Data quality management is a set of practices aimed at sustaining a high quality of information. DQM runs all the way from data acquisition and implementation of advanced data processes, for the effective distribution of data.
It also requires managerial oversight of the information you have. DQM effectively recognized as critical to any analysis of the data is consistent because the quality of the data is very important to follow up Lower and – more importantly – accurate insight from your information. If you are looking for data quality and master data management then you are at right place.
In this article, we will detail everything that is at stake when we talk about DQM: why it is so important, how to measure the quality of the data, the pillars of good quality management, and some data quality control techniques.
Reporting to be part of DQM effective, we are also going through some examples of data quality metrics that can be used to assess your efforts in this regard.
Why You Need Quality Data Management?
While the digital age has been successful in encouraging innovation far and wide, it has facilitated the so-called "crisis" data from the digital era – the low-quality data. But before embarking on something:
What is the definition of data quality?
Data quality refers to the assessment of the information that you have, relative to goals and its ability to serve that purpose.
Data quality is defined by the different factors that will be detailed later in this article, such as accuracy, completeness, consistency, or timeliness. qualities needed to meet the organization's needs in terms of operations, planning and decision making.