Keeping Up With Data #96

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readAug 19, 2022

The image from West Monroe’s report made me chuckle. Given the growing importance of data and analytics for operational and business models of many organisations, it could be dangerous to rely solely on proclaimed capabilities.

Don’t get me wrong, many organisations actually have databases providing unique insights to customers, have ML embedded in the customer-facing platform, or have data science teams driving product enhancements.

And for these organisations, as eloquently put by Thomas H. Davenport in one of the articles in this week’s reading list, “virtuosity with data is often part of the brand.”

Due diligence on analytics and data is important to separate the wheat from the chaff. And as I have the luxury of sitting in the front row, the demand for data due diligence is growing for that very reason.

The role of the data due diligence is to assess the current capabilities, possible risks, as well as future opportunities and bring the buyer and the target on the same page. Otherwise, the second board meeting after the acquisition can get really awkward the true capabilities are revealed.

Data and analytics provide great value-creation opportunities. But unlocking them depends on the real capabilities, not the ones proclaimed by the target. Trust, but verify.

Three exciting reads on the list today. Each written during a different time, all still very relevant today.

  • Competing on Analytics: An article by Thomas H. Davenport from 2006, highlighted by HBR as one of their 12 very favourite articles marking HBR’s 100th anniversary. Written 16 years ago, the theme — analytics providing competitive advantage — is still very relevant. The ingredients of success haven’t changed much either: investing in technology, collecting data, having a company-wide data strategy, and having “executives’ vocal, unswerving commitment and willingness to change the way employees think, work, and are treated.” The technology has evolved a lot since the article was published. So have algorithms and volumes of data. Data leaders transforming their organisations prevail to be the rare element. (HBR)
  • You can make any piece of data look bad if you try: Here is one for people claiming that “hard data don’t lie.” The interpretation of facts typically depends on comparisons, time frames, expectations/priors, and broader context. Just like in the discussions about inflation taking place now: Being good and getting better are two very different things. So are 10-year average and last month figures. What are some market observations that miss the point when focusing on the big picture? (TKer by Sam Ro)
  • The analytics and AI gap in M&A: Analytics and AI play an increasing role in many companies. Relying on self-declared analytical capabilities (or even worth on marketed capabilities) can be destructive post M&A. A study of 40 acquisition targets by West Monroe reveals how much and how often are the perceived capabilities exceeding the actual ones. The gap is not that large when it comes to reporting and BI. But once we move on the analytical ladder to analytics in products and production, predictive analytics, ML, or AI, the gap is significant. (West Monroe)

My parents are staying with us this weekend. And my dad brought his new road bike with him. So a family weekend with some nice Alpine cycling ahead us!

In case you missed the last week’s issue of Keeping up with data

Thanks for reading!

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Adam Votava

Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)