Data Blog by Lizeo
[Practical guide] Your Data Quality Process in 5 Steps
Today, reliable data is essential to making the right business decisions. It provides an up-to-date, real-time picture of the market in order to measure the efficiency of strategic actions. Follow these 5 steps to implement and make your data quality process sustainable.
Data Quality issues
Dirty Data is a recurring problem that has many impacts: non-qualitative data creates inaccurate analyses, an incorrect view of the market and, ultimately, a lack of responsiveness and relevance in decision making.
Before taking steps to improve data quality, you must first define what quality data means in your organization, and above all, what it will be used for :
- What are your use cases ?
- What business questions do you want to answer ?
- What information do you need to answer them ?
- What type of data do you need to use to access this information ?
Data Quality: 5 actions to implement
Details of these 5 steps can be found in our practical guide.
- Framework
- Data Profiling
- Data Cleaning and, if necessary, Data Enrichment
- Sustainability through Data Governance Rules
- Monitor
When we consider data quality, we often think of cleaning data (deduplication, incomplete data enrichment, harmonization, deletion of obsolete data). But be careful, this is only the tip of the iceberg! The upstream (definition of rules, audits, etc.) and downstream phases (sustainability and monitoring) are also essential to start a sustainable approach in creating business value.
Data quality is not an occasional process, it is a continuous endeavor, that should be automated as much as possible, with human expertise, to have complete, homogeneous, integrated, useful and up-to-date data at all times.
The work involved in data quality is a long process: you must have reliable data to make decisions and manage your strategy. To do this, it is essential to ensure the quality of your data at time “T”… and make sure the quality is maintained over time. Download our practical guide to implement the 5 steps of the data quality process!
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