The CFO’s guide to data management


A new report by Deloitte offers tips for how to use advanced tools to solve data problems when systems don’t talk to one another, without large-scale investment.

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Data management tools and techniques are evolving rapidly—and they are designed to help finance leaders solve thorny data issues in a matter of months, not years. While there are no silver bullets, a new report from Deloitte discusses ways leaders may be able to apply digital finance capabilities in less time than they might have thought possible.

“New technologies using machine learning, natural language processing, and advanced analytics can help finance leaders fix or work around many data problems without the need for large-scale investment and company-wide upheaval,” Deloitte said. In fact, such technologies are already being used to help improve corporate-level forecasting, automate reconciliations, streamline reporting, and generate customer and financial insights, according to the firm.

Why are CFOs getting involved in data management? “Business decisions based on insights derived from data are now critical to organizational performance and are becoming an essential part of a company’s DNA,” explained Victor Bocking, managing director, Deloitte Consulting LLP, in a statement. “CFOs and other C-level executives are getting more directly involved, partnering with their CIOs and CDOs [chief data officer] in leading the data initiatives for the parts of the business they are responsible for.”

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As companies generate more and more data each day, finance teams have seemingly limitless opportunities to glean new insights and boost their value to the business. But doing that is easier said than done, the firm noted. The problem is the amount of data emanating daily from various sources can be overwhelming. Deloitte’s Finance 2025 series calls this “the data tsunami.” To manage it, businesses need a practical way to collect, process, and act upon reams of information.

These technologies can also help reduce the cost, effort, and risk associated with digital finance transformation. So, if data quality is a problem—and finance leaders are tired of hearing “the systems don’t talk to each other,” Deloitte recommends exploring new options.

Transitioning to a cloud-based ERP is one way to tackle the problem. But many data challenges can be addressed throughout the enterprise with simpler or more targeted solutions.

Getting a handle on data

Digital technologies are helping to reshape how finance does business—lowering operating costs, effort, and risk while increasing the analytic value and transparency of financial data. Deloitte laid out some of the ways finance teams are using these technologies to tackle data challenges.

1. Financial planning

  • Shift from spreadsheet models and intuition to automated, analytic-based models

  • Integrate cloud planning systems with data lakes to address combined internal and external data needs

  • Ensure consistent data categories and federated aggregation processes from the corporate core 

2. Finance operations

  • Create hierarchies that can handle evolving management, financial, and regulatory reporting

  • Streamline workflows and automate reconciliations across sources to increase journal entry traceability and audit responsiveness

  • Leverage advanced analytics using machine learning for exception and risk identification

3. Decision support

  • Clarify data definitions across business units, geographies, and source systems
  • Unlock insights using a big data or cloud-based data-staging environment so data is accessible anywhere it resides, including the ERP 

  • Create interactive reports that let users drill down through multiple layers of information

Leveraging technology in finance transformation

Advances in digital technology offer CFOs new options in data management—particularly when an organization’s current systems are not on speaking terms. “Cloud-based architecture can organize and reassemble data on the fly,” Deloitte said. “Advanced analytics tools let you draw conclusions from data points spanning multiple platforms. Machine learning and AI can apply controls and monitor risks—enabling course corrections in real time.”

If leaders want to improve the quality of their data and boost finance’s core capabilities, Deloitte advises starting with capabilities in existing systems, with an eye toward eventually automating and enhancing how data is developed, delivered, and consumed.

The firm offers six steps finance leaders can take:

1.       Decide what insights you need to run the business. 

2.       Consider the tools available to collect, manipulate, analyze, and deliver necessary information. 

3.      Align your leadership team. 

4.      Build a data ecosystem, working toward enabling automated data feeds, data set integration, true self-service capabilities, and new tools for insight-driven decision-making.

5.      Equip your workforce. Consider ways to build or buy the talent you’ll need. (For more on this topic, see Crunch time: The finance workforce in a digital world.)

6.      If it’s feasible, test different approaches in different markets. This will let you compare results to gauge what’s best for the company long term. 

For any size business, achieving desired outcomes starts with good data, Deloitte said. And new tools using artificial intelligence, machine learning, natural language processing, robotic process automation, and other emerging technologies can automate data management and improve data quality—better and faster than ever before.

“You don’t need to spend a fortune to reap the benefits, and you don’t need to tie up your resources for years,” the firm said. “Instead, set your priorities, explore your options, and take small steps you can build on over time. With a little poking around, you might be surprised at what’s possible.”

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