Data Quality Management, first step is to measure of how fit a data is to serve specific needs of business. Quality data is always useful. Business leaders are gradually becoming aware of the importance of the quality of data and its impact. However, if data is not complete or consistent, then it can lead to major issues while taking business decisions. Data Quality Management (DQM) is a set of practices which aims to maintain high quality data. It requires a culmination of correct people, processes and technologies that can not only upgrade data quality, but also improve the business as a whole.
Prerequisites for an Effective Data Quality Management
Data Quality Management is an inevitable part for any data-driven business. Effective management of data quality calls for a strong foundation. There are many factors to it:
Organizational Structure
In order to maintain an effective quality management, we look forward to many roles such as DQM Program Manager who keeps an account of day-to-day quality management. Then comes Organization Change Manager who makes decision about data infrastructure. We also need help from Business Analyst who interprets the available data.
Data Quality Definition
In absence of data quality definition it becomes a bit difficult to maintain or retain it. Definition of data quality varies across organizations. The characteristics which helps define data quality are: its completeness, denoting how much of the data has been acquired. Accuracy of the data is of immense importance. We should also check the integrity of data. The data should also be consistent throughout similar databases.
Data Profiling Audits
Data Profiling ensures the quality of data. Here, data is validated against meta-data and other standard measures. A routine check ensures the quality of data to stay ahead of competition.
Correcting Errors
You must make the data error-free, remove duplicates, treat for missing value before processing data.
Importance and Benefits of DQM
The goal of data quality management is to improve and update the data in such a way so that it does not prove to be a hindrance in business. Low quality data can make a business unstable. DQM aims at helping companies point out data discrepancies which can be resolved.
When the quality of data is good, it is easy to process and analyze. It improves organizational efficiency. Properly processed data can deliver more meaningful insights.
Good quality data is essential for cloud computing, proper visualization, AI initiatives and likewise.
When the data is managed, teams can focus on monetary action items instead of cleansing of data. Increased accuracy leads to rise in revenue and also a competitive edge over others.
Business leaders utilize Big Data for high volume and velocity of data. This also implies that if business risks are there, it needs proper management. Thus, here the DQM comes into play. Data Quality Management is an ongoing process that requires continuous monitoring and reporting. The key is to build a solid data architecture team that will help develop a proper framework and help in data designing. Good planning at regular intervals to keeps the data fit for use.
