Welcome to our Clean Data Series! We enlisted three financial pros to weigh in on the issue of clean data, why it matters, and how they ensure their clients’ data is as clean as possible. Anna Ready is a bookkeeping firm owner who focuses on the non-profit sector. Sherri-Lee Mathers does bookkeeping work for clients in the hospitality industry. Erich Ly is a CPA who works with small- and medium-sized businesses. Through this series, you’ll learn about common places dirty data accumulates. You’ll also read about some great strategies for engaging your clients (or your team) with their data to ensure high-quality outputs that lead to better business decisions.
What exactly is clean data? And why is it so important in this age of high-tech data management and AI-driven automation?
Clean data is accurate and actionable data. It’s the cornerstone for deriving rich insights that lead to impactful decisions and powerful business strategies. At all levels of business––from small mom-and-pop shops to global corporations––data is increasingly being recognized as a valuable resource because of its importance across all stages of decision-making processes.
But, an ever-increasing volume of data makes it difficult to use it all to its full potential. To do that, the data has to be well-organized, accessible, and accurate. Thanks to emerging AI technologies, the process of collecting, sorting, and searching through data can be streamlined and simplified.
While small- and medium-sized businesses may not always have the time or expertise to keep impeccable books, bad or “dirty” data can be detrimental to the overall health of the business. Without clean data, business owners can’t get an accurate picture of what’s going on in their operations and finances. And when they don’t really know what’s happening in their business now, they won’t be able to find and fix issues or make impactful decisions going forward.
Systems that employ AI technologies can empower busy financial professionals and business owners to speed up the process of data collection, organization, and analysis. The catch is that AI-optimized systems, much like the humans who deal with data, have one critical limitation when it comes to data management: they have intelligence, but they aren’t psychic. And that’s where our conversation about clean data begins.
We define “clean data” as data that is accurate and error-free. On the other hand, “bad data” or “dirty data” is data that, for one reason or another, doesn’t reflect the truth. Dirty data could be the outcome of a transaction that was coded incorrectly in cloud accounting software, expenses that were accidentally categorized as revenue, or customer names or locations that were inconsistently labelled within a database. This is far from an extensive list: these are just a handful of examples to consider when it comes to the origin of bad data.
We define “clean data” as data that is accurate and error-free. On the other hand, “bad data” or “dirty data” is data that, for one reason or another, doesn’t reflect the truth.
When it comes to apps and even cloud accounting software, the saying “garbage in equals garbage out” couldn’t be more true. Some systems, of course, are designed to catch discrepancies. But, if an accounting system is ingesting dirty data, there’s no way for it to know that it’s providing inaccurate results to its users when it shouldn’t be.
Neither financial professionals nor AI-based software can accurately anticipate, spot, and correct each piece of inaccurate data 100% of the time. Furthermore, AI systems are built to respond to inputs, and, unlike humans, these systems can’t shout down the hall for clarification or make a quick call to a business owner to check in about the nature of a particular data point. While these technologies continue to evolve and become increasingly adept at identifying many data errors, AI systems ultimately work with the data they are given, and are not a solution, in and of themselves, to the problem of dirty data.
So how do you ensure that your clients’ data, or the data produced by your own business, is as clean as possible? And why should you take the time to invest in clean data management practices?