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Best Practices for Successful Data Quality

According to the Data Warehouse Institute, "Data Quality problems cost US businesses more than 600 billion US dollars a year." And this is where data quality best practices come into play; they help to minimize the negative impact of poor data quality. Data quality best practices help in maximizing data quality by ensuring that the data is maintained in such a way that it helps organizations to meet their goals.

Profiling: Also known as data assessment or data discovery, is a set of methods and tools used for collecting statistics and findings that are related to data quality. Profiling tools collect these statistics by assessing various aspects of data such as structure, content, and so on. With the help of these, organizations can pinpoint problems and challenges related to data quality. There are several profiling options such as pattern analysis, range analysis, completeness analysis, and so on that help to improve data quality.

Buy-in: Getting approval and buy-in from all stakeholders is a must. Making the aspect of data quality an integral part of the corporate culture is needed to ensure each and everyone involved in the process is accountable for doing their part perfectly, and each and everyone will be equally responsible for data hygiene and quality successes and failures. Such a strategy will prevent stakeholders from playing games such as finger-pointing, passing the buck, and so on.

Data Stewards: A data Steward's main aim is to preserve data quality and integrity. Usually, data stewards are assigned to data sets that they maintain in terms of quality and integrity.

Compliance: Using data quality monitoring tools and auditing processes can help companies meet not only compliance standards and mandates but also ensure data quality standards and safeguards against potential data leaks are put in place. Frequent, incremental audits are critical to capture data quality anomalies in a timely manner. They help to pinpoint inconsistency, incompleteness, inaccuracy, and so on in the datasets in a timely manner.

Eliminating duplicate data: Duplicate data identification tools are needed to be used to bring about better consistency and accuracy of data. The concept of master data is very important in minimizing duplication of data.

Metrics: There need to be clear and comprehensive metrics for evaluating the whole data quality paradigm.

Governance: Data governance is a framework of policies, guidelines, and standards that needs to be made a part of the corporate culture to establish data quality standards as an integral part of the workplace DNA.

Training and certifications: These aspects are important to understand the deeper dynamics of data quality in terms of tools, processes, techniques, principles, and practices.

Cloud computing: It helps to integrate multiple data streams in a seamless manner, which means fewer errors in the data. By adopting a cloud-native solution and moving all data quality tools into these solutions, it becomes easier for organizations to adopt these tools and implement centralized reusable rules management and preloaded templates across all data sources. These tools help in building integrated data pipelines so patterns, insights, trends, and so on can be got from the cloud itself. Newer hybrid cloud technologies such as cloud containers, data warehouses, and so on pinpoint, correct, and monitor data quality problems in an efficient and effective way, thereby introducing better data quality standards and practices.

Did you know - According to Gartner's 2016 data quality market survey, the impact of poor quality data on the average annual financial costs of organizations worldwide increased by 10% in 2016. It rose from 8.8 million US dollars in 2015 to 9.8 million US dollars in 2016.

Hope this was helpful.

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