WTF is a "Data Team"?

Joe's Nerdy Rants #44 - Weekend reads and other stuff

I'm unsure what’s in the air, but I’ve had many conversations about data teams over the last two weeks. Typically, the themes are along the lines of:

• Obviously, WTF is a data team?

• How can my data team stay relevant? (Shoutout to

  SeattleDataGuy
  )

• Do I need a data team?

• What if my team uses data to make a product? Is that a data team?

WTF is a data team? Good question. The answers are all over the place. It could be a team officially called “the data team.” Or, people might also use data to enable things within an organization, whether that’s process improvement, operations, or improving a customer-facing mobile app. In my career, I’ve seen the term “data team” used almost every day, but I don’t feel like there’s a single definition. Much of this is because organizations differ in their data uses, which I’ll get into. So, what is a data team? As the old saying goes, you know it when you see it.

From my experience, there are two main types of data teams. These often exist in separate universes.

In Universe 1, we’ll call it Enterpriseland, where data teams usually serve a back-office, internally-facing IT function. These teams provide dashboards, “insights” (whatever those are), and reports to the business. Maybe a data scientist sprinkles some “AI” glitter now and then. These teams are also owned by IT (usually controlled by a CIO or sometimes a CDO), who mandate their work and maintain the data team’s budget. The core thing about Enterpriseland is data isn’t what the company sells to the end customer. Data is a byproduct (sometimes called “business exhaust”) of the enterprise's day-to-day operations. Data teams are often tasked with making sense of this data, which is usually tricky because data is frequently not the first consideration of the enterprise operators. As enterprises modernize, this might change. However, for the majority of enterprises in the world, data is not a first-class consideration.

In Enterpriseland, it’s often hard to judge the effectiveness and ROI of a data team. Does the team deliver “business value” (whatever the hell that is)? This uncertainty is widespread. My question of data team effectiveness boils down to asking, “What happens if the data team disappears tomorrow?” If you answer along the lines of “all hell breaks loose”, then keep your data team. If the answer is nothing, then why have a data team? If the answer is “I don’t know,” this is a grey area. Figure out who the domain experts on the team are and if the data is in good shape. Could specific tasks of the team be automated, with the domain expert (treat them well) around to answer ad-hoc questions? Are there reports that don’t need to be sent out so the data team can focus on doing value-added work? Could you embed the data team into business units to improve areas of your business? Again, this is the murky area I see many data teams in. Even the team might not be sure if their enterprise needs them around. Calculating the ROI of a data team is tricky, and I don’t have a universal answer except to make sure the data team helps drive KPIs for the enterprise (these will vary by company and department) in a positive direction. By the way, this is the broader problem in which IT is viewed as a cost center. Enterprises think they need IT but are often unsure if they’re maximizing their investment. This has been the case for several decades, and IT still struggles with this question today.

Universe 2 is Productland. Things are different in Productland, which moves much faster than in Enterpriseland. Productland “data teams” focus on delivering products. For example, a ride-sharing company does something very data intensive - getting a ride to a customer promptly and safely. In Productland, data IS the product. There’s a flywheel where more data is used in better ways, which means the product performs better. Better products mean users and paying customers are happy. The data team - this could be the software engineers themselves, a team of data and ML engineers, data scientists, etc - inherently knows precisely what it contributes to the business. If introducing a new feature improves a KPI, attribution to the data team should be very clear. Contrast this with Enterpriseland, where the value of sending out a dashboard is often unclear. In Productland, data enables better products whose ROI can be calculated.

Data teams will continue struggling to justify their value as long as data in Enterpriseland is a back-office function. But there’s hope. Enterprises are starting to recognize the importance of data. Some are modernizing. Today, this is mainly being driven by the AI hype cycle. Another driver is the popularity of data-product thinking, where data is used to deliver data-enabled products and functionality to end customers. The significant distinction is the focus on end-customers. As long as data is internally focused, it will suffer from the same politics and shortsighted behavior that’s plagued data teams for decades. When data is used to improve the experience and lives of end customers, the benefits of data (and data teams) are far more evident.

Hope you have a fun weekend!

Thanks,

Joe

P.S. If you haven’t done so, please sign up for Practical Data Modeling. There are lots of great discussions on data modeling, and I’ll also be releasing early drafts of chapters for my new data modeling book here. Thanks!

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Cool Weekend Reads

OpenAI's Sam Altman is the ultimate 'personality hire.' The industry is full of them. (Business Insider)

Roadmap: AI Infrastructure (Janelle Teng)

Microsoft AI CEO Mustafa Suleyman audits OpenAI’s code (Semafor)

Will Cloud Software Be Ready for Its AI Moment? (WSJ)

Apple WWDC: AI Announcements Will Enable Home Robot, AR Glasses, Camera AirPods (Bloomberg)

Introducing Apple’s On-Device and Server Foundation Models (Apple Machine Learning Research)

Microsoft Chose Profit Over Security and Left U.S. Government Vulnerable to Russian Hack, Whistleblower Says (Pro Publica)

Yes, artificial intelligence is running for mayor of Cheyenne; city, county clerks comment on candidate VIC (County17)

Python at the Speed of Julia (Glass Notebook)

Study finds 268% higher failure rates for Agile software projects (The Register)

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