Governing Volume: Ensuring Trust and Quality in Big Data
The term “Big Data” doesn’t seem quite big enough to properly describe the vast over-abundance of data available to organizations today. As the volume and variety of big data sources continue to grow, the level of trust in that data remains troublingly low. Ensuring quality in big data is the challenge.
Ensuring quality data as volume & variety grows
Business leaders have repeatedly expressed little confidence in the reliability of the data they use to run their business. In KPMG’s 2017 CEO Study, nearly half of CEO’s shared concern about the integrity of the data they base decisions on.
Results from Precisely’s 2019 Enterprise Data Quality Survey suggests that trend continues:
- 47% of respondents had untrustworthy or inaccurate insights from analytics due to lack of quality
- 26% do not have a process for applying data quality to the data in the data lake or enterprise data hub
The very purpose of the data lake is to enable new levels of business insight and clarity. No one sets out to create a data swamp that provides nothing but confusion and distrust.
According to big data analytics expert Bernard Marr, “Data democratization means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data… The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”
Read on to discover how a strong focus on data quality spanning the people, processes and technology of your organization will help ensure quality and trust in your analytics that drive business decisions.