Big data has taken over the world. The estimated revenue from the big data market is poised to reach $273.4 billion by 2026. In the modern world, we are generating more data than ever before with varied volumes, variety and velocity. Hence, one of the key issues that organization encounter due to the growth of data is poor data quality.
Poor data quality in big data can lead to inaccurate or incomplete analysis, which can result in poor decision-making, wasted resources, and lost opportunities. Inaccurate data can also lead to poor customer experiences, compliance issues, and reputational damage.
Many of organizations have invested in data quality tools available in the market. They spend a significant amount and time trying to detect and report data quality issues. But, does that solve the larger problem of how organizations should think of avoiding such issues in future. No! Everyone understands the importance of data quality, but identifying data quality issues is only half the problem. The real value is delivered when you can fix the data quality problem. In this blog post, we’ll explore how to fix data quality issues in the modern world.
Let us start with the problem itself. How do we identify what is the causing the data quality issue. There are several categories like human entry error, coding issues, data entry mistakes, data integration issues, inconsistency etc. While checking all these errors would seem approachable if the data volume is low, but with companies gaining access to more and more data in the number of billions and trillions, it becomes imperative to build processes and use market available data quality tools to prevent data quality. One of the key points that most of the data quality practitioners stress is correctly data quality at source.
Implementing data quality rules to identify issues is an important step to identify data quality issues at source. With automated data quality tools available in the market, data can easily be extracted, cleansed and profiled to identify any anomalies with the data. The next step involves fixing the data quality issue. Before fixing the issue, it is important to understand what is critical for the business. Hence, identifying critical data is important to have focus on the key data elements that affect the business and ensure their quality. Ensuring the quality of critical data elements is crucial because decisions made based on inaccurate or incomplete data can have severe consequences for the organization.
Now, coming back to fixing the data quality issue. Fixing means going back and correcting the wrong data. However with billions of data around, it becomes difficult to understand where the issue originated and then fix the issues. Such investigation and action require a robust data quality tool. In the market, there are lot of data quality tools, but mostly cater to identifying the issues. However, DvSum provides a robust solution to identify, manage and fix data quality issues.
DvSum’s Agile Data Quality Solution
DvSum’s platform leverages machine learning and AI to identify and prioritize data quality issues based on business impact. It also provides collaboration and workflow capabilities to engage data owners and data stewards in the data quality process. Once data quality issues are identified, data owners are notified to review and remediate the issue. They can use the platform’s built-in data cleansing tools or integrate with their existing data management tools to fix the issue. Once the data is cleaned, it is automatically written back to the source system.
DvSum’s Data Quality platform provides businesses with a powerful solution to improve and fix data quality issues. With its end-to-end data quality management capabilities, businesses can easily identify, prioritize, and remediate data quality issues. Businesses can increase revenue, reduce costs, and improve decision-making by improving data quality.