Most modern businesses rely heavily on data and analytics to operate successfully, but user errors can easily compromise the integrity of those data and analyses. If the accuracy or consistency of your data are compromised, you won’t be able to rely on the insights you gather.
According to insightsoftware, “any mistakes in that information can have serious consequences that spread across departments.” The article goes on to say, “Avoiding such incidents isn’t easy because when errors creep into the data, they’re hard to recognize, and even harder to remove. As a result, they tend to linger and propagate, spreading misinformation and misunderstanding wherever the dubious data gets used or shared. Depending on the error, [the organization] may have a wildly inaccurate understanding of performance.”
So what steps can you take to control user errors in a data-dependent environment?
Work From a Single Source of Truth
Your first job is working from a single source of “truth.” Oftentimes, mistakes creep into your organization because your workers are juggling many different sources. They’re referring to half a dozen different management platforms, and might be drawing data from multiple different reports. If you consult two different sources, you might end up with two completely different versions of the story.
You can fix this by keeping all canonically relevant data confined to a single source. Or, if you need to manage data in many different places, establish a hierarchy for which sources take precedent when there is a contradiction.
Rely on Automated Processes Whenever Possible
Automation is valuable because it saves you time, eliminating the need for man-hours being spent; however, it’s also valuable because it’s almost impossible for an automated system to make a mistake, so long as it’s programmed properly. If you can automatically draw data and enter it into the proper fields, your employees will never have the chance to make a mistake. Obviously, automation isn’t an appropriate option for all modes of data entry or analysis, but it can be highly effective when utilized in a way that showcases its strengths.
Standardize the Data Entry Process
Data entry almost always has room for errors, so do your best to standardize the data entry process. Create a standard operating procedure (SOP) for entering data, explaining the exact formatting and operations necessary to minimize the chance of errors. This way, there will be less room for erroneous procedures to creep into individual approaches; there will be a single source of truth for procedures.
Provide Training to Employees
It’s typically not enough to put your standardized procedures into writing, though it is essential. You’ll also have to invest more time into training employees, including mastering these concepts:
- The importance of data. First, you need to train and educate employees on the importance of preserving data. Teach them to respect the numbers they’re entering, and value what they mean to the organization overall.
- Accuracy over speed. There’s always a tradeoff between accuracy and speed; the faster an employee goes, the lower their accuracy is going to be. Train your employees to work deliberately and cautiously, rather than rushing through their tasks.
- Double-checking. All employees should be competent and confident in double-checking their own work. Teach them to catch their own mistakes early (and correct them).
Instate Supervision and Reviews
Even with employees committing themselves to double-checking their own work, it’s important to instate supervision and other high-level reviews. This can be hard to balance, because adding new roles to the data management team and adding more man-hours to the process can compromise the efficiency of the operation. However, periodic reviews can catch mistakes early and serve as a preventative measure that encourages employees to be more consistent.
Hire and Use Ample Resources
Sometimes, mistakes creep into your data because employees are overwhelmed. If they’re excessively stressed, or if they’re responsible for entering too much data, they’re naturally going to slip up. You can mitigate this by making sure you’ve spread the workload evenly, hiring new people when possible to shoulder the burden of new responsibilities. You can also institute policies and rewards that give people plenty of time and resources to manage their stress.
Gather Feedback and Improve
Finally, and perhaps most importantly, work to gather feedback from your employees about your data management systems. Are there any inefficient processes that could be sped up? Are there problems with how your data is received, or how it’s entered in the system? Use feedback to make new improvements regularly.
You won’t be able to eliminate all user errors, no matter how much of an effort you make. Do your best to learn from the mistakes you catch, and continue to refine your processes for gathering, inputting, and analyzing data.
Larry Alton is a professional blogger, writer, and researcher who contributes to a number of reputable online media outlets and news sources. A graduate of Iowa State University, I’m now a full-time freelance writer and business consultant.
