Missing data, incomplete records, poor formatting, and inconsistent data entry are common issues faced by users of ETO software, which leads to users not trusting the accuracy of their data. But it doesn’t have to be this way!! This blog outlines 5 simple actions that you can adopt to improve your data quality and most importantly, your confidence in your ETO database.
1. Address data quality issues at the point of data entry
You can minimize most data quality issues by addressing the causes of those issues at the initial point of data entry.
Users are responsible for data entry, whether data entry involves imports, batching, or manual data entry. Implement data quality controls at the point of data entry by supporting users with specific tools and resources, including:
- User Guides – Outline the step by step data entry process in user guides so that data entry is uniform and consistent. User guides are also helpful in the event that data entry staff with solid ETO knowledge leave your organisation. New staff members can easily pick up from where former staff left off, and critical organisational knowledge is not lost if well documented.
- User Training – Data entry users need regular user training to know how to enter data properly and consistently based on your data entry requirements. Training is the only way for users to build their capacity. Training is also a great complement to user guides.
- User Support – When users have questions they will often attempt to complete the data entry task on their own if they can’t get an immediate response from their supervisor or ETO database administrator. Leaving users without support opens the door for incorrect data entry. So, instead, it is important to provide prompt support to users when they have questions or need direction on how to complete data entry.
2. Schedule recurring data quality reviews
Add data quality reviews to your recurring weekly or monthly to-do list (or quarterly, depending on the type of review).
This is the best way to stay on top of ETO data quality challenges and prevent these from blowing out into unmanageable, and even unrecoverable issues.
Data quality reviews and audits shouldn’t be an everyday task. Once you know your database back to front, you might feel the urge to get into your system daily and clean up records. But, it is better to schedule a set time each week or month for data quality than to be continually cleaning up your database at irregular times.
3. Create procedures for data quality reviews
To ensure your data quality reviews are efficient and not too cumbersome, create specific methods and procedures that outline the actions you will take during each recurring data quality review. The more you engage in a routine procedure, the less time it will take you to complete it next time.
You could document these data quality procedures in user guides with step-by-step instructions and illustrations of the process. Some examples of procedures you may want to create include:
- Running data quality report XYZ with filters ABC
- Exporting a data quality report to Excel
- Reviewing X report by looking for Y in rows and Z in columns.
- Correcting data inaccuracies in your ETO database (or in Excel and prep for import
- Identifying any consistent themes in data quality and developing action items to rectify in future data quality reviews
Documented data quality reviews are also transferrable to future database administrators in the event that your administrator leaves your organisation or someone else needs to step into the role.
4. Set up automatic data quality reporting
Although you don’t want to be diving into your ETO database daily to clean up records, there are some data quality topics that may require immediate attention or are easier processed in real time. For these data quality topics, you can set up automatic data quality reports that allow you to identify inaccurate, incorrect, or incomplete data entry. For example:
- Scheduled Reports – You can build scheduled reports in your ETO database that run automatically on a set frequency so that you can regularly review the report and the records contained in the report for poor data quality. You can even set up the report to look for specific records that match poor data quality conditions that you have identified in the past.
- Queues and Lists – Like scheduled reports, ETO has the ability to create a simple report with specific criteria that finds the data sets or records with poor data quality and produces a filtered list or queue. Data sets and records that match the criteria flow into the queue and when they are corrected, they fall out of the queue.
- 5. Include multiple users in data quality processes
Managing data quality can be a large task, so it is best to involve multiple user groups in the process by:
- Training users on data quality standards so they know what to look for in data quality
- Provide feedback to users on data quality errors so they can learn where data quality challenges are occurring
- Spread out the responsibilities for data quality reviews so no one person is burdened with the responsibility
- Listen and learn from users across the organisation so you can identify how users value data and how you can leverage off this to ensure data quality
So it is essential that you invest time and energy into data quality procedures because ultimately your organisation relies on your ETO software system to produce accurate reports in order to make sound strategic decisions from your data.
Organisations that don’t have routine data quality procedures might complete major data cleanup projects every now and then. However, as the data quality challenges grow, they are forced to invest in large-scale cleanup projects to overhaul their entire database. Managing data quality on a frequent basis can prevent this from happening, saving time, money, and stress in the long term.