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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.

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]

Poor data quality has far-reaching effects and consequences. The article aims to increase the awareness by providing a summary of impacts of poor data quality on a typical enterprise. These impacts include customer dissatisfaction, increased operational cost, less effective decision-making and a reduced ability to make and execute strategy. More subtly perhaps, poor data quality hurts employee morale, breeds organizational mistrust, and makes it more difficult to align the enterprise. Creating awareness of a problem and its impact is a critical first step towards resolution of the problem. The needed awareness of the poor data quality, while growing, has not yet been achieved in many enterprises. After all, the typical executive is already besieged by too many problems, low customer satisfaction, high costs, a data warehouse project that is late, and so forth. Creating awareness of issues of the accuracy level and impacts within the enterprise is the first obstacle that practitioners must overcome when implementing data quality programs.

The article discusses how business leaders can effectively use data in their decision-making process, including assessing where the new data originated, evaluating the data quality independently, and cleaning the data.

The article offers a guide to good decision making especially in business which include assembling the relevant facts before coming up with a decision, having a broad perspective to make and decision a decision, and avoiding confirmation bias.

The article discusses the things that great data scientists do to acquire quality information, which include recognizing the nuances and quality issues of the data, spending enough time to verify the data, and being curious about the data and everything surrounding it.

The article offers tips on analyzing data results effectively, including not accepting results immediately just because they look good, avoiding accepting general analysis results and focusing more on distinct subgroups and preparing for the impact of results that go against conventional wisdom.

The article explains how managers can exploit the potential of predictive analytics and overcome the perils of bad data and hidden false assumptions by employing both computer-based programs and their own intuition.

In the article, the author discusses issues in business, particularly the possible huge costs to profitability of even the smallest errors, citing as example the case of telecommunications firm AT&T, whose process in the payment of services bought from telephone companies presented some flaws.

The article discusses ways for data presenters to make their plots and explanations easy to understand including telling the data's story in a powerful and animated fashion, understanding the audience's needs, and separating opinions from facts.

The author explores the distinction between correlation and cause and effect in big data and advanced analytics using his own personal diet data.

In the article, the author discusses the bad habits that should be eliminated by business leaders in order to take full advantage of their data in decision making, including analysis-paralysis, second-guessing, and preferring intuition over data.

The article discusses the strategies of becoming a data-driven organization and the three steps to create a discovery process in a company including establishing a separate department, learning to manage the data lab, and managing the people.

The article discusses steps that managers can take to measure the quality of their data which include assembling the last 100 data records used by the group, setting a meeting with people having knowledge of the data, and checking and recording errors in the data.

The article discusses the roles of data provocateurs in driving data quality across an entire company and recommends provocateurs to keep bosses informed, accept help from others, and share credit for success.

The article offers tips for business leaders on how to critically assess their use of data measurements in business, including identifying the data that need to be known, having a thorough knowledge on how actual measurements line up to what one wants to know, and knowing the flaws of the process.

The article offers a guide for managers to learn to use data more effectively and pass those skills on the members of their team.

The article discusses business models aimed to compete with data which include cost reduction through improved data quality, data-driven innovation, providing content and becoming data-driven.

In the article, the author discusses how data revolutionaries can overcome the resistance of their employees to a data agenda, and presents the major types of data resistors like the virulent naysayers, the passive resistors, and the reasonable challengers.

The article examines the various types of data quality problems that companies regularly encounter, which include incomplete data, misinterpreted data and unreliable data, and discusses how they can impact business planning and decision making.

The article discusses the reasons why companies or businesses do not need to hire their own chief data officers (CDO) to manage their data requirements but rather appoint one when they are already determined how they want to compete with data over the long term.

The article discusses the characteristics of data-driven companies wherein they work to drive decision-making to the lowest possible level.