Empowering the Public Sector with Data Science
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Over the last decade, numerous in-house data analytics programs have hastily embraced the latest data trends, spanning from ‘big data’ to ‘machine learning,’ resulting in excessive spending and disappointing outcomes. This trend is now recurring with the rapid adoption of ‘AI’.
As before, boards and non-technical executives are over-pressuring their teams toward AI beyond definable problems, and unqualified vendors are marketing and selling expensive products that don’t deliver (and often aren’t really ‘AI’). You can see it in your inbox and on LinkedIn.
Nevertheless, valuable lessons can be drawn from previous adoption cycles, especially from data analytics programs that thoughtfully incorporated new technologies to solve real problems efficiently. These programs will thrive in the era of AI.
At Civis, we’ve had the privilege of providing technology for and collaborating with exceptional in-house analytics programs over the last decade. Based on the lessons from these collaborations, I’m going to share some recommendations that are more crucial than ever in the AI era.
It’s worrisome to read some of the narrative on this platform, and more broadly, about how AI can ‘solve everything’. AI promises incredible opportunity, but we should learn from past trends and adoption cycles to make sure that in-house data programs become transformational investments — not predictable failures through a reckless rush to AI.
The uncomfortable truth is that most data analytics efforts fail, careless trend adoption is often a major contributing factor, and there’s a huge risk of that happening again in the rush to AI.
Here’s how organizations from companies, to nonprofits, to governments create unsuccessful programs:
The best data teams we’ve worked with follow a short-list of best practices, which makes them successful through trends and ongoing executive pressures.
Data programs today are under incredible pressure to ‘deliver AI’, often with an unclear definition or reason of what that means, which introduces a lot of failure risk as we saw with previous trends. Great teams will partner with their executives, ignore the trends, and solve definable problems while deliberately and carefully incorporating AI into work. And, most importantly, they’ll have a lot of fun doing it. And, of course, if you’re interested in advancing your own AI journey, feel free to reach out.
If you’re excited about sharing more of the data that is driving your work with your stakeholders, we invite you to reach out to our team of experts. Let us guide you through the process, answer any questions you may have, and help you create and share your services.
