Forget for a moment that I\u2019m talking about a mathematical theory.\u00a0 At the core of this post is how we apply some quantitative reasoning to our ability to look at data and predict the future.\u00a0 Personally, I don\u2019t think we do enough to extend and create meaning from our data.\u00a0 If we looked at data with an increasing view of the quantitative sciences, we would have a proportional increase in our ability to apply \u201cart\u201d to our interpretation.<\/p>\n
We predict things intuitively every day.\u00a0 Driving down the road, we watch other cars, measure their velocity, and predict if we are going to collide or not \u2013 and based on those predictions we often change our course.\u00a0 In Human Resources, we should be predicting the direction and velocity that our workforce is travelling in a number of ways.\u00a0 Not only do we watch the size of the workforce, but productivity, engagement, competency growth, and any number of other factors.<\/p>\n
A dynamical system is a mathematical construct that predicts what the state of a particular object will be at a given point in time in the future.\u00a0 Dynamical systems are useful in a limited way \u2013 through them we can predict future state outcomes in a limited number of variables.\u00a0 But the point is that we can predict vector and velocity \u2013 in other words, if the workforce is travelling in the right direction and at the right speed.<\/p>\n
First off, I should absolutely admit that HR is not comprised of a bunch of mathematicians.\u00a0 This should be no surprise to anybody.\u00a0 However, our organizations that make up the analytical arms of the HR organization, those who generate analytics and create meaning out of data, should have some ability to quantitatively view the data and understand trending, directionality, velocity, and the general parameters of the vector.\u00a0 I hate taking us all back to high school calculus, but we remember the first derivative of the curve is the slope of the curve.\u00a0 This is general trend of what our workforce metric is doing.\u00a0 However, the second derivative of a curve is the determination of whether the curve\u2019s direction is slowly shifting.\u00a0 In other words, we might know that our overall turnover rate is dropping, and that is a positive sign.\u00a0 However, do we know if the turnover rate is dropping more slowly than it was last quarter, even if the rate is better than it was before?<\/p>\n
While most of HR is not in the mathematics field, and most of us don\u2019t want to be anywhere near it, we should be applying some of these theories to our analytics.\u00a0 If we look at any of our programs, we can see acceleration of desired outcomes after specific events, and deceleration of outcome after we have not done any change management or communications for a while.\u00a0 We should be looking at competencies not as a growth metric, but as an acceleration curve over time.\u00a0 Growth is good, don\u2019t get me wrong, but acceleration is better.<\/p>\n
I\u2019d like to think that all HR data analysts out there are taking a serious look at the data they are presenting, trying to create and extend meaning beyond what is being requested, but today\u2019s reality is quite mundane, even with all the cool business intelligence and dashboard technologies out there.<\/p>\n","protected":false},"excerpt":{"rendered":"
Forget for a moment that I\u2019m talking about a mathematical theory.\u00a0 At the core of this post is how we apply some quantitative reasoning to our ability to look at data and predict the future.\u00a0 Personally, I don\u2019t think we…<\/p>\n","protected":false},"author":1,"featured_media":1696,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[27],"tags":[240,239,241,242],"class_list":["post-1681","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-metrics","tag-business-intellience","tag-decision-support","tag-dynamical-systems","tag-trending"],"_links":{"self":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1681","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1681"}],"version-history":[{"count":2,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1681\/revisions"}],"predecessor-version":[{"id":1695,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/posts\/1681\/revisions\/1695"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=\/wp\/v2\/media\/1696"}],"wp:attachment":[{"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1681"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1681"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/systematichr.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}