Imagine if your organization could analyze financial and non-financial data to uncover the next market opportunity, hire the best talent, or model hundreds of potential scenarios.
Most organizations are sitting on a treasure trove of data, yet they struggle to find the time and hire the right people to analyze it. In a recent survey of 700+ financial professionals conducted by the Association of International Certified Professional Accountants, more than half who work at large organizations said they spend more time collecting data than analyzing it for insight.
In the same report, Agile Finance Unleashed: The Key Traits of Digital Finance Leaders, researchers found that businesses with positive year-on-year revenue growth are more likely to be using artificial intelligence (AI) than businesses with flat or declining revenue.
The growth group uses advanced technologies like AI, machine learning, predictive analytics and more to discover value in data. They are building digital intelligence, one of three dimensions of agile finance that leaders have mastered. (Read our first article in this series on Operational Excellence.)
When finance teams have digital intelligence, they have the tools and skills they need to find strategic value in data. And, they can deliver these insights quickly so that executives and business unit leaders don’t have to wait for reports in order to make strategic decisions.
Reimagining the way people work is the focus of a CEO-led initiative at Highmark Health designed to completely transform the employee experience at the second-largest integrated healthcare delivery and financing system (IDFS) in America. As part of this corporate initiative, the financial executive team is creating a new operating model for its department, supported by a move to cloud ERP and EPM. “To start, we are rolling out data visualization tools, advanced analytics, and process automation initiatives,” notes Janine Colinear, senior vice president of finance at Highmark Health. “Utilizing these tools in a cloud-based environment will support our goals of reducing systems complexity, consolidating operations in our shared services model, and becoming more metrics-driven.
Highmark Health's Colinear says the importance of Digital Intelligence helped drive her organization's choice to move planning and budgeting to the cloud. “All of our budgeting and forecasting processes were primarily Excel-based,” she explains. “It limits your ability to drill down into the data, so that's what drove our data aggregation effort. I think that the movement to a planning and budgeting cloud is certainly giving us better transparency into the data. There's also some predictive analytics inherent in this tool that we're excited about using.”
Survey respondents at large organizations see the CFO and finance teams as top enablers of digital intelligence, with 60 percent saying that the CFO should lead the enterprise approach to data governance. About half also believe that finance teams should use their expertise to advise other parts of the business on advanced analytics.
This makes sense considering the traditional governance role of finance and the mushrooming potential of data to produce business value. Just as the finance function protects and optimizes physical and monetary assets, so too should it protect and optimize digital assets. There’s a need for strong finance leaders to step into this role, according to the survey findings:
To overcome these challenges and start building digital intelligence, CFOs should focus on two key areas: upgrading finance’s intelligence technologies and building analytics skills and people capability.
Intelligent technologies are in the cloud. On-premises applications and other legacy technologies were not designed to take in the vast amounts of data that businesses collect today, let alone turn that data into insight. The cloud provides the storage to consolidate disparate sets of data and the computing power to drill down and across to analyze the data.
Cloud applications provide businesses with a way to use AI without investing in infrastructure and training algorithms. Indeed, many companies now look to cloud-based ERP and EPM applications to access capabilities such as machine learning to identify data patterns, speed up analysis, and automate project tasks; data visualization to instantly gauge global sales and profitability; predictive analytics to better predict revenue, costs, and overall business performance; and in-memory computing to shorten the time needed to develop plans, budgets, and reports—as well as update what-if scenario models.
The other area to focus on is getting people ready to use all of these exciting capabilities. Less than half of the surveyed finance professionals felt that their teams were highly effective at key skills needed for digital intelligence, such as creating predictive analytics and integrating financial and non-financial data. And a mere 10 percent were confident their teams had the skills to support the organization’s digital future.
A common-sense way to approach this problem is to conduct a skills audit. In some cases, training existing staff will make sense, but leaders should also be open to hiring—bringing in fresh skills that fill gaps and creating new, nontraditional finance roles that support a more agile model.
The overarching takeaway of the Agile Finance research is that a wide gap is forming between organizations with agile, transformed finance functions and those without. Digital intelligence is one thing that separates the digital finance leaders from their peers.