{"id":56334,"date":"2025-01-29T21:49:29","date_gmt":"2025-01-29T21:49:29","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/own-the-adoption-darkhorse-analytics\/"},"modified":"2025-01-29T21:49:29","modified_gmt":"2025-01-29T21:49:29","slug":"own-the-adoption-darkhorse-analytics","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=56334","title":{"rendered":"Own the Adoption \u2014 Darkhorse Analytics"},"content":{"rendered":"


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You console yourself with platitudes:<\/p>\n

\u201cYou can lead a horse to water, but you can\u2019t make him drink.\u201d<\/p>\n

The result, of course, is project failure.<\/p>\n

This situation plays out in organizations every day. Thousands of analysts do brilliant work, only to see it questioned, ignored, and ultimately, rejected.<\/p>\n

So how do we fix this? Are analysts destined to flock to the same set of analytics-friendly managers? Is your company doomed to mediocrity because the decision-maker hates numbers?<\/p>\n

Before we answer that, let\u2019s turn the tables and look at this from the perspective of a decision-maker:<\/p>\n

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You have grown up in an industry and been promoted over the years because you have a track record of making good decisions, developing good people, and growing the company.<\/em><\/p>\n<\/blockquote>\n

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You have devised a number of time-tested shortcuts to deal with uncertainty. You\u2019ve surrounded yourself with people you trust and you weigh their advice before making major decisions. You put your reputation on the line with every one of these decisions.<\/em><\/p>\n<\/blockquote>\n

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Now, perhaps you have an analytics guy. He\u2019s new to the organization, and when it comes to decision-making, he approaches it very differently from your other advisors:From time to time he suggests a solution to the wrong problem. He hasn\u2019t grown up in the business so he can\u2019t be expected to see the big picture, but still, he seems to have a hammer and so everything is a nail.When you ask him for an opinion, he says \u201cI\u2019ll get back to you\u201d then disappears for a month or two to work his black magic.When you ask him how he arrived at his conclusions, he begins to speak a different language. You sometimes wonder if he even understands the words he is using.He keeps talking about his models as if they\u2019re some kind of authority: \u201cThe model says so\u201d. But he has a hard time explaining why it says so.When you question him about the data quality, he waves it away and mumbles something about sample size.Whenever he submits a report, it reminds you of that 4th year econ course you almost failed. He points to numbers and charts that should be understandable but aren\u2019t.<\/em><\/p>\n

In short, this analytics guy is a little bit weird. He surfaces from time to time with ideas and opinions, but he can\u2019t justify them in a way you understand. To make matters worse, he seems offended when you don\u2019t take his advice wholesale. You suspect he won\u2019t last long in the organization.<\/em><\/p>\n<\/blockquote>\n

This story plays out all the time. On the one hand, we have the analyst providing objective, testable truth. On the other, we have the manager drawing on years of experience. The two perspectives seem irreconcilable.<\/p>\n

But they aren\u2019t. There\u2019s actually a simple solution, though it\u2019s not one you want to hear. You see, as the analyst, you are the problem. Defining your role too narrowly is preventing you from contributing.<\/p>\n

You have to own the adoption.<\/strong>\u00a0You have to accept that the job isn\u2019t done when you get the right answer. It\u2019s only done when the decision-maker is convinced.<\/p>\n

Wait, what?<\/p>\n

You heard me correctly. No more blaming your pig-headed boss for ignoring the gem of an employee you are. If he\u2019s not accepting your advice, then you\u2019re not doing your job.<\/p>\n

This sounds patently unfair. And it is unfair. But if you adopt this philosophical shift, it will inevitably lead you to change your approach to your job. It won\u2019t just affect the way you present your work. Instead, you will end up thinking of adoption from the very beginning:<\/p>\n

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  1. \n

    You\u2019ll spend more time up front understanding the problem.<\/strong> You\u2019ll put yourself in the decision-maker\u2019s shoes and ask more questions. You\u2019ll get the problem stone cold right.<\/p>\n<\/li>\n

  2. \n

    You\u2019ll provide more frequent updates.<\/strong> Instead of the big reveal at the end, you\u2019ll provide incremental results. You\u2019ll treat the decision-maker as a resource to improve your approach instead of a roadblock to remove.<\/p>\n<\/li>\n

  3. \n

    You\u2019ll choose simpler models.<\/strong> You\u2019ll be thinking about how easy it is to explain what you\u2019re doing. You will come to value clarity more than an additional digit of precision.<\/p>\n<\/li>\n

  4. \n

    You\u2019ll practice your storytelling.<\/strong> You\u2019ll teach yourself how to translate geek to business and not just business to geek. You\u2019ll use rules of thumb and narratives to explain both how and why your method works.<\/p>\n<\/li>\n

  5. \n

    You\u2019ll visualize way more.<\/strong> The eyes are a shortcut to the gut. You\u2019ll bypass the decision-makers inherent doubts and connect right to his instincts. Finally, your charts and tables will be clear and engaging.<\/p>\n<\/li>\n<\/ol>\n

    The quality of your analysis will benefit too. You\u2019ll be continually poking holes in it and you\u2019ll be much sharper because you\u2019ll know that it\u2019s going to be used (and scrutinized).<\/p>\n

    In short, you\u2019ll no longer be a resource person providing the \u201canalytics\u201d on a particular decision. You\u2019ll be embedded in the entire problem-solving process. This change will benefit both you and the organization.<\/p>\n

    The biggest hurdle you\u2019ll have to leap is your own sense of injustice. Leap it. Own the adoption.<\/p>\n<\/div>\n