Master Data to Sharpen a Competitive Edge
Sooner or later—but probably sooner—many CFOs confront the need to adapt and embed advanced data management capabilities throughout their organizations.
The need to manage data more effectively—for instance, by upgrading from less advanced business intelligence and data warehouse architectures—may become a top priority because of a pressing competitive challenge or a digital transformation initiative. In many cases, the pandemic may have accelerated this trend. In the North American CFO Signals™ survey for the third quarter of 2020, for example, CFOs said that, in response to the turbulent macroeconomic environment, their companies were making strong strategic shifts toward accelerated business digitization and remote/touchless customer interactions as well as focusing on costs and productivity.
Still, as they assess the alignment of the company’s data strategy with its business strategy, CFOs often can’t help but gain a growing sense of just how complex the enterprise’s data ecosystem has grown. That may be the result of accumulated “data debt,” the upshot of too many short-term compromises made with little consideration of how well they would mesh in the long run. The result can show up in data quality, data governance, or a lack of authoritative master data. Then there’s the “data tsunami” that many companies face. Businesses now capture an inordinate variety and volume of data that emanates from various sources, ranging from legacy transaction systems to sensor-collected information, and travels at various speeds. Moreover, within the same company, data is often stored, managed, and processed in different environments, including on-premise data centers and multiple cloud platforms.
In the past, addressing the problems required an ERP-size implementation. But with some of the tools now available, such as machine learning, natural language processing, and intelligent automation, CFOs can have a big impact without committing to an outsize upfront investment, prioritizing their steps based on business value. The primary payoffs of these data efforts can include mitigating data risk, creating direct business value—by leveraging the insights derived from the data to boost revenue, reduce costs, and improve operational efficiency—and enhancing management’s decision-making ability.
A Valuable “Off-Balance Sheet” Asset
Data’s role in the digital economy has often been compared to the historical role of oil, a raw material of unequalled value. But extracting data alone doesn’t constitute a competitive advantage. Companies need to cultivate a shared understanding of the data—examining its structure and isolating its common elements before committing to an overall strategy for centralizing it and enabling the necessary cross-functional access.
CFOs may have the most at stake in ensuring data integrity. From compliance to financial reporting and analytics, a finance leader’s value-add often depends on delivering the right information in the desired format at the ideal time. Reconciling information or data across multiple systems is time-consuming, reducing data flexibility and possibly requiring manual intervention. From an M&A perspective, any data architecture needs to be sufficiently scalable to integrate efficiently with another entity.
Diagnosing a company’s data-borne ills starts by making an assessment of its existing data management maturity. Perhaps members of the finance function want to analyze revenue and profitability by customer rather than simply according to product. Or they’ve wondered why the business can’t easily match up its sales, costs, and customer data. New, advanced enterprise data management tools with machine learning and intelligent automation capabilities can accelerate the ability to provide a unified, standardized architecture for data, uniting silos, improving data integrity, and eradicating inefficiencies and inconsistencies.
Responsibility for improving data management may reside within the domain of the CFO or the COO; either way, input from the CEO and the IT department should also be included. In industries such as financial services, acquiring—or anointing—a chief data officer has become the norm. Such a position can serve to create and oversee a governing body consisting of stakeholders from key functions, fostering a cross-functional perspective.
But CFOs still need to take an active role on issues that touch finance, ensuring there is clear alignment on major outcomes and projected ROI. The participating functions should collaborate in developing a robust road map, setting out a detailed program with milestones that relate to previously determined outcomes. Given the crucial role that data plays in most businesses, revamping or establishing management capabilities will likely have to proceed in phases. As with any companywide change, quick wins can build support, boost momentum, and enable opportunities for self-funding follow-up initiatives.
In some industries, digital technologies have already reshaped certain aspects of how the finance function conducts business—lowering operating costs, effort, and risk while increasing the analytic value and transparency of financial data. The steps companies have taken in the finance function include:
· Financial planning. Some companies have shifted from spreadsheet models—supplemented by intuition—to automated, analytics-based models. They’ve also integrated cloud planning systems with data lakes to help address combined internal and external data needs. In addition, they’ve used technology to ensure consistent data categories and federated aggregation processes from the corporate core.
· Finance operations. Companies have created hierarchies that can handle evolving management, financial, and regulatory reporting. They’ve also streamlined workflows and automated reconciliations across sources to increase journal entry traceability and audit responsiveness. In addition, they’ve leveraged advanced analytics using machine learning for exception and risk identification.
· Decision support. Businesses have clarified information needs across business units, geographies, and source systems. Management has also unlocked insights using a big data or cloud-based data-staging environment so data is accessible anywhere it resides. In addition, they’ve created interactive reports that let users drill down through multiple layers of information.
When implementing any such data initiatives—including new tools, policies, and procedures—CFOs need to pay close attention to organizational change management. Management’s strategic vision needs to translate into tactical priorities, with everyone understanding how leveraging insights will improve both the customer and employee experience. Otherwise, the pushback from those who have to implement the changes can slow the speed of progress, suboptimizing the expected business benefits. Those who may have lost trust in the data, and therefore have begun to use it less, may need to be persuaded to reconsider.