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Heads of Data vs Chief Data Officers

The data & analytics space loves to create new roles and titles. Often, it’s hard to tell exactly what these new roles do, or if their proposed responsibilities are already being met elsewhere. One role I’ve uncovered while researching data product management is Head of Data. While this sounds similar to the Chief Data Officer role, the two are really quite different. This post looks at some of the differences in these two positions.

Enterprises have been adding Chief Data Officers (CDOs) to their C-Suite since the early 2000s, hoping to support business strategy with a cohesive data strategy. The CDO is responsible for many aspects of data management, like:

  • Data governance at the corporate level, commonly overseeing some form of enterprise data governance team.
  • Data monetization strategies across the enterprise.
  • Creating a data vision and strategy around how data is managed, stored and used with what the business’ objectives are and ensuring alignment.

As you can tell, Chief Data Officers are focused on interacting with the business and how it uses data. There is less emphasis on technology and infrastructure, since those remain the responsibility of the CIO or CTO. CDOs may report to the CEO directly, but it’s also common to see them reporting to R&D or finance.

If CDOs are primarily focused on governance with an eventual goal of creating a data-driven enterprise (whatever that means), Heads of Data are a new type of role designed around growth-oriented customer-facing activities. Instead of taking an enterprise scope, Heads of Data are more like senior product managers. This role may be expected to:

Like many new data-centric jobs, Head of Data is a multi-faceted role that requires people to be effective collaborators and communicators, but also adept technologists. Prospective Heads of Data must have deep domain knowledge and be as comfortable optimizing SQL as presenting to executive and board-level stakeholders.

Alternative titles for Heads of Data include: Head of Customer Analytics, Head of Business Intelligence, Head of Analytics and Head of Digital Analytics.

Unlike the CDO, Heads of Data likely report to the Chief Product Officer or Chief Customer Officer, but the reporting structure will depend on the company.

Chief Data Officers often have a strong foundation in data governance or risk management, which I believe limits their potential upside to the business. Heads of Data are likely coming from product management, data science or engineering backgrounds, which focus more on customer value and growth. Unlike the CDO, which focuses on what can’t be done, Heads of Data focus on what can. While both roles may be required for enterprises going forward, the growth-focused Head of Data is a great career progression for data & analytics generalists looking to make the next step.

Photo by Stephen Dawson on Unsplash

Why Data Stored as DNA Won’t Go Out of Style

Whether it’s a sixty year-old unemployment system written in Cobol or a data warehouse on its last legs, every enterprise grapples with legacy technology. What’s worse, every technology will eventually become legacy, regardless of how vibrant it looks today. But what if that wasn’t the case? What if there was a technology that would be viable as long as humans are around to use it? 

During a recent webinar on DNA computing, I described DNA-based data storage as future-proof. Some of the attendees pushed back on this idea and I wanted to explore it here. 

DNA data storage is based on two families of technologies and processes: DNA synthesis and DNA sequencing. Sequencing gets most of the attention in the press, typically around the falling prices to sequence a genome. On the other side of the DNA story, synthesis is the process of creating DNA either through a biological process or in a lab. In the context of DNA computing, DNA synthesis is used to write strands of synthetic DNA that represent your data. Those strands are sequenced to read the data, which is then converted back into its digital form. The figure below attempts to illustrate this. 

These two key technologies, sequencing and synthesis, will continue improving because they are essential to life sciences. The need for them to become faster and cheaper is constant. But whether synthetic or organic, they’re still just working with DNA. There’s little risk that we’ll create a DNA 2.0 that isn’t backwards compatible with current and future sequencing and synthesis technologies. After all, those are the technologies we’d use to create that new version.

As long as there are humans around to read it, DNA-based data storage is the only future-proof storage technology we’re likely to discover.* This, coupled with its incredible storage density of 200PB/gram and a half-life of 500 years, makes DNA storage one of the most compelling technologies in development. 

*This doesn’t mean that the data your storing as DNA can’t be damaged. High temperatures and ultraviolet light degrade DNA over time, so you’ll still need to take some precautions to ensure fidelity.