A little while ago, I chatted with Gartner Analyst David Pidsely about a trend I noticed in the job market. It seemed the last 2-3 years, data strategy and governance roles suddenly required coding experience.

It wasn’t my imagination, he confirmed. In 2023, skills and talent shortage were the number one inhibitor to CDAO success. Hiring managers and recruiters have been packing job descriptions with coding skills that don’t always require them.

By doing this, data teams project to potential candidates that they want an ex-data scientist or ex-data architect, so strategists (who don’t code) will likely not apply. Not all, but most data scientists and architects are not natural horizontal thinkers with the program manager and data translator skills needed between business and data teams. Strategy and governance would not be the most natural fit.

Some hiring managers told me they used AI to draft job descriptions. So there’s 1) the AI that doesn’t know better, 2) the recruiter that doesn’t know better, and 3) the hiring manager who may or may not know better—missing out on 98% of their candidate pool!

Should the CDO Code?

Apparently, there’s been a debate about whether Chief Data and Analytics Officers (#CDAO) or Heads of #Data and #Analytics need a working knowledge of #SQL (Structured Query Language). There’s quite a spirited thread on this topic; I’ll save you the trouble. The thread is disturbingly split.

The majority sentiment was something like, “No. If the leader is prioritizing code, they are not growing the capability or evangelizing the strategy.”

A smaller but still sizable number of comments felt CDAOs should know how to code. “It shows they have come up the data path rather than parachuted in from an irrelevant path.”

There were some neutral “it depends on the size of the organization” answers and a few clever “it depends on the use of “working” knowledge. Meaning that an understanding of how data works through a system is more critical than active coding knowledge.

So which matters most?

Students ask this question a lot. But it’s a good refresher for everyone.

That comment about “parachuting in from somewhere” v “coming up a path” stuck with me. There is sometimes an “us and them” aspect to technology and non technology teams. However, how someone navigates their career should not be held against them. Everyone brings something to the table—at least, that is what we learn in DEI training. Why no live it?

The truth is that analytics demands a harmonious blend of both, yet professionals often find themselves proficient in one area, struggling to master the other. The nuanced nature of business analytics requires a comprehensive understanding of both domains, raising a crucial question: When upskilling, which should you prioritize first? Let’s explore arguments for prioritizing business domain knowledge and pure data analytics skills, respectively.

Putting Business Domain Knowledge First

The argument for prioritizing business domain knowledge and understanding stems from these elements' foundational role in shaping effective analytics strategies. At its core, a business discipline is about understanding a specific aspect of an organization’s business model. For example, people analytics is about understanding people (specifically, different demographics of employees)–their motivations, behaviors, and the intricate dynamics of organizational culture. Another example would be marketing analytics, which is about understanding customers–their motivations, behaviors, and desires for products and services the company offers. Without a deep grasp of these aspects, data analytics can become a directionless exercise, generating insights that are accurate yet irrelevant or impractical for real-world application.

HR or marketing professionals with a strong background in their domain have the advantage of context. They can ask more nuanced questions and define relevant metrics aligning with business outcomes. This contextual understanding ensures that the analytics projects undertaken are meaningful and directly contribute to strategic business goals. Furthermore, HR or marketing domain knowledge facilitates more effective communication with stakeholders, enabling professionals to translate complex data findings into actionable insights that resonate with non-technical audiences.

Prioritizing Pure Data Knowledge

On the other hand, there's a compelling argument for focusing on pure data knowledge before delving into the nuances of specific business domain understanding. Robust analytical skills provide the tools necessary to uncover insights that might not be immediately obvious to those with a pure business-focused background. In a world inundated with data, the ability to navigate, analyze, and interpret this information is invaluable.

Professionals skilled in data analytics can apply their expertise across various business functions, from human resources to finance to marketing. This versatility opens up new avenues for strategic decision-making that may not be apparent without a data-driven lens. Moreover, mastering data analytics first creates a solid foundation for layering specific domain knowledge, potentially leading to innovative approaches to current practices that challenge traditional paradigms.

Navigating the Balance

Both business domain knowledge and analytic skills are vital, and the order of prioritization might vary depending on individual circumstances and career goals. For those entrenched in a specific business sector, deepening domain expertise might be the logical first step, enriching the application of analytics with invaluable context and strategic insight. Conversely, professionals from a technical background might find it beneficial to hone their data skills, ensuring they can provide robust, data-driven solutions to complex business challenges.

The Path Forward

Ultimately, the journey towards becoming well-rounded in analytics and business domain knowledge is ongoing. Professionals should seek continuous learning opportunities through formal education, professional development courses, or cross-functional projects that allow them to apply their skills in real-world settings. Networking with peers from both domains can also provide diverse perspectives and insights, enriching one’s understanding of how data and human resource management intersect.

In any business-specific analytics, the synergy between that specific domain knowledge and analytic skills is not just advantageous; it's essential (people analytics, marketing analytics, etc.). The path to mastering this balance may vary, but the destination remains the same: a holistic, nuanced approach to managing and empowering the human element within organizations.