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Understanding DQM

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  • Last Updated : 19 Jul, 2021
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Data Quality Management (DQL) :
In this rising generation every thing is based on data, a small data also plays an important role in the whole system.
Data quality management or DQM is a dataset which we get in an organized manner in which the user can have access to that data accordingly. In which a bunch of data with right people, the processes, helps with a better decision-making for the technologies.

How does it works?

  1. Definition –
    In this we have to define the business goals for the improvement of data quality such as stakeholders, impacted business processes and data rules.
  2. Assessment –
    We have to assess the existing data of the customer which are having unique values of key attributes.
  3. Analysis –
    All the assessed data should be analyzed properly with accurate values.
  4. Improvement –
    On the basis of analysis, we have to develop the data plans in case if needed.
  5. Implementation –
    For a successful dataset we have to implement all the above process, and it is also an important part of the process.
  6. Control –
    The process of controlling the accessing of data for analysis.

Roles & Responsibilities of Data Quality Management :
It varies from the user how they work in their industry or organization. The role is to provide the accurate data and ensure the needs of the user in an organized manner. The determination to fulfill the aspects of the customer needs for the long time correctly and in a secure manner.

Needs of Data Quality Management :
It is very essential for the making good business organization. It builds a foundation for the organization to proceed further will and also allows us to understand each and every information in a firm manner. The organized data leads to well-being of an organization and for better decision-making.

The more accurate data we have then less business crises will occur.

Base of Data Quality Management :

  • Data Strategy and Governance
  • Standards
  • Integration
  • Quality

Dimensions Used to Measure Data Quality :

  1. Accuracy –
    Accuracy matters the most, an organization can never deal with an incorrect data, nor we should provide wrong information to anyone or anywhere.
  2. Relevancy – 
    The collected data should fulfill the needs of the organization or the customer. It should be relevant accordingly.
  3. Completeness –
    We cannot provide someone the missing data. If the required data is provided then the optional data can be left incomplete.
  4. Timeliness –
    This refers to the updated data. We might not sometimes update our previous data through which we can get trouble with real time data. To support the policy system timeliness is important.
  5. Uniqueness –
    The duplicacy of dataset is not acceptable by any system. Each data has their own identity. In business there is a requirement of data survey and duplicate dataset can lead to chaos to the whole system or organization.
  6. Consistency –
    The consistent view of the user  which matches with their real time data.

Initiatives to Maintain Data Quality Management :
Irregular and bad quality data is never relevant for the user. For improving the quality of data, the  data life cycle is responsible. It includes several processes, understanding, strategies, technologies to implement it.

It requires several steps :

  • We have to define our dataset. How the data works?, what the data is related to?.
  • Searching for the errors in a bunch of data is never being so easy. Sometimes we cannot catch the errors quickly for that we should analyze the data from the base, where the data have come from?, like the purpose of sending unwanted data from outer source.
  • However, the accurate data looks, but sometimes we often make an error. The dataset should be provided error free. To measure the error there are some tools which helps to make our work easier and prevent us with the further crises.

Tools for Data Quality Management :
These are some list of platforms for data quality management.

  • SAP

However, it seems very difficult to maintain high quality data but to build an organization it is important and to  prevent the big chaos. With a good, accurate high quality dataset the business can run perfectly without any hardship and can get profit constantly.

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