Throughout this series, we’ve been talking a lot about how important communication and engagement is for the production, maintenance, and use of clean data. When it comes down to it, the usability of data relies on the interactions between the humans who have an investment in the data and what that data can tell us about the state of the business. We know that data tells an important part of the story, but only when the data is clean. 

Clear and intentional communication helps data managers and stakeholders give and understand context surrounding data. While the capacity of AI technologies continues to advance to support more complex data-related functions, it isn’t yet able to “think” critically about mistakes or anomalies in exactly the same way humans can. So, it’s a core responsibility of financial professionals and business owners to provide the most precise information possible to reduce any potential ambiguity that can ultimately lead to data errors in AI-driven systems. We saw several examples of this throughout the series: Erich Ly’s discovery that a company’s AR hadn’t been aged correctly, Sherri-Lee Mather’s frustrations with variations in time and state name formatting, and Anna Ready digging into balance sheet discrepancies. Without consistent standards and inputs, automation can’t perform its purpose optimally and the data cannot be used as a credible source of truth.

At the end of the day, apps and software systems simply don’t know what they don’t know. As we continue to move into a future where it’s cost-effective and labor-reducing to rely on automation through digital channels, the importance of inputting clean data into these systems increases exponentially. Clean data starts with people who understand its value and are willing to commit to consistently using processes, standards, and technologies to ensure that their data is always as clean as possible.

While AI can help computers adapt to complex human needs and behaviors, there’s still a learning curve ahead and a levelling of expectations required for those who want to make the most out of these technologies. It’s a bit like driving a car: while cars were built to optimize human movement and are continually improved for safety, comfort, and ease-of-use, we still have to learn how to drive. Until self-driving cars become the norm that is, at which point we’ll consider adjusting that analogy…

Communication and engagement are, again, essential. Future-focused business owners and financial professionals alike are on a journey to become more data-driven and to leverage data as a resource that drives revenue, growth, and success. It takes cooperation to share knowledge and best practices to ensure data is clean, and deliberate action is required to help individuals get the most out of the valuable information data contains. It also takes collaboration to successfully perform healthy data hygiene tasks like quality assurance reviews or reconciliations on an ongoing basis.

Ultimately, a database is a bit like a community garden: they take the work of many to cultivate, and to stay healthy and thriving. With the right systems in place, supported by a group of invested individuals, data has the power to inform better business decisions that have extraordinary outcomes.