Today’s CFOs have an eye on more than just the bottom line. They are watching the horizon, predicting what’s coming, and charting the course ahead.
As your organization’s finance leader, the opportunity to better understand the landscape and your business has never been greater. Advances in analytics—powered by digital technologies, such as automation and machine learning—give finance teams deeper business insights and the ability to identify performance issues, predict scenarios, and change outcomes.
It also means finance teams no longer have to look backward in time for answers. Advanced analytics can help them look forward and better forecast the future with predictions, such as which products and customers will be most profitable, or which customers will most likely pay their invoices on time.
At the same time, technology enables the automation of more financial processes, from accounting to auditing, freeing up finance teams to focus more on analysis and partnering with the business.
But despite the value, finance teams remain challenged with putting data and analytics into use. In the global Workday survey “Finance Redefined,” results showed that only 35 percent of respondents are making extensive use of advanced analytics in key areas such as planning, budgeting, and forecasting.
What are the difficulties? The reasons cited by finance leaders are often the same: disconnected systems and data, too much time spent on transactional work, issues with business partnerships, and a lack of talent. At a recent industry event, Matt Schwenderman, principal at Deloitte Consulting LLP, highlighted two key issues finance faces when it comes to analytics: technology and talent. “Analytics is a key way that finance supports a more digital operating model, but we don’t necessarily have the technologies and the talent aligned with that,” he says.
CFOs realize they must begin addressing these challenges now. Without the use of analytics, companies run the risk of making the wrong decisions, ultimately stalling growth and impacting performance.
Based on various research studies and interviews with finance leaders, we identified three key areas that are critical for advancing analytics within the finance function: a core technology foundation, strategic business partnerships, and leadership.
Analytics and machine learning will only get more sophisticated. With the right systems, partnering model, and leadership, finance can be the catalyst for their organizations to embrace these new capabilities.
#1: A Core Technology Foundation
While many finance organizations aspire to advance their analytics, most are still focused on getting the technology foundation right. According to Schwenderman, some are having more success than others. “We have some organizations that are doing incredibly creative and avant garde things with data and insight-driven decisions,” he says. “Others are still relying on what I refer to as human middleware: moving spreadsheet-driven information throughout the organization and walking into very important meetings with varying sets of the same results, arguing about what’s the right number.”
Finance teams often work with data that is spread across disparate systems, with different data definitions. There is no sole source of financial truth to work from, making it difficult to trust the accuracy of data and analyze it for insights. In fact, system inefficiency was cited by finance leaders as the second-highest barrier standing in the way of developing data-driven business insights in the Workday “Finance Redefined” survey.
Jim Kendall, vice president of Finance Solutions at Aon, one of the world’s leading professional services firms, describes how managing disparate finance systems in locations across the globe impacted Aon’s ability to analyze the business. The company faced the same challenges with HR systems as well. “As we’ve grown through acquisitions, the diversity of our systems and processes became a real issue for us,” he says. “It was difficult for leadership to have a global view of our people and financial results—we didn’t have a single source of analytics across finance and HR.”
How can system inefficiency impact performance? Consider global companies that sell the same products and services in multiple countries. They are using different financial systems and applying different data definitions to activities in each region. As a result, each location may be interpreting and reporting on the performance of the same product and service lines differently than required by the corporate office. This can lead to faulty analysis—for example, parts of the business appearing more profitable than they actually are—and ultimately impact decision-making.
Working among multiple systems also makes it difficult for finance teams to focus on analysis because they are spending more time gathering and reconciling data. Robynne Sisco, co-president and CFO at Workday, saw this firsthand in previous organizations where she worked. “Each month finance would have to close the period, access the data, reconcile it, format it, and analyze it,” she says. “By the time we delivered the numbers to the business, it was two weeks after the period ended and too late to take action.”
Finance must address these system challenges if it wants to advance its analytics. In “Advanced Analytics and the CFO,” a Harvard Business Review Analytic Services whitepaper sponsored by KPMG, R. “Ray” Wang, principal analyst and founder of Constellation Research, emphasized the importance of addressing these barriers. “Companies must embark on this process and systems improvement journey in order to lay the necessary foundation for advanced analytics,” he says.
Wang suggests several ways to begin this process. “Standardizing data definitions, integrating and rationalizing core financial systems, and leveraging the cloud for scalability, standardization, and coordination among systems are key first steps in that journey,” he says.
Many organizations are moving financial management off legacy finance systems to a cloud-based global system, enabling finance teams to standardize processes across their organizations and bring all financial data into a single system. Cloud-based finance systems that have analytics built into the application give finance the ability to transact, analyze, and report on real-time data all from the same place—capabilities not possible with traditional systems.
Having this foundation gives finance the access and confidence to use data effectively. According to Schwenderman, when it comes to analytics, “...the first thing is having credibility in what you are producing. If you have that credibility, you can progress faster and forward more quickly.”
A Single Cloud-Based System for Finance
Kendall describes the benefits of moving to a global cloud-based system for finance and HR at Aon. “Having a single system for finance and HR has improved our analytics capabilities, enabling us to take action directly from reports,” he says. “Our colleagues around the world have access to real-time data that allows them to better understand how the business is running and how their actions impact profitability and expenses. We can drill into the variances that matter, understand why, and take actions all in one system.”
“Our colleagues around the world have access to real-time data that allows them to better understand how the business is running and how their actions impact profitability and expenses.”
Kainos Software Limited, one of the longest-standing independent digital technology companies headquartered in the UK, also moved off legacy systems to a single system for finance and HR to support the company’s rapid growth and strategic goals. Peter McKeown, group head of Finance at Kainos, says the change has had a significant impact on how finance works with data. "Having finance and HCM on one platform ensures one source of the truth for all HR and financial information, increases the buy-in across the business, and reduces the number of errors or reconciliations required,” says McKeown.
He also says his team is now focused on more strategic work. “Implementing the single cloud-based system helps me empower my senior staff to spend less time in the weeds or on finance processing, allowing them focus more time on value-add activities."
As finance teams work to better leverage the data they have, many are also thinking about how to leverage technologies such as artificial intelligence and integrate external data to improve analytics. Predictive analytics can be used to help finance teams evaluate patterns within different types of data and then identify risks, such as anomalies that might indicate fraud.
Bringing external data—such as CRM or point-of-sale, or data from industry-specific systems—into the finance system of record can help leaders better understand performance, such as the operational drivers behind revenue and expenses. Schwenderman describes why this is important, saying, “The more we can add those external data sets, the more precise our predictive models can be. Then I can make better resource allocations as a CFO. I can provide better guidance to the street and generate greater shareholder value.”
Sisco underscores the importance of having the right technology foundation. “As companies look to advance their analytics—by leveraging technologies such as machine learning and bringing in more operational data—having a single version of the truth becomes even more important,” she says.
But many finance organizations still have a ways to go when it comes to making these advances. In the Workday “Finance Redefined” survey, finance leaders cited the ability to integrate finance and non-finance data for deeper insights as the number one barrier to greater analytics.
Having the right core technology foundation to work from will be paramount. The Harvard Business Review Analytic Services whitepaper affirms this, stating, “Machine learning and artificial intelligence (AI) will shift the focus from operational efficiency to enhanced data and insights, which can deliver a quantum leap in performance. CFOs need to ensure they have baseline digital capabilities—specifically around data and processes—to capitalize on these future investments.”
#2: Strategic Business Partnerships
It’s not enough to just have data—finance teams must also know how to use it to deliver relevant insights and guidance across the business. As technology continues to augment traditional tasks, finance teams will continue to shift more into a business partner role, requiring new skills and ways of working. According to Deloitte’s “Finance 2025: Digital Transformation in Finance” report, “Accountants using spreadsheets will be replaced by technology that does 90 percent of the work without human intervention.” This will enable finance to focus more on higher-value work, which “requires cross-functional collaboration among business people, technology teams, and finance strategists.”
Historically, finance has faced challenges in delivering the value they want to business partners. Different areas of the business often work in silos, and finance doesn’t have the capabilities or structure to bridge the gaps. Finance teams tend to spend more time on gathering data and reconciling numbers with partners than on strategy and planning.
Sisco describes the issues she has experienced in past organizations. “When it comes to business partnerships, what often happens is that finance, HR, and other managers come together in a room, each with different data, such as varying head-count numbers,”she says. “People spend most of the time debating what numbers are right versus talking about what the numbers really mean.” The key, she explains, is trust in the data. “Having the same data changes the conversation and the trust you have in the decisions you make.”
There are several ways that finance teams can lay the groundwork for effective business partnerships:
Treat business partners like customers. According to the Harvard Business Review Analytic Services whitepaper this mentality is central no matter which business partnership model you use. “While there is no one-size-fits-all business partnering model, there is one element that all hold in common: a customer-centric approach to internal customers that helps drive real value for the business and boosts the bottom line.”
Develop analytical skill sets within finance—which may include hiring new talent. According to the Harvard Business Review Analytic Services whitepaper, “As useful and necessary as it may be to boost the analytical know-how of current staff, it is also clear that acquiring and fostering analytical talent must continue to be a top enterprise objective.” The whitepaper also describes a broad range of skills needed to support analytics in finance. “Demand for talent certainly includes those specializing in analytics tools, methods, and technology, but it will also extend to those with the critical thinking skills to ask insightful questions, interpret data, and draw sound conclusions.”
Schwenderman also emphasizes the need for analytical skills, including data scientists. “For finance specifically, one of the biggest gaps is really the difference between the doer roles and the insight roles,” he says. “Many organizations have folks in all of those functions, but at a disproportionate volume to the doer roles. We’re seeing clients, controllers, and CFOs start to look for and actually bring data scientists into their organization.”
Provide self-service analytics to the organization. According to the “Finance Redefined” survey, only one-quarter of finance teams are broadly making self-service data available to business leaders—a missed opportunity for CFOs to add significant value to the organization. Giving business partners direct access to relevant data and analytics empowers them to access the information they need to make decisions and better understand how those choices impact performance.
Giving business partners direct access to relevant data and analytics empowers them to access the information they need to make decisions and better understand how those choices impact performance.
To effectively deliver self-service analytics to business leaders, two things are important: managers need easy access to data and absolute trust in it. Cloud-based finance systems have made this possible by providing real-time data accessible from anywhere at any time, and a single source of truth.
It’s also important to ensure that data governance and security controls are in place when financial data becomes democratized across an organization. Analyses are most useful when they are tailored, such as providing store managers with dashboards populated only for their specific stores or regions.
Sisco describes how her finance team is supporting managers with real-time data and analytics. “Our finance team has created dashboards in the system that are accessible by leaders. At any time, managers can look at their organization and see budget versus forecast versus actuals for head count and spend and make in-the-moment decisions that will drive the results within the current period.”
#3: Leadership
As demand grows for greater analytics, many CFOs are questioning the vision and path for their organization. This extends not only to finance, but enterprise-wide. In the article “How Analytics Can Help Transform CFOs from Accountants to Strategists,” Chris Mazzei, global chief analytics officer at EY states, “There’s no doubt that CFOs need to be a champion and driver for the use of analytics in all current core financial processes under his or her remit today,” he says. “But you can start to extend out from that. Financial data, as well as other data, is a key input to many other business decision processes, whether it’s procurement, supply chain, operational-type decisions, or risk management-type decisions.”
What should CFOs consider as they develop their analytics vision?
Begin with the greater business challenges in mind. According to Deloitte’s “Finance Analytics: Three-Minute Guide to Advanced Analytics” report, start by identifying the critical business problems that need to be solved and then work backward to see how finance analytics can help. “This may reveal problems you don’t even know you have—as well as potential new sources of valuable information that aren’t currently being tapped,” the report states.
Focus on the most important KPIs for your organization, which will help drive better results than analyzing loads of data. “You can run models and analysis on any set of information and many organizations will do it ad nauseum,” says Schwenderman. “There’s only a few sets of key performance indicators that really drive the performance of any organization. Create a good way to get at that data and use current technologies to be more predictive—learning off of that will drive greater results than trying to produce large masses of data that ends up sitting on a shelf.”
Involve business leaders in the analytics vision from the beginning. This helps set up finance up for success. According to the Harvard Business Review Analytic Services whitepaper, sponsorship at the leadership level is critical, and business leaders should be involved in prioritizing where to focus advanced analytics. The whitepaper states, “It is not good enough to put charts and tables in front of your leaders at the end of an effort. Rather, they need to provide input into the effort, own it, and go along for the journey. Having leadership’s commitment puts the CFO in a better position to get actionable conclusions.”
One way to lead these conversations is to explore with business leaders what data they need to support their decision-making. Talk about specific self-service KPIs they’d want or the types of predictions they need to make. Engaging business leaders in the analytics discussion also makes it easier for finance to access data being held in other systems across the organization.
Assess if you have the technology to support your goals. Partner with the CIO to evaluate technology investments that can address the barriers to your analytics goals—such as improving data quality and access to data—and that will support the ability to advance analytics in the future. According to the Harvard Business Review Analytic Services whitepaper, “Once business leaders have established the strategic analytics vision for the company, finance is in a strong position to not only evaluate business cases for investment in data infrastructure, baseline automation, and analytics systems, but also to recognize and initiate strategic infrastructure investments.”
Schwenderman offers some advice for taking a first step to advancing analytics, saying, “Pilot capabilities around some analytics and predictive modeling. Plan, pilot, and fail fast—get out there, start doing it, see the results and have a feedback loop.”
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Each area described in this article—technology foundation, business partnerships, and leadership—is necessary for CFOs to be able to move their organizations forward on the analytics journey. Schwenderman emphasizes that the capabilities are already here to support the evolving role of finance.
“What we're seeing is the ability of technology—and the cost to implement and maintain and keep those technologies current—to make what I call the 'no excuses time' for finance,” he says. “You can't tell me it takes too long or that you can't bring in that information and manage it and govern it, because those capabilities are here. The only thing holding finance back is embracing that role and setting up its organizational agenda and talent.”
The data opportunity for finance is here. Learn more about how Workday can help advance analytics for your finance function.