Data Quality Management in its most simplistic sense refers to the management of resources such as persons, systems or procedures to ensure integrity in terms of capture, utilization as well as the recycling/maintenance or disposal of data. Some of the key criteria deployed in terms of the gauging the quality of data are:
Accuracy in terms of how correct or error-free the data is.
Relevance in terms of how important the data is.
Accessibility in terms of how easy it is to obtain and review the data.
Timeliness in terms of how up-to-date and how quickly the information is distributed after the fact.
The Importance of Data Quality Management
Data are a crucial resource to any business, seeking to gain an understanding of its’ effectiveness, competitiveness as well as profit value within the scope of its’ unique products, services, customer base as well as market penetration. Every aspect of business is shaped or driven by the quality of data available to it at any given time. Take the example of a company selling multiple digital products online to a target customer base. Data on customer buying habits and behaviors are crucial to the business being able to make key decisions based on understanding which products are selling well and why, which products are NOT selling and why, estimating future trends as well as being able to predict and modify sales or marketing behaviors to improve profitability. It’s important to note here that having the wrong data in place can lead to companies losing revenue over incorrect decisions taken for all the wrong reasons. Therefore having quality data available at the right time, at the right place and in the right manner is key to every business owner seeking to make an impact. It is in this context that the management of data in the form of Data Quality Management assumes prime importance.
Data Quality Management as an ongoing process.
Data Quality Management is an ongoing process of continuous development. For any organization to continue to remain competitive, it’s important to continuously assess and review Data Quality Management processes to ensure best practices. For new businesses who have yet to establish any type of Data Quality Management processes, it is vital to create and set up appropriate measures right from the very beginning to prevent unnecessary loss of revenue as well as time and resource expenditure on redoing existing processes at a later stage.