dashboard design accordingly.

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.

Three major categories of advanced finance analytics include the following:

• 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.

FP&A teams use larger varieties of data than ever before to:

• Support increasingly scalable decision making.

• Support democratized self-service analytics and reporting tools.

• Develop next-generation extended planning and analysis tools into more cohesive, composable platforms

Yet through 2025, poor data quality will remain one of the most frequently mentioned challenges prohibiting advanced analytics deployment.

Legacy approaches to D&A governance are no longer practical

As AI technology emerges, decision makers need more enriched and real-time analytics. But for many finance teams, data quality is too insufficient to provide decision makers with actionable insights, and enterprise stakeholders often lack the data skills or data literacy to resolve critical business problems.

Proper governance starts by addressing four key challenges:

• Data integrity. Increased automation amplifies the need for methods and solutions that support strong data governance and reliable data. Technical solutions used to prepare, store and secure data quality can help ensure the uniformity, accuracy and integrity of your enterprise data, and can facilitate data democratization.

• Adoption of advanced analytics/AI. The emergence of AI and machine learning (ML) solutions creates a need for new governance frameworks. Solutions like augmented data management, AI governance and D&A governance platforms make it possible to create and enforce consistent governance rules for safe AI building and deployment.

• Decision support. A growing number of decision makers require broader analytical contexts, supported by more granular financial and nonfinancial data. Introducing ungoverned nonfinancial data can create ambiguity. Innovations like semantic search or responsible AI help add context to reduce bias, even if a certain level of bias is present in the data.