The ability to use data to make better decisions is a huge competitive advantage for businesses. But while the amount of data is growing, getting the right insights at the right time is a constant challenge for many organizations. In fact, in PwC’s 2016 Global Data and Analytics Survey, executives reported that they want to be more data-driven, however two-thirds said their own companies’ decision-making is only somewhat or rarely data-driven.
Finance teams are increasingly being asked to lead this charge, and deliver data and insights to stakeholders across the business—from marketing and sales to product and operations—that can help inform strategy and provide context on the bottom-line impact of decisions.
To do this successfully, two things are needed: easy access to current financial data and the ability to bring different data sources together for multi-dimensional reporting and analytics. And, non-financial data—especially workforce information, often an organization’s largest area of spend—can provide context to the numbers and deeper insight into the reasons behind performance.
With these capabilities, organizations can more effectively make decisions and plan for their futures. For example, a fast food restaurant chain wanting to better understand the drivers of performance or causes behind problems could analyze profitability by product lines, service lines, locations, or district managers. Armed with these insights, restaurant leaders could take action and course correct in the moment things are happening. Or, consider a company planning for growth—in evaluating expansion into a new market, company leaders can quickly assess the profit and loss impact, making it easier and faster to reach its goals.
Yet finance teams often struggle to deliver these levels of insight. The culprit: fragmented and inadequate finance systems that make it difficult or impossible to get the data they need, when they need it, so that information is relevant and useful to operational managers and leadership.
One of the main problems with traditional accounting systems is that they are structured entirely around a general ledger code block—the central structure that defines the chart of accounts and drives accounting and financial reporting. The code block is configured in these legacy systems during the system implementation, and can’t be changed once configured. While it provides enough fields to support accounting and financial reporting, it cannot support additional attributes needed for management reporting or multi-dimensional views of the business.
So when a transaction occurs in a traditional or legacy system, it goes through a linear batch process that progressively summarizes detailed transactions down to the accounting code block, stripping away the richness of the transactional data.
For example, by the time a purchase order has posted to the general ledger, it will no longer have any supplier information. Or, an employee expense report will end up being classified by expense type and department, and the employee information is no longer associated with it.
To get to these insights, finance teams often have to pull data from multiple systems and merge it into a data warehouse or separate datamart for reporting, or collect it manually in spreadsheets for analysis. At this point the data is stale and business and market conditions may have already changed.
When we set out to build Workday, we wanted to solve this fundamental issue of data latency and limited dimensionality, and this started by changing the way data is modeled in a system—at the foundation. Our approach was to bring together financial management, human capital management and analytics on one architecture so that there’s no difference between reporting and transactional data—there’s just data and real-time insights into the business.
Redstone Federal Credit Union found that the percentage of time its finance team spent on the preparation of data versus analysis significantly shifted from 70/30 to 30/70.
All of the data is stored in-memory, which means finance can transact, analyze, and report on real-time data in the same place, without ever pulling it out of the system. We also created a way for customers to add dimensionality to business events using what we call Worktags, which are customer-defined metadata tags. Worktags let customers assign meaningful attributes for multi-dimensional reporting to any event, such as an expense report, purchase order, or payroll transaction. So when creating a purchase order, customers enable the tags that are associated with the transaction and that they want visibility into, such as the department, supplier, project, or employee.
With this foundation, finance is able to combine data—such as actuals, budgets, statistics, and headcount—into Workday Composite Reports, which are live multi-dimensional reports that stakeholders can drill down into for details on things such as spend and profitability, and then take action on those findings all within the same system. This foundation also enables a close level of collaboration between finance and the business, and gives business managers self-service access to relevant data, such as running a profit and loss statement for their division.
Our customers are seeing a major impact to their businesses with this technology approach. As one example, Redstone Federal Credit Union found that the percentage of time its finance team spent on the preparation of data versus analysis significantly shifted from 70/30 to 30/70.
Wayne Sisco, who is vice president and controller at Redstone Credit Union, said that “Being on one unified system is huge for our business. It’s allowed us to move from a transactional to a more strategic focus, improve visibility throughout the business, simplify and access more effective reports, and drive a needed shift in company culture.”
One of our higher education customers—a top tier research university—dramatically improved visibility into departmental spending, resulting in $4 million in savings from improved cost center accountability.
While many vendors today offer solutions claiming real-time or multi-dimensional reporting, at the foundation, these systems still can’t deliver the speed, easy access, and dimensionality of data. These solutions still require time and resources to move data between systems, resulting in stale data being used for analysis and reporting.
Looking ahead, the technology foundation of a finance system will become even more important as companies seek to leverage more external and internal data sources for insights. Recently we announced Workday Prism Analytics, with the initial release to be delivered in the fall of this year. This offering will provide customers with the ability to blend and analyze both Workday and non-Workday data for deeper people, financial, and operational analytics. Non-Workday data could include data sources such as customer-relationship management, point of sale, or industry-specific systems. For example, an insurance company wanting greater insight into product-level profitability could combine policy and claims data from their insurance systems with finance and workforce data in Workday.
My next blog in this series will look at planning, and how finance can shift from explaining the past to forward looking decision making, and why the foundation of a system matters to supporting speed, insights, and the ability to collaborate.
(Read the first three blogs in this series: “Finance and the Tech Foundation: Making Sense of Enterprise Tech Concepts, Part 1”, “Finance and the Tech Foundation: Making Sense of Enterprise Tech Concepts, Part 2,” and “Finance and the Tech Foundation: Real-time Consolidation and a Shorter Financial Close.”)