Throughout history, booms have been the driver of innovation. Whether it’s natural resources like coal and oil driving the industrial revolution or the internet’s role in globalization, there is always a key resource that serves as the catalyst for growth.
It’s no secret that today’s “boom” is data. How companies collect and use your information is one of the hottest topics on the news, in congress and at awkward Thanksgiving dinners around the country. Data science, analytics and machine-learning roles have consistently been some of the most sought-after skillsets over the last five years. Most companies, following in the footsteps of the “pioneers” in their respective industries, tend to build the boat while they’re sailing.
I recently had the unique opportunity to build out the entire data executive team—from soup to nuts—for one of the most successfully launched products in modern history. Here are some of the key learnings from that experience.
Who and when—Do we really need this hire now?
The most common recruiting dilemma is “when should I hire this person?” or “are we ready for this role?” — addressing these questions feels like a good place to start.
If you’re beginning to build out a true data function, then you probably have a product in the market that’s gaining traction, and you’d like to capitalize on everything your customers are telling you—or not telling you. The key to being able to act on this information quickly is organization, scalability, and the ability to interpret, articulate and put your data to work. You need a skilled team in place to execute effectively. So how do you begin to build a team?
Step One — Data Platform and Analytics Teams:
- Your platform team will consist of engineering, architecture, data warehousing, infrastructure and governance. While this may seem like an obvious place to begin, many companies are reactive and put off hiring executives in this function. Once they realize their current systems aren’t scalable, they either try to mend issues with new technology, or instead pursue a total platform rebuild. The good news is, the data technology at your data platform team’s fingertips is advancing the function at a record pace. If you give the platform team the resources and autonomy to build your platform to scale from the get go, you won’t fall into the vicious cycle of ripping and replacing technologies. Instead you will have the freedom to layer new technologies and functions on top of a robust data platform.
- The analytics team will serve as the connective tissue between your technology, data and the business. I’m grouping a variety of analytics roles into one category, and depending on your business, this group may even include a business intelligence team. To identify and hire the right analytics expertise, think about your company and customers’ needs and what types of data will take your business to the next level. It might be someone focused on marketing analytics, product analytics or even solely focused on your customers’ data, feedback and insights. At some point, your company may require all of these functions, but it’s crucial to prioritize them effectively.
What’s Next — Data Science:
As your data platform and pipeline evolve, focus on the outcomes your data can now deliver. That’s when data science, machine learning and AI teams come into play. These are arguably the most sought after roles in technology today. Just type “top jobs this year” into your search engine, and I guarantee one of those titles is in the top five of any list. That said, taking competition for talent into consideration is important. Going toe-to-toe with your competitors will drive up the price to onboard key hires, so plan accordingly.
The other thing to consider when building out your data science organization is scarcity of talent. The data science/machine learning function, as defined today, is fairly new. The technology has advanced significantly in the last few years, and to be honest, very few industries are producing the level of talent you probably want or need—more on that later. This makes finding the best talent more challenging, but like the analytics category, there are a few different ways to approach building out your data science or machine learning teams.
- First, make sure you really know what you’d like out of the working relationship between data science/machine learning and your engineering teams, analytics and product teams. Clearly define where the handoffs are between these overlapping functions, and identify the behaviors that will lead to success.
- Next, find the people with the right experience, versus those with the sexiest title from a company you hold in high regard. You might attract an amazing machine learning engineering leader, but what your company really needs is someone to work with the analytics team to build complex models. Understand your business’ needs, then shape the role to optimize your desired outcomes—this should be a required guideline for any hiring decisions.
And Don’t Forget — Stats, Research and Insights:
I’d be remiss not to mention some of the other highly valuable roles like statisticians, researchers and insight analysts. For the purposes of this article, we’re not going to spend much time talking about these functions as they’re not as broadly applicable.
Where can I find the right talent?
Geography Focus:
As you would expect, the best geographies for data talent are regions that have emerging technology industries. Silicon Valley is still the king when it comes to this function, but Seattle and LA also have strong talent pools given Seattle’s growing tech scene (beyond Microsoft and Amazon), and LA’s focus on consumer technology. New York and Boston have strong reputations as well, making them both fertile hunting ground for talent and a good place to establish data functions.
Industry Focus:
Technology companies are producing the best leaders to take your data program to the next level. Think FAANG (Facebook, Amazon, Apple, Netflix and Google) companies, or the most-used apps on your phone. Social media, retail innovators like Jet.com, and even the sports industry are other areas producing strong data talent across the board.
It’s no surprise that certain industries have more sophisticated data programs than others. While many companies are approaching their data strategy in new and innovative ways, there are still some using outdated technology and processes that should largely be avoided.
Can you teach ‘old dogs’ new tricks?
Be wary of people coming from industries that have long been leveraging data to glean customer intelligence. Those industries, while deeply invested in a data first mindset, have taken a very different approach to data over the years. In some cases, they may be behind the times, not having migrated to the cloud or invested in new tools and technologies to keep up with the changing landscape. In other cases their hands are tied by regulation or other constraints that have stifled innovation.
On the flip side, I’ve spoken with many executives whose companies are constantly changing their data technology vendors, which has its own set of challenges and risks. The desire to always be on the bleeding edge of technology doesn’t allow your business the necessary time to get a new technology to the point of creating the intended result.
Lastly, compliance is critical. Make sure that you have a highly skilled conduit between your security, data and legal organization. Too many companies wait until something ‘unexpected’ happens before getting serious about governance—you don’t want your organization to fall into this category!
The Data Boom—It’s here to stay!
Over the last year, I’ve spoken with over 200 data executives whose experience and expertise have shaped my insights on this ever-expanding topic. There’s no question that these roles will be the key to success for so many industries, and the continued evolution of this function will be exciting to watch and participate in over the next decade. As you look to hire data-related roles for the first time or reimagine your data organization, it’s critical to acknowledge: Data is its own department. Ensure that you have a data leader who is a peer to the rest of your organization, and empower that individual to drive change and lead your company into its next phase of growth and innovation.
If you’re interested in learning more about SPMB or how we approach building out data teams at the executive level, please contact us at spmb.com or contact the author, Radley Meyers, directly.
Radley Meyers
radley@spmb.com
