Quality data that is trusted, accurate, complete, and reliable is a must for the enterprise of today, especially for those working with big data solutions. In some cases, data quality entails the success or demise of the business itself.
And if the business has a data warehouse setup for its data management system, where data gets to be structured and organized, quality is all the more critical. Ensuring data quality in a data warehouse offers important benefits such as:
Improved decision-making - With good data quality, decision-makers can gain insights that are relevant and reliable, which enables them to make swift and sound decisions that make an organization agile and competitive amidst an ever-changing business landscape. In addition, high-quality data ensures that everyone in the organization is on the same page, reducing the risk of conflicts or misunderstandings between departments.
Increased efficiency and productivity - When data is accurate, complete, and consistent, it means that there are fewer errors and discrepancies in your reports and analytics. This enables decision-makers to spend minimal time on data verification and be able to analyze data more efficiently to come up with effective decisions that enhance productivity and competitiveness. It also allows for better streamlining of workflows and empowering teams within the organization to deliver better results.
Enhanced customer satisfaction – Quality data ensures the business’ ability to provide better customer experiences as people expect businesses to have accurate data of all their interactions with them, and this is especially important, especially in the case of customer support. With good data quality, businesses can ensure that they have reliable information about their customers and provide better services and products, leading to higher customer loyalty and brand recognition.
As such, data stakeholders should be on the lookout for various factors that might affect the quality of the data contained within the data warehouse. There are at least four main sources where data quality issues can potentially arise:
Data sources – They can come in different formats, standards, definitions, and quality levels and can change over time and if these differences are not reconciled, this can adversely affect the data in the warehouse.
ETL processes – These are responsible for moving and transforming data from the source to the warehouse and are prone to design flaws, technical errors, or human mistakes which can overall affect data quality.
Warehouse design – Data quality issues are bound to occur if the data warehouse is designed with unclear or conflicting specifications or objectives, or is built on inappropriate or outdated architectures, technologies, or methodologies.
Users – Each user has different expectations and interpretations of the data and may behave unethically or incorrectly, and these can affect how data is to be presented.
Ensuring data quality is an admittedly challenging endeavor but it is something every organization must maintain if it is keen on remaining competitive and achieving growth in the long run.
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