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The human capability to under-or overestimate

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It can be hard to explain what I do for a living to friends and family. Running an industry analyst firm is an odd job for sure; in a sense, I guess we are the modern-day technology sector equivalent of courtiers (not to be confused with courtesans!). In that, we provide expert advice to tech movers andshakers, be they technology vendors like Microsoft and IBM or executives at public and private organizations. I say expert advice, not to brag, but rather to state that the world of information and automation management, KM, etc., is incredibly specialized. We spend our working days thinking about it and have studied it for many years. Yet, for all the expert advice we provide, be that choosing between system A or B or the benefits and drawbacks of deep learning for document processing, probably the most valuable advice we provide is just some common sense. To quote Harriet Beecher Stowe, “Common sense is seeing things as they are; and doing things as they ought to be.” Oddly enough, that is often not the case in the world of high tech, and the most prominent and typical example is underestimating humans.

Best practices or ‘Best practices’

Take, for example, the massive growth of tools over the past few years that allow us to monitor and gain insight into how people work. Technologies such as task, process, and communication mining tools are rapidly becoming standard. And there is no question that these tools can bring massive value to an organization, particularly in a hybrid, disjointed, and often poorly configured working world. But to mangle another famous quote, “A little knowledge can be dangerous.” Though these tools give us unparalleled insights into what a worker does at their desktop or how a work-related task or process is undertaken, they only give us a partial view of what is happening.

I’ve touched on this in previous columns, but these tools can reveal to an untrained eye a “best practice” everyone should follow—only to find at a later date that the so called “best practice” was the very worst of practices. For example, a worker takes 10 minutes to complete a task versus a colleague taking 20 minutes to complete the same job. It’s all too easy to become excited by the faster worker, only to discover that this worker cut corners, cheated, or was plain incompetent. That is why in all our published and private advisory work at Deep Analysis, we repeatedly stress that skilled and well-informed business analysts should only ever use such tools. These people can add and understand the missing context to the data. That should be common sense, but trust me, it’s advice that can be hard to convey. Underestimating the sheer and often hidden complexity of human work is a common cause of project failure.

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