When you manage critical processes manually, mistakes will inevitably happen. This can cause a broad range of problems for organizations, ranging from inefficiencies and production errors to security issues and service outages. Despite this, you might be surprised to learn just how many of today’s enterprises continue to rely heavily on manual processes, even in critical business areas. Employees often depend on spreadsheets to manage everything from digital identities to compliance requirements. When employees enter data by hand, however, new opportunities arise for human error to rear its head.
Human error has always been an issue, but in the age of artificial intelligence, that problem has multiplied. If an AI application is basing its decisions on inaccurate or inconsistent data, the results it produces will be unreliable at best. With cybersecurity tools, governance, risk, and compliance (GRC) platforms, and other solutions now incorporating AI features on a regular basis, that’s a real concern.
Ultimately, AI is only as good as the data that supports it, which means poor data hygiene can have a significant negative impact on AI-powered programs and the organizations that are increasingly coming to rely on them.
AI has the potential to transform how today’s businesses operate, but using advanced AI tools starts with ensuring they have access to accurate, high-quality information. Doing that means data hygiene and governance are more important than ever.
Why Is Data Hygiene Important for AI?
Manual data entry often leads to inconsistent records, which can cause AI tools to produce unreliable results. Standardized, accurate, real-time data — supported by governance and automation — is essential for organizations to maximize AI’s value.
Manual Processes Lead to Poor Standardization
One of the biggest problems with manual data management is the lack of standardization. Human employees entering values into a spreadsheet will inevitably enter them in different ways. This can make processes like dependency mapping difficult. If a business wants to understand which applications share data with one another, employees can manually enter that information. This method can result in multiple, inconsistent entries, however.
For example, a business that uses Microsoft SharePoint might find that different employees have logged that application under “Microsoft SharePoint,” “Sharepoint,” “SharePoint,” “MS SharePoint,” “Share Point,” or countless other variations. So, if you ask an AI tool which applications interact with “SharePoint,” the result will be inaccurate, or at least incomplete.
This is just one example, but it effectively illustrates one of the most common stumbling blocks businesses face on the road to successful AI implementation. Data is the lifeblood of today’s organizations, and those that cannot count on the reliability of that data will struggle to keep pace.
A business may use AI to quickly evaluate the risks associated with entering a new market so they can make an informed, risk-aware decision. But if that AI tool is drawing upon inconsistent or point-in-time information, the insights it produces will be inaccurate, prompting a potentially risky or damaging decision.
Likewise, a management tool that produces incomplete account data could harm customer relationships. A financial solution that omits certain data sets could result in catastrophe. AI tools can be revolutionary, but getting the most out of them requires real-time, reliable data.
Making Data Hygiene a Long-Term Priority
Given the sprawling nature of modern digital environments, the process of establishing a consistent taxonomy can be a significant challenge. It’s possible to take a manual approach here. Organizations can start the process by creating a data dictionary and conversion table that clearly outlines the preferred language and structure for certain types of data. This approach can be a monumental undertaking given the size and scope of modern digital environments, however.
While incremental improvements may be feasible for some businesses, larger enterprises (especially those in highly regulated industries) require a more direct approach. Fortunately, today’s businesses now have access to more advanced solutions designed to streamline, and in many cases automate, the process of establishing data consistency.
Many of the tools essential to today’s businesses, such as governance, risk, and compliance (GRC) platforms, cannot operate without a high level of data fidelity. And while there are specific GRC point-solution tools for one or two immediate needs, adopting a modern, comprehensive solution will not only help ease the process of identifying inconsistencies within data sets and unifying them, but also empower your organization to scale and make risk-based bets from standardized, real-time information.
This more modern approach to governance allows organizations to look at the data spread across their digital environments in a more holistic manner, creating immediate improvements with new data management processes that are more automated, efficient and effective. Businesses seeking to maximize their AI investments quickly may find this approach preferable to incremental progress: By making data hygiene a priority, they can ensure their AI tools are drawing upon accurate, up-to-date information and providing actionable, valuable insights.
Inconsistencies and inaccuracies aren’t the only issues with manual data management. You also must establish appropriate governance around the use and access of data within the organization. Where does the final version of a critical file live? How and when is it updated? What are the systems of record for different business areas? Will those systems of record be redeposited into the data lake?
There isn’t necessarily a “right answer” here, but AI tools need a clearly defined process for determining which data sources are authoritative if they are going to be accurate and effective. Establishing clear guidelines, implementing automated tools and employing modern governance solutions can go a long way toward reducing the potential for human-driven inconsistencies.
Getting the Most Out of AI
With effective data hygiene practices in place, organizations can shift their attention to managing AI solutions themselves. What data should these solutions have access to? How will they query that data? Are there safeguards in place? Perhaps most importantly, who should have access to these solutions?
These are important questions for organizations to answer if they want to maintain the integrity and security of their AI tools. If they haven’t invested the necessary resources in establishing and maintaining a high degree of data hygiene, however, these tools are ultimately moot. AI is changing the world at an astonishing pace, and businesses that fail to use it risk being left behind. Modernizing your way of business isn’t just a good idea. It will put you in the best possible position to get the most out of your AI solutions.
