June 2021

I started my blog one year ago, June 2020, about the same time that I started my company, Cybele Data Advisory.

 

So far I've written 12 blog articles, all with one goal: helping my readers avoid the trough of disillusionment when it comes to your data operations. This trough is part of the Gartner Hype Cycle for Emerging Technology. The Hype Cycle is something that is published every year by Gartner, and is mandatory reading for anyone in the tech industry.

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Basically, technologies go through a cycle as follows: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment and plateau of productivity. This cycle can be applied to anything, really, but it does fit emerging technology very well.

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This month I'll get right to the point, and talk you through how data operations can be viewed in the context of the Hype Cycle. Following is a narrative I derived from typical thought processes, comments and behaviors I have observed in multiple organizations over the years.

 

Technology trigger: "We need to have data science / AI / machine learning. We need to be using our data better, to make better decisions. We have all this data, we need to do something with it."

 

Peak of inflated expectations: "Whew, we've finally got these really smart data scientists, and one or two expensive data tools. First, let's introduce them to everyone in the organization and tell everyone how they're going to save us from our data quagmire. Then let's put them all in a room, and they'll emerge with some really helpful insights about our data. We don't know what is in the data just yet, but if anyone can figure it out they can. We can't wait for them to demo their work to everyone!"

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Trough of disillusionment: "So, what are they doing in there? When are the data scientists going to have something we can really use? We thought they'd be providing value in the form of better decisions by now. They've shown us some graphs, but we're just not seeing it. Do we need a different software tool or more people? Maybe we made a mistake."

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Slope of enlightenment: "OK, maybe we need to learn a little more about this data stuff. Maybe it takes more than just a couple of data scientists and a bit of tech. Maybe we also need to raise the data literacy of the workforce and leadership, and define some specific ways that the data scientists can help. Maybe we don't even really need all these data scientists, but instead need more data engineers and analysts, and perhaps some data culture. Wow, we kinda screwed up, but we can fix it."

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Plateau of productivity: "Great, now we know what data we have, what we need, what we want to get out of it, as well as who and what we need to do that and how we do it. We've defined the value we expect, have communicated it and are measuring it. We now have sustainable and effective data operations!"

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Sound familiar? This narrative was not as tongue-in-cheek as you might think. The unfortunate thing is that so many organizations are stuck in the trough of disillusionment. In fact, it was much more difficult to derive the narratives for the 'slope of enlightenment' and 'plateau of productivity' from observations and experience, because unfortunately I so rarely observed them. Those came more from what I would like to see happen…

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My goal with my blog this past year has been to drag you up the slope of enlightenment and get you to the plateau of productivity on your own. The goal of my company is to help you get there in a way that is more custom to your needs.

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But like every other problem, first you need to admit to yourself that you have one and seek out a solution.

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Admit the problem, then read my blog posts and see if they help you dig out of the trough of disillusionment on your own. If you need help, reach out to Cybele Data Advisory and we'll help you make your way out of the trough, along the path of enlightenment and onto the plateau of productivity.

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Were you wondering why the squirrel photo was part of the Hype Cycle image above? C'mon, you should know me after 12 months of articles. No reason at all - he's just there because I know y'all secretly like them as much as I do.

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