Keeping Up With Data #111

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readDec 20, 2022

Carruthers and Jackson have recently published their Data Maturity Index revealing challenges companies are facing when getting the most of the data they collect.

There are many approaches to assessing data maturity. As the industry is evolving and people are educating one another by sharing their challenges, best practices, and experience we see increasingly often that data and analytics maturity is not only about data and analytics.

At DataDiligence, we look at strategy, analytics & data, people, and infrastructure aspects of data ecosystem. Carruthers and Jackson’s data maturity model comprises purpose, people, method, and tools (or technology).

While the maturity assessment often takes form of the comparison against an elusive benchmark, it’s the strategy (or purpose) that defines how well is the company doing with respect to its own business challenges and ambitions.

We often hear about the importance of having a data strategy tightly aligned with the business strategy. But the same is key for the maturity assessment. Rather than benchmarking your company to an arbitrary group of peers to play on the FOMO note, assess how ready is data and analytics to enable and support your business strategy.

Today’s reading list looks at ChatGPT, data maturity index, and forecasting of electricity prices.

  • ChatGPT and the Imagenet moment: ChatGPT has flooded the news and social media with examples covering the good, the bad, and the ugly of automatically generated texts. Benedict describes ChatGPT (or any other generative model) as an intern, who has read every book on a planet and can mimic what looks like a credible answer to a well-defined prompt. A “confident bullshitter”, if you may. But just like with a confident intern, you need an expert to create the reasonable prompt, validate the facts, and ensure integrity of the message. We are already seeing content becoming a commodity. With excess supply of content, people will need to focus on careful selection of what to read, use, or ask for recommendations. (Benedict Evans)
  • Data Maturity Index: “It’s a near universally acknowledged truth that to be a successful, modern organisation, businesses need to use their data effectively. Despite this growing recognition of the power of data, many organisations are still failing to use it properly. Almost all sectors talk the talk when it comes to data transformation, but just how mature are they in their use of data?” The gaps are mainly in data literacy, disconnected (or no) data strategies, and weak data governance frameworks. (carruthers + jackson)
  • Typical Year Forecasting of Electricity Prices: Accurately forecasting electricity prices is difficult under normal circumstances, let alone during the current situation. The solution rarely lies in using more complex and sophisticated models, quite the opposite. A robust, simple, explainable solution built on expert domain knowledge — such as ‘typical year forecasting’ method — can deal well with the inherent variability of the energy prices. (ADG Efficiency)


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

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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)