After incubating and scaling data & analytics organizations at 3 companies, I’ve learned from my share of mistakes. Here are 3 that I help my clients avoid after earning my PhD from the “school of hard knocks”:

Under-investing in data architects, engineers, and stewards

This happens when teams grow enamored with the potential of data science, AI engineers, and advanced statistical analysts. Without a robust, secure master data environment, these incredibly brilliant (and expensive) teammates spend most of their time pulling, joining, cleaning, and transforming data.

KEY SIGNS: Analyses, predictive models and ML algorithms take months vs. weeks to complete; high attrition among the data scientists.

SOLUTIONS: Prioritize investment in data architects, engineers and stewards!

Suffering from Build Syndrome

This happens when teams grow enamored with building the perfect data environments and analytic solutions, avoiding or disregarding feedback from the business. 6-week efforts grow to 6 months, and the business moves on to available alternatives that meet a portion of their needs.

KEY SIGNS: delays; never-ending pursuit of the latest and greatest technology; “we understand their needs better than they do.”

SOLUTIONS: alignment & prioritization with business stakeholders on use cases; rapid iteration with business on development; 80/20 principle.

Overcomplificationalizing

This happens when teams grow enamored with advanced statistical techniques, ML algorithmic options, and yes, Gen AI LLM nuances. The eyes of their business stakeholders glaze over; they lose their audience, the influence and the impact from their work.

KEY SIGNS: Presentations with formulas and statistics; multi-page descriptions of methodologies; audience looking at phones.

SOLUTIONS: focus on insights, takeaways, recommendations, and impact; practice with non-technical audiences.

How do these experiences compare with yours? What am I missing? Please share in the comments.

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