{"id":1135,"date":"2020-09-24T10:07:19","date_gmt":"2020-09-24T10:07:19","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=1135"},"modified":"2020-09-24T10:07:21","modified_gmt":"2020-09-24T10:07:21","slug":"does-data-driven-decision-making-make-you-boring","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2020\/09\/24\/does-data-driven-decision-making-make-you-boring\/","title":{"rendered":"Does data-driven decision-making make you boring?"},"content":{"rendered":"\n
\"\"
Image by g3gg0 from Pixabay\n<\/figcaption><\/figure>\n\n\n\n

With all the focus on data and AI, it was simply a matter of time before the countermovement started. Reflecting on several discussions around this topic that I\u2019ve had over the last year, the key theme seems to be that data and AI are predicting the future based on the past and as long as the future is like the past, this works fine. However, the world is in constant flux and these technologies cause stagnation as we can\u2019t predict fundamental shifts and disruptive innovations. Even worse, we don\u2019t even look for them as we look at data in a short-sighted fashion.<\/p>\n\n\n\n

Although I most certainly believe that there\u2019s a very important place\n for human creativity and insight, I also think that not exploiting the \nadvantages data and AI offer is simply akin to shooting yourself in the \nfoot or tying one arm behind your back. There are several reasons for \nthis.<\/p>\n\n\n\n

First, for all the criticism on machine learning for predicting the \nfuture, the fact is that in most cases, humans are even worse at it. \nEven for highly variable data, ML algorithms often manage to exploit \npatterns that humans fail to detect. For large retailers, predicting the\n amount of product to order and then allocating it to each individual \nshop used to be a human task, but it\u2019s clear that ML algorithms, given \nsufficient data, do a better job. A counterargument used frequently \nrecently is that these algorithms didn\u2019t predict the Covid-19 \ndisruption, but of course, humans didn\u2019t predict it either, leaving many\n stores with a significant surplus of goods.<\/p>\n\n\n\n

Second, I still meet people that continue to express beliefs about \nthe world, their industry, their customers or their own performance that\n simply aren\u2019t true. Although some, like Steve Jobs, were known for \ntheir \u201creality distortion field,\u201d for virtually all of us, just wishing \nfor something to be true doesn\u2019t make it so. As William Edwards Deming \nfamously said: in God we trust; all others must bring data.<\/p>\n\n\n\n

Third, data-driven practices don\u2019t remove human creativity but \ninstead focus it on the formulation of hypotheses. In traditional \norganizations, one can build a career on making strong statements that \nare hard to verify and being vocal about them. Often, these are based on\n individual instances and storytelling, to which we as humans are very \nsensitive. When adopting data-driven practices, the focus should be on \nformulating testable hypotheses and being less concerned with being \nproven wrong. Even hypotheses that are creative and novel but don\u2019t pan \nout provide ample opportunity for learning.<\/p>\n\n\n\n

Fourth, when using data-driven practices, you need to know what \nyou\u2019re optimizing for. In virtually all companies that I work with, \nfeatures are prioritized and developed based on the beliefs of some \nproduct manager. The effect of the prioritized feature on the customer \nor system behavior and the way it generates value is often described in \nqualitative and vague terms. The worst argument here is that it\u2019s a \n\u201cstrategic investment.\u201d Rather than prioritizing a feature to be \ndeveloped based on the beliefs of a product manager, it\u2019s much better to\n treat the feature as a hypothesis, define its expected, quantitative \neffect and then measure its impact as you iteratively develop the \nfeature slice by slice.<\/p>\n\n\n\n

Working in a data-driven fashion doesn\u2019t make you boring. Instead, it instills a higher level of discipline in the organization, uses technology where it fits best and focuses creative energy on the areas where humans provide the most value. It helps organizations to shed so-called \u201cshadow beliefs\u201d (beliefs that everyone in the organization considers to be true but that are not) and, through that, remove hypotheses that don\u2019t hold from the pool of ideas. Neither humans nor machines can predict the future. However, although history never repeats itself, it often rhymes. And machine learning is better at detecting the rhymes than you.<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at <\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

With all the focus on data and AI, it was simply a matter of time before the countermovement started. Reflecting on several discussions around this topic that I\u2019ve had over the last year, the key theme seems to be that data and AI are predicting the future based on the past and as long as … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[15,4],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1135"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1135"}],"version-history":[{"count":1,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1135\/revisions"}],"predecessor-version":[{"id":1137,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1135\/revisions\/1137"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1135"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1135"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}