Strategy and Spreadsheets: The Building Blocks of an Analytics Leader

To evolve into effective analytics leaders, early career data professionals must invest in their soft skills as much as in their technical expertise.

One of the most compelling reasons to pursue a career in data analytics is the diversity of paths along which a data professional’s career can evolve. A collaborative analytics team includes a range of specialized practitioners — data engineers, data scientists, data strategists, data visualization experts, and so forth — all of whom work together to drive business value for their brand partners.

While most data professionals end up gravitating toward a specific discipline naturally as their career progresses, there are certain broadly applicable skill-sets that nearly every early career professional should focus on developing. Some of these are self-evident: a familiarity with Java, Python, R, and/or SQL, a solid foundation in statistics, a basic understanding of popular business intelligence tools. Others — particularly those that fall under the umbrella of soft skills — are not.

However, irrespective of whether an early career data professional dreams of becoming a senior data engineer or a data strategist, finding professional success in today’s analytics landscape hinges on being able to marshal resources — human and otherwise — effectively and think strategically about one’s work. By complementing their technical know-how with the softer skills explored below, a data professional can position themselves for success whichever professional path they choose to pursue.

  1. Understand How to Be a Brand Partner

Constant communication — and, wherever possible, collaboration — among project stakeholders is essential throughout the duration of a relationship between a brand and its analytics partner. From project status calls with anywhere from a half-dozen to a dozen parties to broader brand roadmapping exercises, data professionals often end up participating in highly collaborative work.

Insofar as data-driven decision-making has become a necessary condition of business success in many industries, in an ideal world, a brand should trust its analytics agency partner to take the lead in managing the at times complicated dynamics of cross-team collaboration. That said, especially in the early stages of a brand-analytics agency partnership, data professionals must make a concerted effort to earn this trust — that is, to become a partner in function as well as in name.

Finding ways to drive value above and beyond their strict remit is the most effective way for a data professional to step into the role of a genuine brand partner. This is not to suggest that a data professional should engage in exploratory work at the expense of the work they have been expressly assigned, but rather that they should always be looking for opportunities to elevate their brand partners’ business through analytics.

Because data-driven value generation hinges on asking the right key business questions (KBQs) at the start of a project, data professionals must position themselves to consistently deliver supplementary value campaign and to actively work towards being integrated into the earliest stages of their brand partners’ work.,. Beyond retrospective reporting — which certainly has its place as a means of documenting the value a team of data professionals creates — a brand can use analytics to better understand the demographics of its audience, tailor its advertising strategy to its audience’s media consumption habits, create detailed profiles of its distinct audience segments, and much more. However, all of this is only unlocked if the brand asks the right questions of its data — which is by no means a guarantee.

Helping their brand partner arrive at “the right questions” is arguably a data professional’s most critical role, but doing so requires both analytical expertise and extensive knowledge of the brand’s business. As such, it is not enough for a data professional to be an expert in just their craft; they must also be a domain expert. That is to say, they must prioritize their work based on what matters to the business stakeholder(s) that hired them. They not only must be a technical practitioner, but a trusted brand partner.

  1. Become a Student of the World

In truth, analytical expertise and extensive knowledge of a brand’s business are not always enough to position a data professional to help a brand ask the right KBQs. This knowledge and expertise must be properly contextualized in the brand’s competitive landscape, its broader industry landscape, and, ultimately, the world at large. To develop the capacity for such dynamic contextualization, a data professional must make a concerted effort to pay attention to everything happening around them — to become “a student of the world.”

Sophisticated data professionals dedicate ample time and energy to staying up-to-date on the ins and outs of the latest analytics tools and techniques, but they also remain attuned to shifts in the way people navigate the world — after all, consumers are people first and foremost.

Consider, for example, a platform like Instagram. While, for the right brand, Instagram can be an immensely valuable channel, it is a data professional’s responsibility to make recommendations to their brand partner about how the brand should approach using Instagram. These recommendations must be grounded not only in a nuanced understanding of the brand’s audience and a technical understanding of how Instagram advertising works, but in a fundamental understanding of how people engage with Instagram Live versus Instagram TV versus Instagram Reels versus “traditional” promoted Instagram posts. A data professional might be able to develop this understanding by reading industry trades, but they could just as easily — and perhaps even more effectively — develop it by being attentive to how they and their friends and family engage with Instagram.

While data professionals should by no means privilege their own anecdotal evidence over cold, hard data, they should recognize that even though data analytics is a science, its application is an art. Using data to drive business value boils down to shaping how real people engage with a brand in the real world, a challenge that is made far easier when a data professional makes a point of deeply engaging with the world — in both their professional and personal lives — themselves.

  1. Embrace a Project Management Role

On the point of ensuring that data analyses reflect — or, more accurately, approximate — what is happening in the real world, a brand that looks only at its website performance or only at the performance of its digital advertising will end up with an incomplete picture of the state of its business. Real consumers do not experience a brand through a single touchpoint or channel, meaning understanding the reality of how consumers perceive and interact with a brand involves integrating multiple types of analyses into a coherent, panoramic brand perspective.

Doing so can involve collecting data from several discrete organizations — a brand team, the brand’s media agency, the brand’s creative agency, and so forth — each of which may need to run through its own set of data collection processes. Particularly in an agency context, establishing rigorous processes for determining and documenting where specific data exists, which stakeholders at which organizations should be contacted to secure this data, and what deadlines these stakeholders should be given to share their data requires adept project management.

As unglamorous as this work may seem on the surface, it is fundamental to the success of an analytics partnership. When it comes time to start executing actual analyses, the cleaner the data is and the clearer it is which organization the data came from and what date range the data covers, the better. In short, the higher the data quality, the stronger the foundation upon which a team of data professionals can build their analyses and insights. And while a data professional is not the only type of individual capable of filling this project management role, there is a great deal of value in data professionals taking a hands-on approach to orchestrating the collection of the “materials” of their work — not least because personally overseeing data collection processes ensures a data professional will have everything they need to deliver the analyses they have been asked to perform.

Preparing to Lead in The Data Decade

In many ways, the fragmentation of the digital media landscape over the course of the last two decades has at once created more opportunities to perform analytics and increased the complexity of driving tangible business value through analytics. An analytics team operating in today’s landscape can spin up dozens of dashboards that track a brand’s performance across a wide variety of channels, but these largely retrospective, often siloed analyses seldom represent the full extent of what data analytics has to offer.

To produce game-changing results for their brand partners, data professionals must think more expansively about what their roles entail. Analytics is no longer just about number-crunching — if it ever was. It is about understanding a brand’s business, contextualizing this business against the backdrop of the real world, and making sure each and every stakeholder has whatever they need to not only produce data-driven insights, but act on them.

Ultimately, sophisticated data analytics is as much about strategy as it is about spreadsheets, and it is the data professionals who manage to develop competencies in both who will assume the most prominent leadership roles in “The Data Decade.”

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