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Data Quality Management

With the growing impact of technology , data has became an integral part of every business operation. The success of the business is determined by the quality of the data that is gathered, stored and consumed during business processes.

Quality of data is all about the evaluation of the information you have in accordance to its purpose and its ability to serve that purpose. The quality of data is defined by many different factors like accuracy, completeness, consistency, timeliness etc. Quality plays an important role to fulfil the requirements of an organization in accordance of operations, planning and decision-making.

Data Quality

Data quality management (DQM) is a complete set of steps or practices that’s followed by an organization to maintain a high quality of data and every information needed to grow the business. DQM starts with acquisition of data and applying advanced data processes to achieve end results to effectively distributing data with a proper managerial oversight about the information you have. Effective DQM is known as an essential part of any consistent data analysis, as the quality of data plays an important role to get actionable and accurate insights from the information you have.

DQM is a principle in every business and it requires a combination of the right people, processes and technologies all with the exact same goal of improving the quality of data that matter a lot to an organization. The ultimate purpose of DQM is not only to improve quality just for the sake high-quality data but its to achieve the business outcomes that actually depends on quality of data.

Five Important Pillars of DQM
1. The People
2. Data Profiling
3. Defining Quality Of  Data
4. Data Reporting
5. Data Repair.

For measuring quality of data, you need data quality metrics, that defines the data. It is the main key for assessing efforts that you put in for increasing the quality of your information. Among all the techniques of quality management, metric is top-notched and clearly defined. These metrics encompass different aspect of quality all the quality of information i.e. Accuracy, Consistency, Completeness, Integrity, and Timeliness.

Data management is work of entire team:
Data owner – controls and manages the quality and specify quality requirements.
Data consumer – a regular data user who defines data standards.
Data producer – captures data ensuring that data complies with data consumers quality requirements.
Data steward – in-charge of data content, context, and associated business rules. The specialist ensures employees follow documented standards and guidelines for data.
Data custodian – he manages the technical environment of data maintenance and storage.
Data analyst – explores, assesses, summarizes data, and reports on the results to stakeholders.

Importance of data quality

Getting into business is easy but having a top-level management is difficult. The most important thing for a good business is to have good data with specified quality. As we are changing with digital transformation, the support is focusing on quality of data in a much better way than it was before.

It is a way to show the extent where data meets users standards of excellence or expectations. High-quality data is easy to process and interpret for multiple purposes like planning, reporting, decision making, or doing operating activities.

Data management is very important for the getting better business results.

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  1. Hey loved reading it. Especially the way it is detailed out

  2. Thanks for sharing about quality data management. This will surely gonna help a lot.

  3. I was quite oblivious about this area of industry. Your post has given a nice idea about how it works.

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