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 In Data Quality

data quality definition

Data quality is a complex method of measuring data properties using several different points of view. Discovering if data is of high quality typically involves:

  • Finding out if data is fit for use for its intended scope,
  • Finding out if data conforms to the standards your organization is using.

The above criteria have an important role in connecting the employee’s work to the client that is buying your product. Only when used together, it is possible to build efficient systems that meet the organizational accuracy and quality standards and appeal to new and existing customers.

Data Quality Characteristics

With the cost of storage and computing continuously plunging, the number of databases a typical organization uses has significantly grown. Thus, many companies realized that, in order to get useful results out of their data sets, they need to start improving the quality of their data. As of now, there are several widely accepted characteristics that describe data quality:

Relevance

To be relevant, data must meet the needs for which it was collected. It is important to note that, in order to be that sure data is relevant, you have to perform periodic reviews of your requirements. These reviews must reflect all the changing needs of your organization.

Accuracy

Accuracy represents the degree to which data sets represent the characteristics of the described real-world object or event. For best results, you must capture data at the point of activity.

Timeliness

It is related to whether data is indeed available when it is needed. The timeliness of a dataset is tightly related to the user’s expectations.

Reliability

Datasets have to echo consistent processes across all collection points and, most important, over time. You should make sure that data sets variations to reflect real changes rather than fluctuations in your data collection method.

Completeness

Completeness simply means that no data records are missing. It also means that no data records miss data elements. To achieve data completeness, you should clearly define your data requirements. Furthermore, you have to specify these requirements based on the information your organization needs.

Validity

You should record, store, and use data only in compliance with relevant legal and organizational requirements.

Conformity

Data must follow a set of organizational wide definitions such as type, format, and size. You should continuously enforce conformance to specific formats.

Integrity

You must always be able to trace and connect datasets saved in the database to other data. For a customer database with customers and addresses, you should be able to track all addresses to customers. Otherwise, the record is considered orphaned.

Why is Data Quality Important?

As many organizations are on the verge of adopting disrupting technologies, like artificial intelligence, IoT, or automation, their ability to differentiate is most likely going to depend on their capacity to get data quality in the right manner. With the current exponential growth in data, there is also a great opportunity in exploiting it.

Data quality brings few key benefits to companies:

Improved decision making

With better data, your employees are more efficient and confident in the outputs they are producing. Reliable inputs do not leave room for guesswork in decision making. This increases efficiency and drives better business results.

High productivity

Instead of fixing and validating data errors, your employees will focus on their jobs. Thus, quality data will make your company more productive.

Regulatory compliance

In many industries such as finance, you must maintain good-quality data in order to avoid significant fines. This is an ongoing activity. Existing regulations are constantly modified and new ones are imposed, like GDPR.

Marketing

More accurate data means targeted communications that bring better leads and more conversions. On the other side, if your competitors are able to get more insights than you, then you are going to miss important market opportunities. Thus, you must consider data as an important asset and manage its quality. It is the best way to gain competitive advantage.

Conclusion

Without data quality, you can’t understand your market and you can’t either successfully communicate with your customers. With the right tools, it is easy to find relevant information about current and potential customers. In turn, with this information, you can improve your marketing and sales processes and increase conversions.

The Advantages of WinPure Clean & Match

WinPure Clean & Match is the leading solution for cleaning, matching and deduping your lists and databases. Furthermore, it is both affordable and very intuitive. Its new Data Cleaning Matrix contains an array of tools you can use to clean, correct, standardize and transform your data. Once set up, you can save your preferences and use them with other similar data sets. This is going to save you significant amounts of time. WinPure Clean & Match also includes a powerful data profiling tool designed to provide more than 25 different statistics. Our solution is also able to highlight potential data quality issues with different colors which will help you quickly identify any problems.

Why not download a free trial of our award-winning WinPure™ Clean & Match?

 

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