Data Dissonance: The Barrier to Automation and Efficiency
By Michael Schmidt, Business Analyst, Marcum Technology
Why can’t these systems talk to each other?
If I could get these datasets to be easily comparable, I could…
I’d save hours if I just had this in a nice spreadsheet.
The challenges above constantly plague finance and accounting departments. Years of growth or disruptive mergers/acquisitions often lead to a dizzying array of disconnected and redundant enterprise systems. Personnel turnover compounds the problem because when the people who implemented these systems leave, they leave with years of legacy knowledge that rarely transfers over to a successor, leaving the systems even more unmanageable.
The result is data dissonance. Separate systems don’t automatically communicate, leading to wasted time manually entering information (and the potential for human error). When datasets are in different formats, managers can’t identify key performance metrics. If employees are stretched too thin or lack the skillset to remedy data dissonance, the cycle of dysfunction continues.
This problem is not surprising given the plethora of software solutions on the market. Capterra, a leading aggregator for business software solutions, lists 761 accounting software products. Consultants, CPAs, and lawyers operate in this chaotic data landscape.
Professional services firms are increasingly centered on data, particularly data provided by a client. A digestible dataset is a prerequisite for most high-level work. High-level work includes analyzing data, building predictive models, forensic tracing, or gleaning meaningful business intelligence from disparate sources. In fact, a 2020 survey found that data scientists spend 45% of their time on data preparation rather than sophisticated analysis.
Technology companies like to preach the promise of automation, but the reality is that without data standardization, automation is frustratingly out of reach. Data dissonance is the primary problem that prevents automation in most organizations. For professional services companies, automation is even tougher because every client has a different set of enterprise systems and thus, more data formats to program into an automation.
Finance and accounting departments frequently handle data dissonance by hiring expensive software implementers to upgrade existing systems. Sometimes this strategy works. But too often the department is ill-equipped to provide sufficient project specifications to yield a satisfying result, or the consultants act like salespeople for new software instead of advocating for the organization. Even successful implementations can take years to finalize. Given the upfront costs and time needed, it is not surprising that most organizations view this method as too risky to attempt.
Some professional services firms dealt with data dissonance by outsourcing to overseas data entry shops, but that has proven inadequate. The lead times required are unrealistic in a fast-paced business climate, the quality of the results is often unreliable, and the costs are not low enough to justify either deficiency.
AI is also touted as a panacea for data dissonance. In certain situations, this is true. With the correct software and knowhow, AI can be used to standardize data from disparate systems. But the cost of the software and the effort needed to teach the machine should be carefully considered. For larger organizations, this may be a good option if the volume of transactions and time saved is significant. For smaller organizations, the volume is rarely high enough to justify the cost. Most professional services firms handle so many enterprise systems that development costs/time typically outweigh any gains.
What can be done? One solution is to hire an in-house specialist in data manipulation and formatting. Many organizations already informally use this solution. The problem is the specialist often has other responsibilities, so they burn out or their performance suffers. The correct way to implement this solution is to officially designate a data specialist(s) and reduce other duties accordingly. In the professional services industry, this solution is particularly attractive because the vast collection of data formats makes software, outsourcing, and AI unlikely to succeed.
At Marcum Technology, our team of data solutions analysts (called Intelligent Prism) functions as our data dissonance busters. The team services all 2,500 employees at Marcum LLP across all business lines. To date, the team has handled 10,000+ data requests ranging from simple data extraction/standardization to building complex predictive models for accounting professionals to analyze. In addition to serving a supportive role on assurance, tax, and advisory engagements, Intelligent Prism directly serves other professional services firms and accounting/finance departments to meet their data needs. Often, Intelligent Prism’s work creates an onramp to automation as the low upfront cost and commitment is used as proof of concept. In fact, a good chunk of the work is already done by Intelligent Prism when the decision is made to implement an automation project.
For more information about beating data dissonance and the exciting opportunities available, contact firstname.lastname@example.org.