5 Critical Steps To Achieve Trustworthy Data in your Data Warehouse

5-Critical-Steps-to-Achieve-Trustworthy-Data-in-your-Data-Warehouse

Data Warehouses provide a comprehensive view of data across many different sources, and proper analysis can encourage better business decisions and problem-solving. However, building and maintaining an effective data warehouse requires careful thought and consideration to ensure that it is reliable and can withstand the business demands of the organizations that rely on it. The biggest concern for data warehouse users is whether they can trust the information it produces.

They can use the data to make informed decisions and implement successful business strategies if the data is trustworthy. But if the data quality is terrible, stakeholders risk making bad decisions that lead to unnecessary expenses, wasted effort, staff management issues, and many other negative impacts that directly and indirectly hurt the business.

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Knowing the rewards and risks involved with data accuracy, the development team must devote a large part of their time to building effective data warehouse testing methods. Many organizations do not follow a consistent or comprehensive approach to testing the quality of their data warehouses.

They are often overwhelmed by the complexity of managing multiple data sources and all the potential issues that could arise throughout the development process. By taking a systematic approach to testing a data warehouse, developers can continually address challenges as they arise and improve the quality of their data.

Here are five critical tests that should be used during development to ensure high-quality data in data warehouses.

number-one

Ensure that ETL processes run reliably by continuously monitoring the data flowing into the warehouse. This type of testing is essential for large projects with many moving parts and complex dependencies between the different datasets. For example, suppose you are working with a data warehouse incorporating data from multiple source systems, perhaps storing similar data but with varying transformation steps. If you analyze your dataset without monitoring the results throughout your development cycle, you may find that output values are incorrect or might be missing altogether. As a general rule, the longer you avoid monitoring the data for accuracy, the harder it becomes to trace back to the root source of the data and identify any fixes. To overcome this, tools that continuously monitor the source data during extraction, transformation, and loading (ETL) will ensure that the data is appropriately captured, allowing you to quickly identify anomalies in the results that require further investigation.

2

Make sure that the output of your transformations is accurate by running validation procedures after each transformation step. For example, if you modify your inbound data multiple times from the source to the target, you should validate the expected data after each transformation. By ensuring these validations, you will be more focused on the small details you might otherwise overlook. This test will allow you to identify any problems more efficiently and to ensure that they are corrected before delivering the new data to the warehouse.

three

Execute cleaning and scrubbing procedures against your incorrect data. Many source systems, especially those in use for an extended period, can contain a lot of noise in the form of incomplete or inaccurate data. For example, a CRM system might have customer records with old addresses that are no longer valid, while other records might include outdated or incomplete contact names. Although ETL test cases assume that data remains unchanged from source to target, ‘dirty’ data can skew the results of your analysis and lead to unreliable results and inflated statistics. By building tests that identify the most common inaccuracies and transform them into the correct values, you will ensure that your warehouse incorporates higher-quality data into its operation.

four

Review your source system processes and configurations regularly to identify potential impacts caused by database schema changes. For example, business logic changes might require source systems to track new fields in your data warehouse, such as marketing codes or business segments. If you miss the downstream updates, you might inadvertently include invalid data in your warehouse. In addition, the relationships identified between your tables often change as new data is added and removed., causing records to be overwritten or corrupted. Regular reviews will ensure your source systems capture the correct schema modifications before they affect the quality of your data.

number-5

Verify the rules of your data warehouse regularly by adding unique Test Cases across the various source systems. While you ideally want to map accurate data from start to finish, including specific exception logic to ‘fix’ insufficient data, you should also include deletable source test data to verify the accuracy of your transformation steps and identify any errors that might occur in the data on its journey into the warehouse. For example, you might want to add a duplicate record on a field that might never show duplicate records to see how the system performs, or you might deliberately create an undefined lookup value to demonstrate how this error is handled. These results will help you confirm that the warehouse can process the output from the source systems under all conditions and also will provide an accurate representation of the enterprise data when the time comes.

Although these five test recommendations may seem straightforward, designing them can be complicated and time-consuming without the benefit of a specific starting point. At the very minimum, begin with a flexible testing approach that focuses on validating the accuracy of the data delivered to the warehouse for one ETL testing use case. Then add more complexity to your scripts, incorporating the other suggestions. Remember that your ultimate goal is to create a solid test plan that produces reliable data at its core and will deliver reliable and meaningful results for your stakeholders. How you reach this goal does not matter as long as you use proven, practical approaches.

Fortunately, there are several tools that you can use to accelerate your test development efforts. Whether you are building data warehouse tests from scratch or building out legacy scripts that have been in use for years, DataGaps and its suite of high-performance testing tools can help you identify data quality issues and build a robust testing framework that you can replicate and reuse throughout the project lifecycle. For example, with the DataOps Suite, you can streamline the ETL testing and quality assurance process while empowering your team to easily create and manage thousands of test cases on the fly.

Datagaps DataOps suite has a library of ready-to-use adaptors to popular data sources, such as Talend, Informatica, SAP, and Oracle that makes it possible to automate complex test data workflows without writing a single line of code, providing an intuitive interface that allows you to build, test, and deploy pipelines with relative ease. You can generate complex data test scripts and documents quickly, guaranteeing your users receive the trusted information they need to make better and more informed business decisions.

To know more, request a demo now!

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Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

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