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  • Writer's pictureChelsea Wilkinson

A reflection on getting it right the first time

Late last year, when Adam Votava and I founded DataDiligence, we co-authored an article: Why is data science failing to solve the right problems?

(Let me be clear, I was very much the editor, and sprinkler of analogies and soundbites. Whereas Adam was the domain expert, drawing on years of experience as a data scientist.)

When we published it, I don’t believe we realised just how pivotal this inaugural article would be. Or how the concepts we wrote about would become the cornerstone, framework, and lexicon of our burgeoning business.

Nine months later, we still encourage our current and prospective clients to read the article (I encourage you to read it too!), because it pre-empts potential pitfalls without tumbling into them yourself. After all, nobody wants to join the 80% of projects that Gartner predicts will fail through 2022.

The key takeaways from the article are:

💎 Don’t play into the ‘Buzz Lightyear’ effect — Headlines imply that data science can solve just about anything! Yes, opportunities abound, and optimism is high — but, we must safeguard against overselling and hyperbole. Data is not a toy. It is a strategic tool supporting critical decision making, optimisation and potential revenue streams.

💎 Start at the beginning: the business issue — Business issues define the analytical problems; analytics and data offer potential solutions. As a data scientist (and business leader) be systematic in understanding the business issue first.

💎 Build bridges not islands — The more you and your data team are integrated into the wider organisation, the greater your likelihood of success. Be generous with your insights and curious about the business as a whole.

💎 Be peers not processors — Ask questions. Never accept analytical problems that you haven’t been part of framing.

Like I said, I encourage you to read the full article; link available below.



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