By itself, data isn’t useful for business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competence of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, Finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights. Analysis thus should include, for example, data from the systems that manage sales operations, human resources and field service and that data must be available to all departments and applications that need it.
By being applied to diverse sets of data, analytics can provide richer performance measures that offer executives and managers deep insights into not only how well the business is doing but why. Such analysis also must give them the ability to look forward, providing more accurate forecasts and early alerts to enable decision-makers to address issues and opportunities sooner and receive better guidance on what to do next.
To better understand these challenges we have our Finance Analytics Dynamic Insights research. The research will explore organizations’ experiences with finance analytics initiatives and their attempts to align IT projects, resources and spending with new business objectives. Using concise web-based surveys, the Ventana Research Dynamic Insights platform gathers real-world data while immediately providing research participants with a personalized assessment of their organization’s efforts as well as research- and experience-based advice on potential next steps to improve. Each participant who completes the survey is provided insights to support decisions ranging from prioritizing application and technology investments to what best practices are most relevant to the organization’s efforts.
Data issues often prevent finance organizations from using analytics as effectively as they could. The inability to access needed data and excessive time spent in preparing data constrain the capabilities and limit the productivity of analysts. Our previous research on this topic shows that it’s worth the trouble to ensure that finance analysts have access to robust, complete data. Nearly all (92%) companies that said they have very accurate data also said they have a process for creating finance analytics that works well or very well. Conversely, only 22 percent of companies that said their data is somewhat accurate have such a process.
Another issue is that finance departments today need to use more than accounting data to support management decisions because accounting data by itself is insufficient to assess company performance and guide business decisions. Combining financial and operational data gives managers and executives deeper and more complete understanding of performance drivers. It provides them with more realistic analyses to guide their business decisions.
For example, combining information from CRM systems with financial data can lead to better understanding of customer profitability and costs to serve. Likewise, understanding all the factors driving costs related to a project can improve project management and support better-informed pricing of resulting products. Being able to analyze inputs and outputs in terms of units of things — such as labor hours, board feet of lumber or the number of full truckloads — separately from prices and costs makes performance measurement and management more accurate and actionable.
However, our previous research found that although almost all finance organizations use financial data in their analytics, fewer said they include customer and product information or data about manufacturing and suppliers. This research will further explore the various data sources organizations are using to develop effective finance analytics.
In addition, finance departments must be able to easily organize financial and operational data so they can drill down into details based on the relevant characteristics of the business, not just the chart of accounts. Executives and managers need to examine results from multiple perspectives, such as by some combination of business unit, department, product type, sales territory, customer and sales channel. They also need to be able to “drill around” data to explore other views of related information, perhaps in other applications, to improve understanding of business conditions or factors that may be influencing results.
Finance organizations also need to be able to quickly create and revise reports and dashboards to communicate their analyses to those who need the information. Companies can realize fully the benefits of finance analytics only if the analyses are communicated in a timely fashion. Business is fluid, so it’s important to have tools that make it easy for analysts to adapt to changing requirements.
Finance organizations do a good job of delivering basic, inwardly focused financial analyses to executives and managers, but they can do more. Their analytics must provide deeper insight into the operating performance of the company, incorporate more external data and provide information as immediately as possible. And information technology is essential to delivering better finance analytics. Companies must use the right software and have the right data available to be able to improve the scope, quality, business impact and timeliness of their analytics. This research will assess these aspects of finance analytics, providing real-time advice to those who take the survey as well as insights and best practices to the Ventana Research community.
We’re interested hearing about your experience with finance analytics. Click here to participate in this research, and here to learn more about Ventana Research’s methodology and large body of business research. Ventana Research also has conducted research in related areas including Data Preparation, Office of Finance, Business Planning, Big Data for Business, Machine Learning, Data and Analytics in the Cloud, Next-Generation Predictive Analytics and Big Data Analytics and Integration.
SVP & Research Director