Modifying Data Management Processes to Streamline Financial Reporting

Every DMO depends on clean financial data, but getting there often means someone quietly doing the unglamorous work of cleaning it up by hand. That was the case at Visit Tulsa, where the team’s monthly cash balance projection model relied on a manual, multi-step process to turn CRM data into something the finance team could actually use. By bringing Business Intelligence and Finance to the same table to solve the problem at its root rather than its symptoms, the Visit Tulsa team eliminated that manual step entirely, and in doing so, uncovered a model for how small process changes upstream can pay off across an entire organization.

Numbers You Can Use  ·  Business Intelligence

The Problem: Every month, updating the finance team’s cash balance projection model meant exporting unstructured data from the CRM, reviewing it record by record, and manually re-entering it into the financial model in a structured format. Because that review depended on human judgment, results varied, sometimes for the exact same data point depending on who reviewed it or when. The additional step of manual data entry compounded the risk, introducing fresh opportunities for typos, mismatched fields, and inconsistent formatting before a single number ever reached the model itself.

The Solution: Rather than continuing to patch the output after the fact, the team started at the source. They worked closely with Finance to understand exactly what format the data needed to be in to meet reporting requirements, then worked backward from there, designing the process around the destination rather than the starting point. Comparing the unstructured CRM exports against the target output revealed clear opportunities to remap fields into standardized, structured formats from the moment data was entered, not after it had already passed through several rounds of human interpretation. Standard operating procedures for the CRM were updated to reflect these new data standards, so the fix lived permanently in the system itself rather than in a one-off manual workaround that someone would eventually have to repeat all over again.

The Results: Structuring data at the point of entry eliminated the need to manually process CRM exports before they could be used in financial modeling. Reporting became faster and meaningfully more consistent, since the same input now reliably produced the same output every time. Just as importantly, the team gained a foundation for future automation that the old manual process never could have supported, since automating a process built on inconsistent inputs only automates the inconsistency. Once data is clean at the source, it opens the door to additional efficiency gains down the line. One question worth asking your own data: where is your team still cleaning up after a system that wasn’t built to give you what you actually need?

Dollars & Sense  ·  Finance

The Problem: Before this change, the finance team’s visibility into cash position depended on a slow, error-prone pipeline that sat between the CRM and the model that mattered most to leadership. A model that should have offered a current, reliable read on financial health instead lagged behind, vulnerable to the same inconsistencies introduced upstream by manual data handling. Finance was, in effect, working from a snapshot that was already somewhat out of date and potentially inaccurate by the time it reached them, making it harder to plan with real confidence.

The Solution: Once data was structured correctly inside the CRM, Finance no longer needed to wait on a manual hand-off to get usable numbers. The cash balance projection model could pull standardized, reliable inputs directly, without a reviewer first translating unstructured exports into something the model could actually read. That shift didn’t require Finance to change how it modeled cash flow or rebuild any existing tools; it simply gave the model the clean, consistent inputs it had needed all along, removing a bottleneck that had nothing to do with the financial logic itself and everything to do with how the data arrived in the first place.

The Results: Eliminating manual intervention gave the finance team something they hadn’t had before: real-time visibility into their financial position, rather than periodic snapshots assembled after the fact and reviewed for accuracy along the way. Reporting is now faster, more consistent, and far less dependent on any one person’s interpretation of the underlying data. More importantly, that visibility is changing how the team operates day to day: instead of reacting to periodic reports after the fact, they can make informed, forward-looking decisions about upcoming opportunities as their financial position evolves in real time. One financial action item to consider this week: ask your data team what’s currently being manually reconciled before it reaches your desk, and whether structuring it earlier in the process could close that gap for good.

This is a clear example of what happens when Business Intelligence and Finance solve a problem together instead of in sequence: faster reporting, fewer errors, and a foundation for the automation that comes next. Have a similar story from your own team about disciplines working together to solve an operational challenge? Share it with us through the BizOps resource hub.