If there is one component of a business that needs to be as efficient as possible in order to ensure smooth functioning, it is proper data quality management. Data quality can directly impact a company’s bottom line and cause potentially irreparable damage if the data that is being used is not accurate.
In simple terms, the quality of the data refers to the level of reliability the data has in order to achieve a goal in a particular context. The quality of data is usually high if it is fit for use in decision making, planning and other important business operations.
Managing this quality is vital for every business, and all companies should adhere to certain steps in order to ensure that the data being used is as viable as possible. Here are seven business skills that all organizations should know about maintaining high quality data.
Completion
A pillar of low quality data is data that is incomplete. Data contains critical information about certain operations, and it needs to be complete in order to use it properly. If data is incomplete, this will lead to frequent misunderstandings and potentially critical errors. Frequent quality checks need to be made on the data itself, but one of the priorities needs to be ensuring that the data is not missing any important information first.
Profiling
Improving the quality of data also entails profiling the data itself. This is frequently referred to in the business world as “data archeology”. This entails analyzing the data for uniqueness, consistency, correctness and completeness. In profiling, this will allow a company to leverage important data functions and will make a final assessment of the quality less stressful.
Cleansing
Cleaning operational data is very important, especially in cases where the data is being used in cross-organizational reporting. When cleaning the data, it is important to concentrate on critical elements of the data and to leave anything that insignificant unchanged. The data does not need to be cleaned all at once because of potential time constraints, but the elements of the data still have to be classified in order of importance.
Normalization
Normalizing the data is critical because the data that a company uses can originate from a variety of sources. This can be easily interpreted as different data points altogether. A point of standardization needs to be established as well as a singular approach so that redundancy is reduced as much as possible.
Metadata Management
Managing the quality of data also entails managing the metadata. As the sources and variety from data begins to grow, different sections of an organization can easily misinterpret certain concepts of data. Therefore, managing the metadata will help alleviate any potential confusion and false interpretations. This will also help to establish future corporate standards.
Data Quality Firewall
Building a firewall is essential to protecting data. In a firewall, specialized software is used to ensure that the data is as error free as possible. A firewall will also help to reduce the potential redundancy that comes with the data being used. Seeing as data is one of the company’s biggest assets, every effort should be made to preserve its viability, and a firewall is an effective way to go about this.
Consolidation
Finally, consolidating data helps avoid companies make a common mistake. That is to use multiple systems to track the data’s source. Separate systems can prove to be very costly. Consolidating the data into a single system will help control the data’s integrity better.