Data science solutions, in the form of a commercial product or custom-built, are unlike other types of software. The classic problem-solution gap, illustrated in the well-known cartoon below, can be hugely exacerbated in data science by the fact that, in the end, the customer may not realize they got something other than what they asked for. Instead, they may have the false impression that the solution is working quite well.<\/p>\n
Customers have learned to objectively evaluate most aspects of software, such as performance, usability, or functional fit. What they have not learned yet is how to evaluate the quality of mathematical modeling in data science solutions. The reason for this is straightforward: most customers lack the expertise required to be able to see that the solution is not optimally, or not at all, suited to their needs.<\/p>\n
In some situations, there are objective metrics that help. If a solution outperforms the competition in relation to a desired metric, such as the precision of a prediction model, then the customer rightly has a reason to be satisfied. Even so, it is not always clear which metric should be used in a given situation nor being better than competition is a guarantee of an optimal solution. Moreover, much of data analytics is not predictive or prescriptive, but rather descriptive, and for this type of analysis objective metrics are rare.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Data science solutions, in the form of a commercial product or custom-built, are unlike other types of software. The classic problem-solution gap, illustrated in the well-known cartoon below, can be hugely exacerbated in data science by the fact that, in the end, the customer may not realize they got something other than what they asked […]<\/p>\n","protected":false},"author":7,"featured_media":596,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[13],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/posts\/643"}],"collection":[{"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/comments?post=643"}],"version-history":[{"count":0,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/posts\/643\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/media\/596"}],"wp:attachment":[{"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/media?parent=643"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/categories?post=643"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bloomsocialanalytics.com\/fr\/wp-json\/wp\/v2\/tags?post=643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}