5 tips to improve data quality

At a time when data is gaining more and more importance in companies, a big question arises: how to ensure the quality of the information collected, stored and used ?

In fact, low quality data poses a risk to management: poor decision making, wasted time, lower profitability, etc.

Do you want to develop data quality in your company? Here are some tips that might help you.

  1. Define clear data governance

“Data” in the broadest sense is certainly one of the most valuable assets available to your organization. To ensure that this information is of high quality, set a data management it’s essential.

This is specifically the internal policy of a company that must ensure that the data are:

  • Collected and organized according to structured processes
  • Reliable
  • Safe
  • Accessible
  • Documented
  • Managed and audited regularly

This governance constitutes the cornerstone of any process for exploring data. By ignoring it, you run the risk of starting from a bad base and getting unsatisfactory results.

  1. Purchase data consolidation software

A large amount of data is collected, stored and analyzed using Excel workbooks. Your business is certainly no exception.
However, despite its many qualities, Excel also has several weaknesses : Time-consuming, insecure, also encourages input errors and makes data tracking difficult.

Various software now allow consolidate Excelinside replicating your existing Excel into forms and converting data collection processes into secure workflows.

These tools provide better control over information. They allow you to easily consolidate certain data sets in a fully automated way.

  1. Look for errors and duplicates

Improving the quality of your data often requires going through a cleaning phaseand in-depth data sets. The objective is to prevent input errors, duplicates, inconsistencies and bad formats from jeopardizing the future use of the collected information.

The process is certainly time-consuming and laborious, but it is necessary to sanitize your data and start over on solid foundations.

In addition, there are tools capable of to automate these operations data cleaning and refinement.

Also note that these common errors are fixed daily, not all at once. Despite the precautions taken, it is common for problems to pass through the cracks.

“Quality checks” should therefore become routineideally supported by data profiling tools that reduce the amount of manual work.

  1. anticipate problems

The best problems are the ones that don’t arise. . Data quality, therefore, is not just about correcting existing failures, but also about anticipating future risks.

This anticipation requires carrying out regular audits, with the aim of identifying potential risk factors in order to minimize problems later on.

In parallel, your company’s “data” processes will have to be tweaked and in some cases automatedto better meet your data governance guidelines.
For example, introducing mandatory fields into forms is a good way to work with incomplete data or to restart collection operations.

  1. Developing a data-driven corporate culture

Although specialized profiles (Chief Data Officer, Data Steward, Data Scientist, etc.) everyone is responsible for the quality of the data. The vast majority of your company’s employees are concerned with data management.

To develop team involvement, Efforts should be made to raise awareness, train and gather the various services around related best practices. The governance principles must therefore be known and understood by all employees, depending on their area of ​​responsibility.

Anyone in the company should feel like a changemaker and being able to act at scale on the data you manage on a daily basis.
Finally, continuous improvement must be placed at the heart of your data exploration strategy. Instead of seeking immediate and, in any case, utopian perfection, seek to identify areas for long-term improvement. gradually towards operational excellence.

Leave a Comment