Observe your data quality and make decisions based on facts

In today’s marketplace, businesses generate and use massive amounts of data, collected from various sources and in different formats. Without proper organization and management, this data becomes confusing, inconsistent, and meaningless for sophisticated business applications respectively the users of these. Ensuring data quality is a critical aspect of good data governance practices and one that is often overlooked. Data quality testing is the process of examining and validating data integrity, accuracy, and consistency, whereas data governance is the overall management and control of a company's data assets. In this blog, we'll discuss the importance of continuous data quality testing for better data governance.

For organizations to effectively use data for improved daily operations, strategic decisions and building beneficial business applications, it’s crucial to harness the perspectives of both data minds and business minds.

Data minds, who are primarily data engineers, are responsible for producing data infrastructure and products that can later be consumed by business or end users. The latter are the business minds who use the data to create and launch digital services, which offer the users the right data in the right amount for the specific use case in order to achieve the best user experience. However, their difference of roles also means that their viewpoint on data quality is unique, as well as the way they measure and make observations on data. Nonetheless, these perspectives need to come together in a collaborative effort to achieve better business outcomes and create a more data-driven organization. This support for various personas is foundational and essential to an organization's success.

dcupl is a data quality platform and management tool in which different stakeholders, whether data analysts or engineers or business decision-makers such as CIO, CDO, etc., receive a constantly updated view of the integrated data resources and thus a picture of the data quality in the company. With this knowledge, the feasibility and implementation of different use cases - whether B2B or B2C - can be soundly evaluated and decisions are made on the basis of facts. The focus of dcupl is to establish business “fit-for-purpose” checks across your ecosystem and system landscape irrespective of cloud or on-premises and dynamic or static resources.

Benefits of Continuous Data Quality Testing for better data governance:

• Consistent Data: Continuous data testing ensures data consistency, accuracy, and completeness across all business operations, even as new data is acquired.
• Increased Trust in Data Quality: Continually testing your data gives organizations visibility on the value and credibility of their data assets, increasing trust in data and improving decision-making processes.
• Improved Customer Experience: Continuously testing data quality generates insights that help companies better understand customer behavior, preferences, and interests, leading to increased customer satisfaction and loyalty.

Continuous data quality testing also helps you identify problems and resolve them before they become major issues respectively lead to errors in applications or poor user experience.

Implementing a continuous data quality testing process may seem daunting, but it doesn't have to be. Here are some tips to get you started:

[object Object]0. Start by defining your data quality standards and objectives. Decide what data is most important to your business and set a standard for the level of accuracy you expect. Using dcupl you “describe” the quality level of your data in the model descriptions. [object Object]1. Identify the sources of your data and determine how to integrate them into your data quality testing process. As you know, you can connect dcupl to dynamic (APIs, services) and to static (CSV, JSON, …) data resources and set up your models in no time. [object Object]2. Choose the appropriate data quality testing tools and techniques. There are many tools and techniques available, such as data profiling, data cleansing, and data validation. With dcupl you can profile and validate your data, data cleansing has to be done in the source systems or your static resources. [object Object]3. Develop monitoring and reporting processes to ensure that your data quality remains consistent over time. dcupl monitors your data and sends alert notifications to your defined contacts.

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Challenges with data quality testing and how to overcome them

One of the biggest challenges with data quality testing is the complexity of data infrastructure, which can make it difficult to identify and resolve data quality issues. To overcome this challenge, businesses should invest in data management tools and technologies that can automate data quality testing and monitoring - like dcupl. Additionally, businesses should develop data quality testing processes that include collaboration between IT and business units, ensuring that data quality issues are identified and resolved from multiple perspectives.

dcupl users monitor the data deliveries and thus ensure the data quality in the applications and therefore the full functionality and user experience.

• For example, product data is monitored and checked with regard to quantity, missing prices, release characteristics for eCommerce, etc. - initially within the development and integration process and continuously according to definition in production.
• Content blocks in your application are zip-code specific? Set up a data quality rule in dcupl and monitor the amount of filled zip-code properties in your data.
• Is it business-critical if a technical value outside the permitted limit range is listed in the product catalog? Simply define the value range in the model and check it continuously with dcupl. This way nothing can go wrong!

Conclusion

In conclusion, continuous data quality testing is essential for businesses to maintain accurate and reliable data. By implementing a continuous data quality testing process, businesses can subsequently improve the quality of their digital services, whether it’s about the digitalization of inhouse business processes or digital transformation projects for end users.

dcupl does not aim to monitor data in real time and intervene when it is generated. dcupl specializes in monitoring integrated data for specific use cases (“dcupl applications”) and initiating appropriate processes for remedying problems.

By prioritizing data quality, businesses can leverage the full potential of their data and unlock its true value.

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