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Data Quality is critical to making accurate, informed decisions. And while all data has some level of “quality,” various characteristics and factors determine the degree of data quality (high-quality versus low-quality). Furthermore, different data quality characteristics will likely be more critical to multiple organizational stakeholders.

Managing data across the enterprise

Accessing and monitoring that data across internal, cloud, web, and mobile applications is a big task. Data integration is the only way to scale monitoring across all systems. But data integration by itself is not sufficient.

A proactive approach to data quality allows you to check and measure the level of quality stored in any data set. When connecting different data sources, data profiling is crucial in assessing your data quality for completeness, accuracy, timeliness, and consistency. It helps you answer the “What your data looks like” question. Profiling saves time and allows you to spot inaccuracies quickly.

What is Data Quality?

Data quality refers to developing and implementing activities that apply quality management techniques to data to ensure the data fits an organization’s specific needs in a particular context. Data deemed appropriate for its intended purpose is considered high-quality data.

How to Improve Data Quality

Data quality measures can accomplish with data quality tools.

  • Data profiling
  • Data Standardization
  • Geocoding
  • Matching or Linking
  • Data Quality Monitoring
  • Batch and Real-time

A good data quality services should provide a data quality dashboard that delivers a flexible user experience and can be tailored to the specific needs of the data quality stewards and data scientists running data quality oversight. These tools and solutions can provide data quality testing but cannot fix wholly broken and incomplete data.

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