Master Data Management

Big Data: 4 Ways to Fix Your Data Quality Problem

Big Data can fail you. Inefficient data costs your company valuable time, resources, and, most importantly, revenue. Furthermore, it can introduce your organization to a heightened risk of errors and uncertainty. So, how do you manage data’s inherent complexities? In this article at DZone, John Vester explains how to rehabilitate data quality when it goes bad.

How to Improve Big Data Quality

“The lack of the data quality will always have a catastrophic impact on systems used for data-driven decision-making,” says Vester. Here are some key tips that will help you improve your data quality:

Learn How Data Quality Impacts Business Decisions

As an IT leader, you must identify the link between key performance indicators (KPIs), data assets, and business processes. Then, make a list of the existing data quality issues you face. Learn how it is impacting your business revenues. Once you have identified data quality issues, create an improvement program that vividly defines the scope, the list of stakeholders, and the investment plan.

Set a Standard for Data

To improve big data quality, you must understand your business needs. You must have regular discussions with key stakeholders and identify their expectations. Different companies have different standards. Therefore, undoubtedly businesses have different expectations for the data quality improvement program.

Implement Big Data Quality Dashboards

These dashboards offer a holistic view of data quality to all the stakeholders. You can use the data quality dashboards to compare the data’s performance for various business processes. The analysis will help you make the right decisions to achieve the desired organizational objectives. Remember, you can also customize the data dashboards to meet your specific needs.

Highlight Data Quality at Governance Board Meetings

Link data quality initiatives to business outcomes. This will help the C-suite executives track the investment in data quality improvement against the business objectives. Keep board members regularly updated on data quality improvement progress and challenges.

Are you curious to learn more about improving your big data quality? Click on to know more.

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