As companies explore efforts to get themselves ahead in their respective marketplace, all signs point back to high-quality data. It’s important to make sure that sensitive information is kept secure, but also that a database contains information that is accurate and pertinent to an organization’s past, present, and future. The quality of your data can be maintained in a variety of ways, so it’s important to know the examples that are out there to deal with any future hurdles that a company may run into.

The Importance of Data Quality

“Data quality” refers to when data fits the purpose that it was intended for. The data is considered to be of high quality when it accurately represents real-world constructs. Information often needs to be looked at through the context and actions regarding what you turn to this information for within business processes. Data quality examples are prevalent across various industries, transforming data sources into better formatted and secured information that can uncover remedies to problems that companies are dealing with.

For example, marketers rely on the best quality of data to avoid any duplicates. Duplicates in information can result not only in inaccurate data but overspending of the marketing budget. The same database may receive marketing materials with slight variations. This leads to frustration in creating and maintaining customer profiles. A supply chain relies on high-quality data to automate business processes to maintain standards and precision through machine learning. In financial reporting, inconsistent data can be detrimental to proper queries and accurate information for governance standards.

Dimensions of Data Quality

As business users work to improve the quality of data, the main task is to work on enhancing the range of data quality dimensions. These dimensions break down into six primary standards: comprehensiveness, consistency, accuracy, format, timeframe, and integrity. Comprehensiveness refers to the need to fill essential fields in a dataset. Consistency involves issues and hurdles within datasets, as low data quality can cause problems across different departments because of a lack of clarity regarding these data entries. This slows down business intelligence and machine learning capabilities.

Consistency is necessary to ensure a single value across all channels. Meanwhile, accuracy deals with those values being correct across different data sources. The format ensures that entry is consistent across data input, creating a singular standard to maintain quality attributes. Timeframe refers to the effectiveness of data sources that are current and relevant at the time of a query by business users. With current data, decision-makers are able to make timely choices. Integrity is a final trait of a data item to make sure that rules and standards are adhered to in order to get the most out of these data values.

Benefits of Quality Data

At the end of the day, high-quality data facilitates better decision-making across all industries. In a consumer-centric market, businesses are able to spend more time on data quality measures to benefit their customer base and internal mechanisms. That way, organizations can better understand their consumers, while also creating better team collaborations across data sources and different systems throughout a business.

By having a correct format in place to suit the needs of your company’s various sources, there’s more effective data quality management for proper assessments. With subjective assessments, organizations can measure how stakeholders, analysts, and other parties perceive data quality. This helps to find inaccuracies or incomplete data. Companies can also take objective data quality assessment to look at measurable indications, which are recorded with a dataset then evaluated based on the performance of a specific task and a metrics-based dataset. Quality data at your fingertips is truly a game-changer for any business.

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