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 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 businesses.
In KPMG’s 2017 CEO Study, nearly half of CEOs shared concern about the integrity of the data they use for decision-making.
Results from Precisely’s 2019 Enterprise Data Quality Survey suggests that the trend continues:
- 47% of respondents had untrustworthy or inaccurate insights from analytics due to poor 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.
“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.”
Data analytics expert, Bernard Marr
People: Empowering the Business to Use Big Data Without Barriers
Organizations must recognize that data scientists and analysts are one part of the equation for deriving value from data. Of course, sales, marketing, operations, and others spanning all business functions also need quick and efficient access to trusted data for the organization to be successful.
For decades, the IT department “owned” the company data and fulfilled all requests for data from the rest of the organization – a process that cannot keep up with the massive demand for vital information in today’s data world. Instead, many organizations are actively “democratizing” their data.
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.
