Organizations need to improve data quality to ensure they’re always using accurate and useful data. According to Experian’s 2021 Global Data Management Research report, businesses say that poor quality data:
- Wastes resources and increases costs (40%)
- Damages the reliability of analytics (36%)
- Negatively impacts reputation and customer trust (32%)
- Negatively affects the customer experience (32%)
- Slows down digital transformation and hinders key business initiatives (31%)
Improving data quality resolves these issues and results in several significant benefits, including more reliable analytics, efficient operations, and reduced costs. That is why Gartner projects that in 2022, 70% of organizations will use key metrics to track data quality – and, in doing so, improve it by 60%.
There are several things you can do to improve the data quality in your organization. It isn’t difficult to do – if you’re committed to doing it.
- Ensuring high data quality provides more reliable analysis and more efficient operations
- To improve data quality, start by assessing your data and defining what is acceptable quality
- Organizations also need to eliminate data silos, correct errors upfront, and promote a data-driven culture
- The best way to improve data quality is to use an automated data quality management solution, such as DataBuck
How to Improve Data Quality in Your Organization
Given the measurable benefits of high data quality, it is no surprise that organizations of all types focus on improving their data quality. Here are 12 steps your organization can take to improve data quality – and improve the effectiveness and efficiency of your business.
1. Assess Your Data
Before you can improve the quality of your data, you have to understand what data you possess. That means conducting a formal data assessment to determine:
- What data you collect
- Where it is stored
- Who accesses it
- Current format (structured vs. unstructured, etc.)
2. Define Acceptable Data Quality
You also need to define what your organization considers acceptable data quality. If data cannot be 100% accurate and relevant, how close to perfect do you need to get? You may need to establish different data quality (DQ) standards for different data types and for different uses of that data.
3. Correct Data Errors Up Front
Identifying and fixing data problems is part of any data quality management (DQM) initiative. You make DQM easier, however, when you ingest clean data. That means designing systems to ensure more accurate data entry and flagging inaccurate or incomplete records before they enter the system.
4. Eliminate Data Silos
A large enterprise often silos data within different departments or physical locations. When this happens, it is not easy to obtain a comprehensive view of your business or efficiently locate and use all the data you possess. Data silos, operating independently with their own rule sets, are also prone to data quality issues. You need to centralize all your data to make it more usable and to ensure that all data is subject to the same DQM processes and requirements.
5. Make Data Accessible to All Users
Data silos also have the unfortunate effect of isolating valuable data from many of the employees who need it. The data you collect needs to be high quality and accessible to a broad range of potential users. This argues in favor of cloud-based file sharing that employees can access from any location, especially those working remotely.
6. Use the Correct Data
Your organization collects plenty of data – but are you collecting the correct information? Even more important, are you choosing the right input to use for your various analyses? You need to tap into a diverse collection of resources while filtering out those not relevant to your current needs. This also means capturing the right data in the first place – your data collection efforts need to reflect your projected data needs.
7. Impose a Defined Set of Values for Common Data
Many data errors come from users entering freeform data. Consider the scenario where you allow users to manually enter a state name. Some users enter “MN,” some enter “Minn,” some enter “Minnesota,” and others misspell it as “Minesota” – all of which lead to significant errors in your data. Instead, offer users a defined list of values or options for common fields so that, in this example, they can only select approved state abbreviations from a drop-down list. This will give a cleaner and more consistent data set than other methods.
8. Secure Your Data
You must secure valuable data from unauthorized access. You need to comply with relevant privacy regulations and other requirements to ensure customer data doesn’t fall into the wrong hands. This is especially true for protecting against data breaches and cyberattacks and ensuring the wrong users cannot edit data and compromise its integrity. This requires employing various data security methods while still enabling access to authorized users in your organization.
9. Promote a Data-Driven Culture
An effective data quality improvement process requires the participation of all your employees, from the C-suite to the administrative pool. Initiate regular training on data quality and key DQM processes to ensure that everyone does their part.
10. Appoint a Data Steward
If your organization truly takes data quality seriously, you should consider appointing a data steward to oversee your DQM efforts. This individual should be responsible for analyzing your data quality, conducting regular DQ reviews, and implanting new approaches to DQM. The data steward should also train your staff on DQM procedures and improve DQ over the long run.
11. Conduct Regular DQ Reviews
Finally, to ensure that your data quality improvement efforts retain their effectiveness, you should conduct regular reviews of your organization’s data quality. These reviews will tell you if you are making progress and, if not, where you need to make further improvements. These reviews should be the purview of your company’s data steward.
12. Employ a Robust Data Quality Management Solution
Finally, one of the most effective ways to improve your organization’s data quality is to use an automated data monitoring solution, such as FirstEigen’s DataBuck. An automated DQM platform automatically analyzes your data, identifies existing issues, and then “cleans” or deletes bad data. This is a much more effective and faster approach to DQM than trying to do it all manually.
Let DataBuck Enhance Your Data Quality Improvement Process
When you want to improve the quality of your firm’s data, turn to the experts at FirstEigen. Our DataBuck software is an autonomous data quality management solution that automates more than 70% of the data monitoring process. When you use DataBuck, you know that your company’s data is complete, accurate, and utterly reliable. Contact FirstEigen today to learn about using DataBuck to improve your organization’s data quality.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)