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Volumes have been written on the need for high-quality data to support organizational decision-making. Most of those books appear to focus on the development and sustainment of data from the standpoint of those directly responsible for the management of data stores and the use of technology necessary to acquire, store, and secure data sets.
Navigating the Data Minefields: Management's Guide to Better Decision-Making provides executives and SMEs with a 'reasonable' set of (useful) tools they can adapt to their specific organization and operating environment, which now incorporates Artificial Intelligence (AI) environment and its suite of solutions such as Generative AI (Gen AI), Machine Learning, and Large Language Models (LLMs), et.al.. While complexity can never be taken out of an integrated system, decision-making can be facilitated using metrics that take into consideration the quality of the data used to make the decision, i.e., risk mitigation.
Professionals that depend on large-high-quality data sets such as senior and midlevel management, engineering Subject Matter Experts (SMEs), Data Scientists, IT, Systems Engineers, and medical professionals will want to have this book in their decision-making arsenal.
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Volumes have been written on the need for high-quality data to support organizational decision-making. Most of those books appear to focus on the development and sustainment of data from the standpoint of those directly responsible for the management of data stores and the use of technology necessary to acquire, store, and secure data sets.
Navigating the Data Minefields: Management's Guide to Better Decision-Making provides executives and SMEs with a 'reasonable' set of (useful) tools they can adapt to their specific organization and operating environment, which now incorporates Artificial Intelligence (AI) environment and its suite of solutions such as Generative AI (Gen AI), Machine Learning, and Large Language Models (LLMs), et.al.. While complexity can never be taken out of an integrated system, decision-making can be facilitated using metrics that take into consideration the quality of the data used to make the decision, i.e., risk mitigation.
Professionals that depend on large-high-quality data sets such as senior and midlevel management, engineering Subject Matter Experts (SMEs), Data Scientists, IT, Systems Engineers, and medical professionals will want to have this book in their decision-making arsenal.