Keeping Up With Data #92

5 minutes for 5 hours’ worth of reading

Adam Votava
4 min readJul 22, 2022

More and more businesses across industries are undergoing digital transformation. The trend has been further accelerated by the Covid-19 and the spotlight it put on the direct-to-consumer approach.

The digital transformation often focuses on building mobile applications, e-commerce platforms, digitisation of existing analog processes, and creation of omni-channel experience.

As seen in the image above, technology creates digital records for consumers, employees, clients — it creates data reflecting the business. This business data twin can be used to generate insights, and consequently take actions leading to desired results/outcomes.

There are three components of the process I’d like to highlight. Technology, people, and data. Technology dominates the budgets. People (and the culture change) dominate the rankings of challenges to digital transformation. Data is growing in its importance.

From being an afterthought of the technology and source of frustration for people, it is becoming the glue tying the technology and digital processes to the business outcomes.

We shouldn’t assume that technology will generate data needed to take the actions leading to the sough-after results. We should work backwards — from desired results, to actions leading to them, to insights required to take the actions, to data required for the insights. And only then invest in the technology.

Anmut’s Data Ledarship Report (May 2021) reads: “When it comes to transforming a business, data leaders say data is almost twice as critical than technology and faces four times the challenges. Yet data receives just under a third of the attention and a seventh of the budget.”


I don’t think the solution lies only in increasing the data budgets. It’s about being smarter around technology as a source of data.

What other articles caught my attention this week?

  • Is Data Scientist Still the Sexiest Job of the 21st Century? Who doesn’t remember the article about data scientist being the sexiest job of the 21st century? I used it when naming a new team in a bank I worked for. I spoke about it when I was hiring people into the team. I referenced it when explaining why the team should focus on business problems, not technology. It’s been ten years now, and data science has evolved significantly. Back then, everything was new, everyone was creating their own interpretation of data science (mine was data science is about solving business problems with data and analytics). We were experimenting with tools (remember R?), we were explaining why we need access to data and flexibility in analysing it. We were establishing ourselves as partners to business. Ten years in, many things are easier, but there are still plenty of challenges ahead. The data science has established itself as an industry. Now, it’s about living up to the expectations set up by the sexiest adjective. (HBR)
  • How to use Customer Lifetime Value (LTV) for data-driven transformation: Maximising the full value of every customer relationship is the proposed purpose for data-driven transformations. The full value of the customer is not only the past value, but future value too. It’s the lifetime value — LTV. How should your company define the LTV depends on the business and its strategy. “The LTV calculation approach that you choose will greatly inform the day-to-day decisions that your leadership team will make. It will guide the investments that marketing makes, the incentives that sales provides, the design of your product, the pricing of your services, and the product mix that you offer.” There are multiple widely-used formulas out there — for retail, subscription products, or multi-tier products — that can serve as inspiration. (Lak Lakshmanan @ tds)
  • Why it’s time for ‘data-centric artificial intelligence’: The boom of AI as seen in the last decade called for large amounts of training data. But despite the importance of the data, the focus was often on the algorithms. And the commercial applications aimed at multipurpose AI systems addressing very high-value problems across companies and industries. But every company’s reality is slightly different. “Heterogeneity in the physical environment, which is very difficult to change, leads to a very fundamental heterogeneity in the data,” Ng said. “These different sorts of data need different custom AI systems.” To leverage AI, every company needs to focus on its data quality and consistency. I’m sure we will now see the focus moving to the data. (MIT Sloan)

And now, let’s enjoy the Friday evening!

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)