Businesses today have more data at their fingertips than ever before. New technologies allow companies to generate, collect, and analyze data with incredible speed and volume. However, this data abundance also carries greater risk. Without proper data governance and management, poor data quality, inadequate security and lack of oversight can critically impact business performance.
For finance teams, data governance will become a significant priority as flawed data leads to inaccurate insights and data breaches put sensitive financial information at risk. Just as finance oversees fiscal discipline, it must instill rigor into its data practices, and CFOs are feeling the pressure as they partner with IT, data scientists and more to clean and protect their data.
What Does Data Governance Mean for Finance?
Data governance refers to an organization’s ability to maintain the quality of data throughout its life cycle. It involves implementing controls over the data as needed to support business objectives and facilitate better decision making.
Key areas of focus in data governance and management include:
- Availability: Is the data accessible to all relevant users?
- Consistency: Is data from across sources and departments congruent?
- Integrity: Is the data accurate and reliable? Garbage in equals garbage out.
- Usability: Is the data accessible and understandable for its end users?
- Security: Is the data safe and used in compliance with regulations?
For finance and accounting teams, proper data practices help remove costly redundancies and errors, while also providing uniform processes that aid in faster modeling and strategic planning. For businesses that want to adopt AI and other intelligent technologies that process large amounts of data, it’s all the more imperative to have clean, safe data.
The Greatest Data Issues Facing Finance Teams
Fractional analyst and CFO Chase D. sees a number of data challenges facing his clients.
“The biggest data issue, if I were to explain it as a business owner would experience, is that they just don’t have data that’s granular. And they need it, when they need it, to make a decision,” says Chase.
Trouble retrieving and isolating that data quickly, according to Chase, is often due to a lack of standard operating procedures (SOPs) regarding data input and management.
Despite having the data, businesses still grapple with silos between systems and stakeholders, as well as slow manual processes and poor controls.
Better Data Governance and Management Requires Defined Processes and Stakeholders
Whether finance leaders are defining who has access to sensitive data or what to consistently name a certain field of data, it’s about documenting and defining operating procedures and crucial data governance roles.
“It all starts with a well-defined field or a well-defined process to get the data into a table or an application,” says Chase.
Ask questions like:
- Where does the data initiate and who owns it?
- Who inputs that specific field of data?
- Who are all of the downstream stakeholders for this data and how will they be made aware of changes to the data?
- Are the stakeholders impacted able to contribute to the decision making around that data?
In addition to defining these roles, it’s important for leaders to work with the right experts, whether in their organization or hired fractionally, to help automate and centralize data management for consistency.
Building Trust and Credibility in the Data
Credibility is of paramount importance, especially for CFOs and FP&A practitioners who must communicate complex financial data to non-finance leaders and managers.
It’s important to first establish transparency around the data’s quality and explain the benefits of making quality assessments. Have a plan in place that you can communicate to stakeholders when the data just isn’t quite there yet.
Establishing trust in business comes down to a simple matter “of just doing what you say you’re going to do, when you say you’re going to do it,” says Chase. While leaders know they can’t rely on the data yet, there’s a benefit in knowing there’s a plan in place.
Key Partnerships for Data Success
Building a data governance and management plan is not something that finance leaders can do alone. Finance benefits even more by partnering with IT and database administrators, as well as outside experts.
“Establish a partnership with the administrators of the database and your company and really start working across the organization with the data organization,” states Chase.
Today’s CFOs are working more closely with IT and even their own data organizations to better understand:
- Where is the data coming from?
- What are the current gaps?
- What could go wrong with this data in the future?
The other component is working with the right fractional or outsourced experts who understand and practice these standards with today’s most up-to-date tools.
Take a page from small businesses. For smaller businesses, data management can be a mountain, starting with needing an accountant just to record the numbers correctly. The best way to get up to speed is to stack your team with the right transferable skills to help reach those goals faster. This cost-effective investment helps you solve data challenges sooner. The longer you wait, “the more bandage you’ll have to rip off and replace with a long-term solution,” states Chase.
Enterprises will also benefit from outsourced and interim expertise, helping them to fill gaps and train internal teams in new areas of data analysis, data science and more. Providing ongoing education on using data insights is essential to a data management plan that sticks. Team leaders may incorporate some continuing education in the form of a formal course or even an expert coming onsite for a day to lecture.
Strengthen Data Governance Best Practices With Fractional Expertise
At Paro, we help businesses increase the ROI on their data investments with solutions powered by vetted, on-demand finance experts. From establishing processes and deeper controls to building more efficient financial models with your data, our experts have the necessary analytical and technical skill sets to improve your data integrity and achieve greater success.
