Keeping Up With Data #108

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readNov 11, 2022

There are many approaches to measuring data and analytics maturity. I like the ones focusing on the business impact of data, rather than technicalities like technology used, data types processed, or levels of analytics deployed.

Because in the end, outcomes are way more important than outputs.

With this in mind, it can often be advised to start using advanced analytics provided as a service by external parties. Nowadays, companies don’t have to create their own OCR algorithms or language translation models.

Often, they don’t even need to build their own pricing optimisation algorithms, demand forecasting solutions, or marketing propensity models. They can just plug their data into someone else’s solution and fast-track the way to business benefits.

But there are many cases when you don’t want to deploy a ready-to-use solution. Sometimes you want to build your own to fit your specific needs, to create a competitive advantage, or to keep the key intellectual property in the business.

The build vs. buy decision is often less straightforward than it seems. And it is evolving in time! What might have worked for a company during initial stages, might not be enough for a matured market leader.

Evaluating whether a company has a good mix of internally developed and externally procured solutions is a bread-and-butter of data due diligence.

Quite often we see companies spending fortunes to develop elements that could have been outsourced, while others are carelessly relying on external services for processes of strategic importance to the business.

Finding the right balance is an art requiring experience and considerate evaluation. Getting it wrong can become very costly.

This week’s reading list looks at data and analytics maturity, necessity, and organisational considerations for data products.

  • Costs of Being an Analytics Laggard…And Path to Becoming a Leader: I’m including this article in particular for the description of data and analytics maturity. Its five stages — business monitoring, business insights, business optimisation, insights monetisation, digital transformation — are a interesting way to look at data and analytics maturity from the business angle. The descriptions of each stage from the perspective of data management, analytic capabilities, business alignment, and culture are spot on. Including the early signals of the future evolution — see e.g. data monetisation mentioned across multiple stages. (Data Science Central)
  • The Rise of Data Capital: “For most companies, data is their single biggest asset. Many CEOs in the Fortune 500 don’t fully appreciate this fact,” is a quote from Andrew Lo, a director of the MIT Laboratory for Financial Engineering. He said it in 2016. Since then, many has changed, and now more companies even outside of Fortune 500 are starting to realise the importance of data for value creation. If data is the single biggest asset, should it be treated as a by-product of operations and managed as a cost centre? Or rather as a strategic asset with required focus, dedication, and determination? (MIT Technology Review)
  • An Operating Model for Data Products: The article is built around the Conway’s Law, paraphrased as “your systems and data will follow your organisation structure.” What does it mean for the data products in the context of data mesh according to the author? “Data mesh will likely start with local implementations, then evolve to regional implementations, probably within common time-zones (2+/-).” This is obviously key for large multi-nationals, but I believe it applies to smaller and less geographically dispersed organisations too. Sometimes a different team / floor / office / building can almost feel like a different time zone. (Eric Broda @ tds)

Enjoy the weekend!

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

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

Follow me on Medium, LinkedIn and Twitter.



Adam Votava

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