A Very Dangerous Data Science Article

A few years ago an article was published in Harvard Business Review online called <a href="https://hbr.org/2018/10/prioritize-which-data-skills-your-company-needs-with-this-2x2-matrix" rel="noopener ugc nofollow" target="_blank">Prioritize Which Data Skills Your Company Needs with This 2×2 Matrix</a>. Now, HBR is well known for its articles on business strategy, but it is not really a market leader in technical content, and this article certainly illustrates this. This article is shockingly bad, and is also very dangerous because it encourages people to think about data science in a way that is both impractical and downright wrong. I’ll elaborate on this in a moment, but the obvious danger is that someone who doesn’y know better will use this article to inform their own organization’s data strategy. This could cause a lot of pain and angst for many data professionals, and leave others feeling like their skills and experience have been pejoratively dismissed. <h2>What does this article say?</h2> The article has basically cut and pasted a well-known strategic business framework and tried to apply it to data skills. The author uses a cost-benefit matrix where the cost is the ‘time taken to learn’ a data skill and the benefit is the ‘usefulness’ of that data skill to the organization. The author suggests that this is a useful framework to determine what data skills to invest in. In theory this sounds fine, of course. Matrices are useful strategic frameworks for helping prioritize against a couple of key considerations (though they tend to be overused in practice and often their axes are not as independent as people think they are). <a href="https://keith-mcnulty.medium.com/a-very-dangerous-data-science-article-415e576f0c44">Learn More</a>