The development of quality rules play an important part to the success of data quality management processes because the rules will detect and help avoid compromised data by checking the health of the entire data set. As a result, they will fix all the data inconsistencies.
The concept of data quality management
Data quality management means a set of practices whose aim is to keep the information at a high quality. Data quality management includes acquisition of data, implementing advanced data processes as well as distributing data efficiently. It also asks for a managerial oversight over the information you have. Effective data quality management is truly important to any consistent data analysis, because data quality is essential to generate actionable and correct insights from your information.
There are a lot of strategies by which you can advance the quality of your data. Data Quality Management processes will make a preparation for your company to deal with the difficulties of digital age data, no matter where and when they are going to turn up. In the following post, you are going to read anything that should be known about data quality management.
The importance of data quality management
In spite of the fact that it has been so successful for the digital age to boost innovation further, it has also led to the data crisis of this digital age, which means data of quality.
Regarding the concept of data quality, it means the evaluation of the information you have captured, meaning its purpose and its capability of satisfying that aim. The quality of data is defined using a lot of factors that you are going read, including the accuracy, the completeness, the consistency as well as the timeliness. This quality of data is important to achieve the needs of a company when it comes to operations, making plans and decisions.
Currently, most of an organization’s operations or decisions are being done depending on data, thus data quality is even more important than ever. Actually, data of low quality can lead to failure for improved data and technology solutions. According to the research, about 9.7 million dollars of American business are lost every year because of low data quality. Moreover, data of low quality can also negatively influence the productivity and the total ROI of an organization.
Data with poor quality results in many consequences. However, you should note that the real goal of data quality management is not creating subjective concepts of what high quality data truly is. Its main aim is developing the return on investment for those business segments that rely on data.
From client relationship management to supply chain management or enterprise resource planning, the advantages of good data quality management can lead a significant impact the performance of a company. Thanks to quality data, they are able to form data warehouses for different purposes of examining trends and setting up strategies for future possible issues. According to another survey, over 90 percent of companies are using big data in order to manage their job and are satisfied with the brought results. As you can see, the executives of big companies know understand and agree on the importance of high data quality.
The pillars of data quality management
After you have already understood the importance of high-quality data, you need to take actions in order to build the data foundation. Below are the techniques of data quality management along with pillars supporting it.
First and foremost, technology is only effective if the individuals implementing it work effectively. When it comes to the people, there are some data quality management roles that should be achieved.
The first one is data quality management program manager. The program manager role should be assigned to a high-level leader who takes the responsibility of the overall oversight for the business intelligence initiatives. He should take care of the management of daily activities of the company including data scope, project budget or program adoption. He should lead the vision for both quality data and ROI.
After that we have organization change manager. The change manager is responsible for organizing. He or she will support the company by offering clarity and insight into improved data technology solutions. Because quality problems are associated with using a dashboard software, the change manager is really essential when it comes to visualizing data quality.
Moreover, business or data analyst is also an indispensable role. This person should define the quality demands under the perspective of an organization. These demands are then quantified into data models for both acquisition and delivery. This person or his group should make sure that the theory behind data quality is understood by the development team.
Data profiling is another important process when it comes to data quality management. There are four parts included in this task, which are reviewing data in detail, comparing and contrasting the data to its own metadata, operating statistical models and finally reporting the data quality.
This process is aimed at developing insight to the current data, with the main goal of comparing it to the quality purposes. It supports companies to develop an initiating point in the data quality management process and sets the standard on how to better the quality of their information. The data quality metrics of correct data are a must to this step. Correct data is aimed at disproportionate numbers while complete data is aimed at the data body and making sure that all the data points are whole
Defining the quality of data
The final pillar is the quality of data. In this step, you should build and define the quality rules according to the goals and demands of your businesses. These are business or technical rules with which data will follow in order to be viable.
Business demands are the most important components in this pillar because important data factors will depend on the industry.