AI-Driven Automation And Human-Driven Management Of The Business Of Data
Seek AI launched today a cloud-based AI platform that automates some of the repetitive work that data professionals perform. Thy are often asked by business users to write new code to query a database to answer ad-hoc questions. Using generative AI like DALL-E, Stable Diffusion or GPT-3, Seek AI automates this process, improving the productivity of data professionals. Business users can access Seek AI’s natural language interface by means of email, Slack, text, and a range of customer relationship management (CRM) systems.
In an interview with Authority Magazine, Seek AI co-founder and CEO Sarah Nagy highlighted the challenge of managing the tradeoff between data accuracy and accessibility: “On one hand, accessibility allows less technical folks to start interacting with the knowledge wellspring that is a company’s data. On the other hand, what good is a wellspring of polluted water (i.e. bad data)? … The best data teams are those that manage this tradeoff in the most optimal way possible, and a big part of that is carefully calibrating and vetting any tools that non-technical users can interact with.”
The Talend second annual Data Health Barometer, based on a recent worldwide survey of 900 data professionals, reported that on all five markers of healthy data —timeliness, accuracy, consistency, accessibility, and completeness — companies rate themselves around 10 points lower in 2022 than they did in 2021. Timeliness (-29pts) and accessibility (-15pts) saw the greatest drops, as respondents struggle with getting the data they need as a result of remote work (57%).
More than one-third of companies report that trusting the data they rely on to make business decisions is a major challenge. In fact, nearly half of respondents feel that ensuring data quality is a top challenge in using data effectively.
Healthy, high-quality data that can be trusted, is a significant contributor to the health of the business. Respondents to Talend’s survey ranked increasing revenue and optimizing costs at the top of the list for data use and both of these business objectives have increased in priority from last year.
However, nearly half of companies claim that their data doesn’t yet have the speed and flexibility they need to satisfy all the demands of the business, and 41% claim they don’t have fast access to the right data. A major obstacle remains the data literacy gap between data professionals and data users: One in three respondents have reservations about how well employees understand the data they work with.
“Without a common language for data, these businesses may not be prepared to face the challenges ahead,” concludes the Talend report. A new Harvard Business Review article describes a new job category—data product managers—responsible for creating, communicating, and managing the common language and work of data engineers and the users of data.
In “Why Your Company Needs Data-Product Managers,” Thomas H. Davenport, Randy Bean, and Shail Jain observe that “many companies have adopted the concept of data products — an attempt to create reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem.” They report that at Vista, the marketing and design services company, data products have been responsible for an incremental $90 million in profits; and at Alabama-based Regions Bank, data products have earned or saved hundreds of millions of dollars.
Data product managers are individuals with both broad business skills and knowledge of the work done by data and AI professionals. They coordinate and manage the use of data products and measure their impact on the business.
To paraphrase Calvin Coolidge, “The chief business of today’s businesses is data. They are profoundly concerned with producing, buying, selling, investing and prospering in data.”