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[Practical guide] Your Data Quality process in 5 steps

[Practical Guide] Your Data Quality process in 5 steps

Today, reliable data is essential to make the right decision, to have an up-to-date view of the market in real time and measure the efficiency of your strategic actions! Follow these 5 steps to implement and make your Data Quality process sustainable.

Data Quality issues ​

Dirty Data is a recurring problem which has many impacts: non-qualitative data means inaccurate analyses, an incorrect view of your market and, ultimately, a lack of responsiveness and relevance of your decisions.
But, before taking steps to improve your Data Quality, you must first define what quality data means in your organisation 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, harmonisation, 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 to create business value.
Data Quality is not an occasional process, it is a continuous endeavour, that you should automate as much as possible and complete 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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