Machine learning, AI, and the increasing attention for data quality
Data quality has been going through a renaissance recently.
As a growing number of organizations increase efforts to transition computing infrastructure to the cloud and invest in cutting-edge machine learning and AI initiatives, they are finding that the main barrier to success is the quality of their data.
The old saying “garbage in, garbage out” has never been more relevant. With the speed and scale of today’s analytics workloads and the businesses that they support, the costs associated with poor data quality are also higher than ever.
This is reflected in a massive uptick in media coverage on the topic. Over the past few months, data quality has been the focus of feature articles in The Wall Street Journal, Forbes, Harvard Business Review, MIT Sloan Management Review and others. The common theme is that the success of machine learning and AI is completely dependent on data quality. A quote that summarizes this dependency very well is this one by Thomas Redman: ''If your data is bad, your machine learning tools are useless.''
The development of new approaches towards data quality
The need to accelerate data quality assessment, remediation and monitoring has never been more critical for organizations and they are finding that the traditional approaches to data quality don’t provide the speed, scale and agility required by today’s businesses.
For this reason, highly rated data preparation business Trifacta recently announced an expansion into data quality and unveiled two major new platform capabilities with active profiling and smart cleaning. This is the first time Trifacta has expanded our focus beyond data preparation. By adding new data quality functionality, the business aims to gain capabilities to handle a wider set of data management tasks as part of a modern DataOps platform.
Legacy approaches to data quality involve many manual, disparate activities as part of a broader process. Dedicated data quality teams, often disconnected from the business context of the data they are working with, manage the process of profiling, fixing and continually monitoring data quality in operational workflows. Each step must be managed in a completely separate interface. It’s hard to iteratively move back-and-forth between steps such as profiling and remediation. Worst of all, the individuals doing the work of managing data quality often don’t have the appropriate context for the data to make informed decisions when business rules change or new situations arise.
Trifacta uses interactive visualizations and machine intelligence guides help users by highlighting data quality issues and providing intelligent suggestions on how to address them. Profiling, user interaction, intelligent suggestions, and guided decision-making are all interconnected and drive the other. Users can seamlessly transition back-and-forth between steps to ensure their work is correct. This guided approach lowers the barriers to users and helps to democratize the work beyond siloed data quality teams, allowing those with the business context to own and deliver quality outputs with greater efficiency to downstream analytics initiatives.
New data platform capabilities like this are only a first (albeit significant) step into data quality. Keep your eyes open and expect more developments towards data quality in the near future!
Author: Will Davis