The first time a student walks into a business class, they may expect to learn a lot about numbers. What they might not realize is they are walking into a foreign language class! Every industry has its own subculture, jargon, expectations, and perspectives; and young professionals in specialized fields, like accounting or computer science, tend to form silos where they think and speak like their peers — and don’t understand the needs and abilities of those in other fields.
Thus, bringing accountants and data analysts together to use and understand data is a challenge, as they often speak different languages at work. My frequent opportunities to work with (and translate between) these two audiences has provided me with unique insight into their communication gap. Here are some observations that can help data analysts and accountants understand each other — and improve projects they work on together.
Communicate early and often.
Data analysts can’t provide a solution for a job they don’t even know exists. Take the time upfront to talk to each other and save time later. This gives management time to make decisions computers can’t, while the computers can do other things, like predicting the completion time for a manufacturing job, or identifying patients with billing problems at a hospital.
Use the power of data analysis and create tools that eliminate repetitive tasks.
Analysts and accountants can work together to identify jobs best performed by the computer. For instance, accountants may not realize that the Excel spreadsheet they create manually each month to calculate their allowance for bad debts can be generated faster using a macro, complete with accounts receivable agings and formulas. They need to ask IT to create a custom process.
Don’t accept what the computer delivers without analyzing results.
An accountant who manually created an “allowance for bad debts” spreadsheet may notice the accidental omission of a certain account in the calculation. The computer can’t do that. Therefore, when an accountant meets with a data analyst to explain their needs, they need to consider, and explain, every possible exception. Otherwise, the analyst has no way to know if their coding introduces errors into the process. End users need to understand they may be the only people who can recognize whether a result is reasonable — and that they are ultimately responsible for the content of the work product.
For example, I was working with a client to process large volumes of payroll data to perform a unique, specialized analysis. I identified a number of employees whose results didn’t meet expectations. While we had previously discussed the calculations at length, it turned out that, unknown to me, the company paid one employee differently than other employees. If I didn’t have a baseline for my expectations, the discrepancy would have skewed the data. Luckily, I did, the data was sound, and the client was able to pay the employee properly. The experience emphasized the risks of ignoring the quality assurance step of any data analysis project.
It’s a challenge to identify missed opportunities for better data efficiency and accuracy. One way to improve this is to tear down the wall of miscommunication between the accounting and information technology functions within your organization. Consider:
• Providing classroom or hands-on cross training
• Including each other in team meetings
• Creating team opportunities for collaborative work to help them understand what their colleagues are doing
As accounting and IT groups explore each other’s capabilities and needs, the two groups will build synergy, understanding and better communication. Integration like this can help the accounting staff become familiar with the tools at their disposal, and can help the IT staff better understand the larger goals of the organization.