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Why Data Quality Matters for Your Data Reports & Visualizations

Publication date: 4 October 2021

Most companies base their decisions and plan their future goals according to the data they gather. However, what determines the success of all these business decisions and goals is the quality of the gathered, stored, and consumed data.

To meet the requirements of its intended usage for clients, decision-makers, downstream applications, and processes, business intelligence must serve a purpose. 

Data quality is a fundamental attribute as data is powerful enough to impact various aspects of a business, including regulatory compliance, customer satisfaction, or accuracy of decision making.

So, how can companies ensure they are dealing with quality data only?

Data quality is usually measured by:

  • Accuracy. To be considered high-quality, data needs to be accurate.
  • Relevance. Data should meet the requirements for the intended use.
  • Completeness. High-quality data should not have missing values or data records.
  • Timeliness. Data should always be up-to-date.
  • Consistency. Quality data should have the data format as expected. Also, it should be possible to cross-reference data with the results.

Facts and figures play an incredibly significant role for many businesses. However, those in charge of making decisions are not the ones who gather and analyze data. Instead, data is ‘presented’ to them – most often in the form of tables, graphs, or charts. For that reason, it is important to collect the most vital data and visually represent it so that it is understandable.

Improved data quality (and understanding) are very likely to lead to better decision-making across a company. You can have more confidence in your decisions, reduce risk, and ensure consistent results improvements. 

Here are five best practices for improving the quality of your data:

  1. Involve top-level management. Sometimes, data quality issues are easily resolved by having a cross-departmental view.
  2. Keep a detailed account of all data. Operate a data quality issue log with an entry for each issue with detailed information.
  3. Start at the beginning. Begin with a root cause analysis for each data quality issue raised. Data quality issues can only be resolved if the root cause is addressed.
  4. Link data to your KPIs. Define data quality KPIs that are linked to the general KPIs for business performance.
  5. Optimize the data management process. Instead of relying on downstream data cleansing, develop processes and technologies that prevent issues from arising as close to the data onboarding point as possible.
If you are still unsure how to efficiently handle the data your company collects, TRUECHART is here to help out. We help revolutionize industries and solve data collaboration problems efficiently along the entire value chain. Our solution supports best-in-class visualization, data-driven communication, and platform independence.

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