Top 5 data quality and accuracy challenges and how to overcome them
Poor data quality leads to increased complexity of data ecosystems and poor decision-making over the long term. Data quality is comprised of multiple factors - completeness, consistency, validity, timeliness and uniqueness.
The article covers several tips:
- Data quality being a unique challenge for each business
- What you don’t know can hurt you
- Don’t try to boil the ocean
- More visibility = more accountability and better data quality
- Data overload is increasing
Why it’s relevant to Nextspace
The article is of interest to Nextspace Partners as it addresses the fundamental benefits of data structured by the Nextspace platform’s agile data schema and ontological approach.
Also of use is a list of “quality” factors that Partners can use when enquiring about Customer data. Completeness consistency, validity, timeliness and uniqueness.
Thirdly, it quotes a Gartner report that $12.9 million is lost each year due to poor data quality. The Gartner report is also reviewed here and the point to note is that $12.9 million is an average per company.