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This article by Thomas C. Redman emphasizes the importance of high-quality data in powering artificial intelligence (AI) systems. It draws parallels between the financial crisis and the potential risks of AI, highlighting that good data science with bad data can lead to negative outcomes. The article discusses the need for clear problem definition, relevant and complete data, freedom from bias, timeliness, and appropriate exclusions in data quality. It also emphasizes the importance of management responsibilities and quality assurance in ensuring data accuracy and integrity. Redman suggests transitioning from dealing with bad data downstream to creating good data upstream through customer focus, clear management accountabilities, and involving people at all levels. The article concludes by stating that managing data properly is crucial as it becomes an asset with limitless potential when combined with AI. [Extracted from the article]

This article discusses the challenges that senior managers face in extracting value from their company's data and offers strategies for getting their data program back on track. The author emphasizes that data is a management problem, not just a technology problem, and that everyone who interacts with data has a role to play. The article suggests focusing on three key areas: improving data quality, starting with easier data science techniques, and leveraging data to create a competitive advantage. The author also highlights the importance of involving and supporting employees in these efforts. [Extracted from the article]

This article discusses the potential and challenges of artificial intelligence (AI) and the need for a comprehensive approach to its implementation. The author acknowledges the excitement surrounding AI but also highlights the historical pattern of technologies arriving in clusters and the accompanying organizational and societal changes required for their success. The article identifies barriers to the widespread adoption of AI, including the need for massive computing power, high-quality data, organizational capabilities, and societal acceptance. The author emphasizes the importance of data quality, organizational readiness, and senior leadership commitment for successful AI implementation. [Extracted from the article]

A reprint of an article titled "Digital Transformation Comes Down to Talent in Four Key Areas," written by Thomas H. Davenport and Thomas C. Redman is presented. It was originally printed on May 21, 2020 on the Harvard Business Review website. Particular focus is given to the author's insights about how difficult true digital transformation really is, but how it can be achieved through the use of four key areas including: technology, data, process and organizational change capability.

As practitioners know, creating awareness of a problem and its impact is a critical first step toward resolution of the problem[5]. The needed awareness of poor data quality, while growing, […]

By Thomas C. Redman and Thomas H. Davenport Published on HBR.org / March 08, 2021 / Reprint H068AY Jonathan Knowles/Getty Images Many organizations have begun their data science journeys by starting "centers of excellence, " hiring the best data scientists they can and focusing their efforts where there is lots of data. How will data and data science be employed throughout the organization? As previously noted, most organizations focus their data science efforts where they have the most data - even if they don't mean to. [Extracted from the article]

Fifty years after the expression "garbage in, garbage out" was coined, we still struggle with data quality. Studies show that knowledge workers waste a significant amount of time looking for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust. When data are unreliable, managers quickly lose faith in them and fall back on their intuition to make decisions, steer their companies, and implement strategy. They're also much more apt to reject important, counterintuitive implications that emerge from big data analyses. But improving data quality is often not as hard as you might think, the author asserts. The solution is not better technology. It lies in better communication between the creators of data and the data users; a focus on getting the process right going forward rather than on cleaning up existing bad data; and, above all, the shifting of responsibility for data quality away from IT folks, who don't own the business processes that create the data, and into the hands of managers, who are highly invested in getting the data right. INSETS: How Data Get Dirty;Fixing the Metadata. [ABSTRACT FROM AUTHOR]

IF I'M READING THE TEA LEAVES CORRECTLY, we're on the verge of a sea change in the way organizations manage data. I'm referring specifically to data quality, which has been […]