How to effectively manage data quality

Companies, regardless large or small size, find it difficult to keep a good quality of increasing volumes of data needed to operate.

Data quality management here does not just mean cleansing bad data in a periodical manner. There is a high demand for good business sense to regard data quality as an important part to streamline and integrate processes. Too old or wrong data can result in many consequences in business decision-making process.

A lot of solutions and strategies have been used by organization to manage quality of data more effectively. An ideal approach regarding data governance and data management can bring about many advantages. A proactive approach for managing, monitoring and controlling data quality is the main key instead of reacting to data issues or dealing with detected data anomalies. Here are some of the key strategies that your company should implement.

First and foremost, you should allow your business to drive data quality. the main aim of data is fueling business. Instead of requiring IT to be responsible for the data quality, the business units which are the main users of this data should be trained carefully to define the data quality parameters. In case business intelligence is related to the underlying data, there are more opportunities of implementing the right methodologies which would prioritize the critical data of the business.

Secondly, it is highly recommended to appoint data stewards. These would be roles who need to define the owners of data quality. Data stewards are those taking control over data integrity in the system. It is a must that data stewards are chosen from the business as they are the ones who know clearly how data is delivered to the particular demands of their company.

There is also a need to formulate a data governance board. This group works with all business functions from data stakeholders to IT. Data stewards should collaborate effectively with the members with these roles. The board makes sure that similar approaches and policies about data quality are implemented throughout the company and deliver into all functions of the company. The board defines new data quality goals, takes measurements and analyze data quality in different business departments by meeting regularly.

Last but not least, establishing a data quality firewall is also an ideal way. Data in an organization will be a financial value, thus it is necessary to check and balance regularly for assuring that the data coming into the systems comes with a good quality. Moreover, every time the data is retrieved or changed, it may be exposed to the risk of being wrong. Bad data can have a big negative impact on the business. Setting up a smart virtual firewall can help make sure detecting and blocking bad data at the starting point from which it comes into the system.

 Best practices to adopt data quality techniques

Data quality management is a process in which every logical step will be implemented. Thanks to this, data management practices can be standardized. In the following section, there are some best practices for adopting data quality techniques.

First and foremost, it is data quality evaluation. This means that you should follow the data of your company to inspect in detail so that you can identify the data quality problems in that environment. A focused evaluation of data quality is truly important in order to know whether data quality is week, which can help adjust to reach the business goals. It also offers a referral point in order to make investment and plan in improving the quality of data and measuring the end results of advancements.

Evaluating data should be done depending on a data analysis regarding its influences. The data that is important to a business should be a vital parameter used to define the scope and priority of the data. This approach can help identify data anomalies and predict their impact on the business goals. This step should be done with a formal report listing down what has been found.

The second practice is measuring data quality. The result from the data evaluation report can help narrow down the area to define the important data elements. The attributes as well as dimensions used to measure the data quality, deciding the units of the measurements along with the acceptable thresholds for the metrics is the foundation to adopt improvement processes. Such attributes as completeness, uniqueness, consistency and timeliness can be defined as an input to determine the tools and techniques should be utilized to reach the wanted levels of data quality. Data validity rules are clearly defined according to those metrics. As a result, data controls will be pressed into the functions which change the data in its lifecycle.

The third step is to incorporate data quality into the functions and processes. Paying attention to setting up the functionality will take precedence over data quality within any app development or system upgrade. The metrics in the previous step can be utilized to put data quality goals into the system development life cycle. Data quality analysts should identify the data demands for every application. A traversal of the data transfer in every application will provide insights into the insertion points to inspect data and manage routines. These demands should be sent to the functional requirements of the system to incorporate seamlessly into the development cycle. As a result, data can be validated before sending into the system.

Data quality improvement in operational systems

Data which is shared between the data vendors and users must subject to the contractual agreements in which the levels of quality are clearly defined. The data metrics can be incorporated into these agreements.

Thanks to defining data standards, data flow can be made smoother from one company to another. Last but not least, the metadata can be put and used under a repository in order to make sure that data is represented in a beneficial way to both collaborating parties.

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