Keeping Up With Data #114

5 minutes for 5 hours’ worth of reading

Adam Votava
Data Diligence
4 min readJan 27, 2023



A data leader needs to be in tune with their business, its strategy, needs, and context. Every industry has its specifics, every company has its own journey, ambitions, and challenges.

Just as the image above illustrates different roles of data leaders when setting up a greenfield data function or supporting a rapidly growing business.

But aren’t there similarities in how you go about the execution? How you effectively and efficiently manage the strategic, tactical, and operational elements of the role of a data leader and their teams?

What do you do to continually identify data-driven business opportunities, recruit and retain top talent, setup and manage high-performing teams, establish and execute data governance and management processes, select and implement data and analytics tools and technologies, operate, maintain and troubleshoot analytical solutions in production, improve data and analytics capabilities, build strong relationships with key stakeholders, maintain relationships with vendors and service providers, develop yourself as a leader, or keep an eye on the latest trends in the fast-evolving space of data and analytics?

Being a methodical person and an efficiency junkie I spent the last few weeks breaking the problem down from multiple angles. I was listing out individual tasks and activities on the most granular level, breaking down key responsibilities of a chief data officer, thinking about types and frequencies of meetings and other rituals, and designing tools to drive things in the desired direction with minimal effort.

I have also spent a lot of time reading in search for best practices, lessons learnt, or inspiration. It’s hard to find anything specific (and evidenced) on the role of a chief data officer beyond very high-level principles, job descriptions, or anecdotal experience.

Where this process left me so far, is that it is important to clearly differentiate between tools and rituals used to influence the data strategy execution at strategic, tactical, and operational levels. And be very diligent and deliberate in using and enforcing them.

Another important realisation was that each level of execution is running at a different frequency — at the risk of oversimplifying we might say that strategic decisions are taken quarterly, tactical ones monthly, operational weekly, and the work gets done on a daily basis.

As John Doerr said: “Ideas are easy. Execution is everything. It takes a team to win.” So, let’s put it in practice now and iterate quickly.

Today’s reading list looks at agile data science, design thinking, and alternative to RACI.

  • Agile Data Science: Agile methodology is commonly used in software development. Can it be applied to an R&D-heavy space of data science? In many instances yes, but it’s important to find the right blend of agility while respecting the data science lifecycle. It’s worth consideration because it allows for rapid experimentation and iteration on models, and encourages collaboration between data scientists, stakeholders, and other members of the project team. Plus the inspiration from the SWE world might help to highlight the need for both development and operations in data science too. (Data Science Process Alliance)
  • Data Science and Design Thinking Belong Together: Design thinking is a problem-solving approach that emphasises empathy, experimentation, and iteration. It is often used in product design, and the article argues that each stage of design thinking can benefit from data science. I’d argue that it goes the other way too. That is that data science can benefit from applying design thinking principles. Anyway, it seems clear that the combination of design thinking and data science can lead to more effective, efficient, and user-centered solutions, that are more likely to be adopted and used by the end-users. (frog)
  • The limits of RACI — and a better way to make decisions: “If told that you were responsible or accountable for a decision, would you get to make that decision?” Eloquently demonstrated one of the pitfalls of RACI. McKinsey recommends an alternative to RACI called DARE — standing for deciders, advisors, recommenders, and execution stakeholders. At a glance it’s obvious what is required from the each actor. Nōmen est ōmen. (McKinsey)

Enjoy the weekend and remember that keeping up with data is easier than catching up.

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

Thanks for reading!

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Adam Votava
Data Diligence

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