Substantial Challenges Filling Data Teams with Dread While Maintaining Data Quality in 2022

  1. Challenges Related to Data Sources: Not only the volume of data but growing data sources also impact organization’s ability to make data useful.
  2. Composing Data From Disparate Sources: Data acquires many forms and is a sojourner in different touch points like data warehouses, data lakes, devices, platforms and many more.
  • Inconsistent data across enterprises
  • Maintaining and expanding data preparation processes.
  1. Inaccurate Data: According to Corinium Intelligence and Precisely study, 40% is the average proportion of time devoted by data teams on data cleaning, integration and preparation. Organizations need to improve quality, as “Improving data quality is the purpose, but obtaining accurate data is the consequence”.
  2. Data Uniqueness: Enterprises suffer from problems of identifying data entities across systems and lack proactive steps to prevent duplicate records.
  1. Data Integration and Data Orchestration: With increasing new use cases of analytics and digital application, enterprises require months of data discovery, data-pipe engineering, data cleansing, data documentation, and common standards.
  2. Data Catalog and Discovery: Data catalogs are unable to keep pace with new realities like automation. While data catalogs have the ability to document data, the fundamental challenge allowing users to discover and glean meaningful, real-time insights about the health of data has largely remained unsolved.



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