Why Financial Data Management Must Be a Priority for CFOs

Reviewing company finances, coordinating between teams, securing the right talent and aligning compliance policies with regulatory requirements. With so much to do, financial data management can sometimes slip through the cracks.

It’s not so much that CFOs don’t see how the right data can improve their role and the health of the business. It’s because they have so much on their plate that it can be difficult to make financial data management a priority.

According to Deloitte, only 10% of CFOs ranked AI and business analytics among their top three priorities for 2023. But financial data management (FDM) is crucial to the future growth of a business. In this article, I’ll discuss why it’s important and how CFOs can improve their organization’s financial data management. 

Financial data management involves going beyond the basics of cash flow forecasting and the reporting of balance sheets, income statements and equity changes. It’s a set of processes that help stakeholders have access to a single source of financial truth. A source that’s always updated and accurate so businesses can achieve greater financial results.

Financial data management is important because there’s a heck of a lot of data being regularly processed in companies. And 40% of it is bad, in the sense that it’s incomplete or inaccurate. If that bad data gets out of control, then it’ll come back to bite you. Uber discovered  this problem in 2017, when an accounting error cost them potentially millions of dollars in revenue. 

Adding to that, you can’t rely on legacy ERP systems to process information that it was never built to process in the first place. It’s not a sustainable way to manage financial data in the long term. So, having a process that favors better software, better invoice documentation and better data storage is the bedrock of strong financial data management.

I wanted to get to the heart of what CFOs can do to instil stronger financial data management into their organizations. To do that, I interviewed David Walters, Chief Data Officer at Creditsafe, to get his thoughts. As someone whose job is centered around financial data management, David knows all too well how it can impact financial planning and analysis.

Here are eight best practices he has learned throughout his career and recommends other CFOs should follow.

1. Businesses should maintain a well-structured system for organizing and categorizing financial data. Use consistent naming conventions, create relevant folders or categories and establish a clear hierarchy for easy retrieval and analysis.
2. Protect sensitive financial data by implementing strong security measures. Use firewalls, encryption and secure access controls to prevent unauthorized access. Regularly update software and systems to guard against potential vulnerabilities.
3. Establish a regular backup schedule so that financial data is securely copied and stored in case of data loss or system failure. Have a disaster recovery plan in place to quickly restore data and resume operations.
4. Perform regular account reconciliations so that recorded financial data matches the actual transactions and balances. This helps identify discrepancies and inaccuracies, maintaining the accuracy and reliability of financial data.
5. Use reliable accounting software to streamline your financial data management processes. These tools can automate tasks, generate reports and provide real-time visibility into financial data, making it easier to track and analyze finances.
6. Establish internal controls to safeguard financial data and prevent fraud. Set up segregation of duties, implement approval workflows and regularly monitor and review financial activities to maintain transparency and accountability.
7. Keep a detailed audit trail of financial data changes, including who made the changes and when. This helps track the progression of financial transactions and facilitates audit processes.
8. Provide training and awareness programs to educate employees on financial data management best practices. Make sure they understand the importance of accuracy, security, and compliance when handling financial data.

David Walters also believes that a strong financial data management strategy starts from within the organization.

“CFOs should clearly communicate the importance of accurate and timely financial data management. By setting expectations and standards, they create a culture where financial data integrity is valued and prioritized. This is done by establishing a strong data governance framework so data is managed efficiently and consistently. This includes defining data ownership, data policies, data quality standards and data management processes.

Collaboration between finance, IT, sales and other departments is crucial for effective financial data management. CFOs should encourage collaboration and open communication to address data-related challenges and ensure alignment with organizational goals.

Another important step is conducting training sessions to educate finance and non-finance personnel about the importance of financial data management. This helps create awareness and empowers employees to contribute towards maintaining data integrity.

CFOs should stay up to date on regulations and compliance requirements related to financial data management. This includes data privacy laws, industry-specific regulations and financial reporting standards.

Finally, CFOs should lead by example in practicing good financial data management habits. By demonstrating their commitment to data accuracy, security and integrity, they will create a culture of trust and accountability within the finance department and the organization as a whole.”

I also interviewed Brian Morgan, Director of AR Center of Excellence at BlackLine, to get his thoughts on financial data management priorities that CFOs should be mindful of. He shared interesting use cases that CFOs can apply with data.

“In a previous role, we analyzed the number and value of bad debts and the type of businesses that were failing across our customer base, including the age of the debt at the time of failure. We also reviewed the revenue that sales were generating for the same type of business which was significant.

Now, for many businesses, this would be a conflict of interest as sales naturally want to sell as much as possible and finance wants to reduce bad debts impacting the profitability of the company. By using and combining different data sources from sales and finance systems, we set up a new and different strategy for this particular business. We budgeted to decrease the number of bad debts by 20% (contrary to the natural goal of credit managers) based on a 50% increase in revenue. The agreement was that if the customer went beyond the agreed payment terms, services would be suspended immediately.

Honestly, the CFO took some convincing at first. But the result was that all stakeholders agreed and we exceeded the sales revenue targets. And the value of the debts was reduced because of the tighter collection strategy. Without the financial data at the start, I doubt all the stakeholders would have bought into the change of policy.”

David and Brian have made a compelling case for the positives. On the other side of the coin, it’s worth exploring what happens when financial data management isn’t handled properly. 

Trust issues

Inadequate financial data management can erode trust among stakeholders, such as investors, lenders and customers. Without transparency and reliable financial information, stakeholders could end up questioning the integrity of the business' financial operations.

Fraud vulnerability 

Poor financial data management makes it challenging to detect fraudulent activities in financial records. This can expose the business to higher risks of internal and external fraud, potentially resulting in significant financial losses.

This also extends to encryption and breach of data privacy regulations. Without regular security audits, stakeholder trust will be further damaged because nothing is being done to protect them. 

Risk exposure

Weak financial data management practices will increase a company’s risk of exposure to threats. Errors or omissions in financial data can lead to mismanagement of funds, inaccurate budgeting and failure to address financial risks in a timely manner.

Ethical use of AI and technology will help to offset these consequences. What I mean by this is having guidelines in place that establish accountability, fairness and transparency in how financial data is managed and processed.

And once this policy has been implemented, AI will provide the following benefits:

• Financial data, such as credit scores, DBT scores, frequency and value of past due payments,  are analyzed in real time. Trends, anomalies and patterns are assessed in a fraction of the time it would take a person, leading to better decision-making and forecasting. 
• Automation streamlines financial data management by eliminating manual tasks and reducing human errors. Automated data entry, reconciliation and reporting processes save time and improve accuracy. 
• Fraudulent activities are monitored consistently, alerting stakeholders and mitigating financial fraud.

Having listed these best practices and solutions, I’m aware that not everything is black and white. Each business has its own requirements and every CFO is in a situation specific to them. A slow and steady approach to FDM is the way to go. For CFOs to build value from data, they have to start from the requirements of the end-user and the level of knowledge and accessibility across departments and the organization. 

As David Walters, Chief Data Officer for Creditsafe, rightfully points out, balance is necessary with both technology and addressing challenges with financial data management. “By leveraging technology and embracing AI solutions, businesses can enhance financial data management processes, improve accuracy and efficiency and gain valuable insights for better decision-making. However, addressing challenges related to data quality, security, ethics, and skill development is crucial for the successful implementation and utilization of technology in financial data management.

As technology continues to evolve, businesses will need to adapt to these trends to stay competitive and leverage financial data as a strategic asset for growth, decision-making and risk management.”

Brian Morgan from BlackLine also weighed in on the future of financial data management. “AI will be at the center. But the definition of AI is so broad. The negative is that many things being called AI will simply be reports under a new banner. For me, it’s vital to ask the question ‘so what?’

For example, what is this telling me that I can’t access today? Or what is this solution/data going to do that I can’t do today? If it’s simply doing it faster, then surely this is process improvement and not AI. Of course, there are different dimensions for every scenario to be considered.

When used in the right way and from a trusted source, AI will greatly assist in understanding the trends of data and predicting what could or might happen. Where human intelligence kicks in and works alongside AI is to understand the data it presents and make decisions using the experience and knowledge of the business and the market.

Ultimately, the need and desire for improved financial data management will be accelerated. While AI is significant, change agent human expertise will still be essential and needed to validate the outcomes of the AI and the data management that’s produced.”