The rise of big data and data science is already having a significant positive impact on our lives. However, there are also dark sides to how data is collected, used, and misused. Often this misuse is not from an intentionally malevolent actor. Rather, it could be someone like you or […]
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Machine learning predictive models are often used in data science projects. These models often deliver significant value. Examples of these AI models range from identifying skin cancer to optimizing when to milk cows. Unfortunately, there are many examples of AI (machine learning algorithms) not working as desired. For example, a […]
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The importance of AI project management, or more specifically, managing AI projects, is highlighted by a survey showing that 85% of AI projects do not deliver on their promises to business. In other words, as noted by Forbes, most AI projects fail. An AI project is different than a typical […]
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The Boston Dynamics humanoid robot tripping over a curtain and tumbling off stage? A prediction model that tips a teen’s parents off to her pregnancy? A music recommendation engine that suggests Coldplay? Yup. There are just too many big data, data science, and data analytics failure examples to cover in […]
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It’s an understatement that great leadership is challenging and rare. And leading data science teams has unique challenges: While this topic might seem most relevant to management and executives, remember that leadership is not a title. Rather, I encourage everyone reading this post to assess these points and to upskill […]
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While the potential ethical issues that might arise when using data science and artificial intelligence have certainly been in the popular press recently, there is not been as much discussion with respect to how a data science team should incorporate ethics within a project. Furthermore, despite this recent publicity, via […]
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Data science projects are challenging. To increase your odds of success, start them by asking several key data science project questions. As Ben Franklin said, “an ounce of preparation saves a pound of headaches in your data science projects”.* To help you prepare for a project and evaluate whether to […]
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