I often see articles or posts that identify data integration or preparation as the key issues facing data science projects. This always puzzles me as this is not our lived experience – not what we see when we work with Fortune 500 companies adopting predictive analytics, machine learning or AI. But I think I have figured it out. The problem is as follows:<\/p>\n
What data scientists think counts as a “data science project” Let me illustrate this with some data from a great study. Back in 2016, the Economist Information Unit did a survey on “Broken links: Why analytics investments have yet to pay off<\/a>” and below you see how this data appears to support the argument that data problems are #1.<\/p>\n Wow – pretty clear that Data integration\/preparation is the biggest problem with nearly twice as many projects reporting it as a problem as the next one.<\/p>\n In fact, though, this is a subset of the data from the survey. Here’s the full data set:<\/p>\n Data integration and preparation only ranks #4. Problem definition\/framing, Solution approach\/design and Action\/change management all rank higher. This is our experience.<\/p>\n In large, established “grown-up” companies, data science projects fail for one or both of two reasons:<\/p>\n And this illustrates the problem.<\/p>\n The problem is that data scientists THINK their project starts with data and ends with the communication of their analysis. If that’s your focus, then data is your #1 problem.<\/p>\n But this is not where data science projects start nor where they end. They have to start and end with the business<\/strong>. That means starting with a business<\/strong> problem – a business decision that the business wants to improve – and ending with that problem being solved – the business<\/strong> behaves differently (better). If that’s your focus, then your problem is not data but problem definition and operationalization – making the analytic work IRL.<\/p>\n Here’s the difference, shown on those phases. On the left, what many data scientist think their projects involved and on the right, what it really involves.<\/p>\n Bottom line: If your data science team is telling you that I’ve written about this before – check out this LinkedIn article on the study itself<\/a> and this one on adopting decision modeling<\/a> as a better way to define the problems your data science team is trying to solve. You might also like our recent white paper and videos on Building an Analytic Enterprise<\/a>.<\/p>\n Feel free to connect with me on LinkedIn<\/a> to message me with questions and comments. And if we can help your data science team start working on a better definition of a project, we’d love to<\/a>.<\/p>\n This article was originally posted to LinkedIn<\/a> where it has 60+ comments, 140+ reactions and 1,000+ views. Check out the discussion.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
is not, in fact, a data science project.<\/strong><\/em><\/span><\/p>\n<\/a><\/p>\n
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data is their #1 problem then they’re doing it wrong<\/strong><\/em><\/span><\/p>\n