COVID-19 has forced many companies to tighten their belts and invest in only the most necessary functions — fortunately, data is often one of them. Still, the success of your data team relies on more than their ability to wrangle data and build predictive models.
Over the past decade, data teams have become increasingly important for maintaining a company’s competitive edge, leading to greater innovation and more intelligent decision making. Despite this exponential rise to prominence, however, the notion of data science as applied to industry wasn’t even a thing until the early 2000s. Data engineering, now an indispensable part of many data-driven technology companies, was incorporated into the lexicon even later.
COVID-19 has made data’s impact even more apparent. Not only has data been critical to curbing the spread of the deadly virus, but companies are increasingly relying on data to better understand changing customer trends and make smarter spending decisions.
Although 50 percent of data analytics organizations have not yet had to adjust their staffing and hiring plans in response to economic effects of the pandemic, startups have been forced to lay off tens to thousands of employees, many of them data analysts, scientists, and engineers. As the industry adjusts to this new normal, it’s important to set your data team up for success.
To help you scale your team with confidence, I put together four simple guidelines for turning your squad into a force multiplier for your entire organization:
Define your team’s core 1–2 responsibilities. Prioritize accordingly.
Many data leaders I talk to feel bogged down by the various responsibilities that fall on their shoulders. The Harvard Business Review recently published a report defining the seven distinct jobs of CDOs, from the “Chief Data and Analytics Officer” and “Data Governor” to “Data Entrepreneur” and “Data Ethicist.” It’s hard enough just doing one job well — imagine seven!
Similarly, a 2018 McKinsey study suggests that this disconnect between ROI and data analytics occurs because teams “struggle to move from employing analytics in a few successful use cases to scaling it across the enterprise, embedding it in organizational culture and everyday decision-making.”
By setting clear goals for her team rooted in her CEO’s OKRs, this VP of Data was able to unlock true business value with data science. Image courtesy of ThisisEngineeringRAEng on Unsplash.
Amy Smith, Senior Data Scientist at Rebel, an international consulting firm focusing on sustainable and inclusive transportation and a former Senior Data Scientist at Uber, suggests that data leaders dig deep when it comes to gauging how data science can benefit their companies.
“Think about your company’s data needs at a high level,” she said. “What do they care about? What do they need to understand better, and what data will give them those insights?”
Like Amy, I suggest using your company’s top-level priorities to determine how to best leverage your team’s skill set. Stick with 1 to 2 primary roles, as determined by your company’s core objectives. If your company is using Q3 2020 to decide which new products to deploy to your users, perhaps your KPI should be tied to generating more timely analytics on customer behavior; if your CEO wants you to launch your services in the EU, a key responsibility should be mapping compliance work to GDPR requirements.
Once these goals have been determined and signed off on by your stakeholders and CEO, it will be easier to justify your team’s growth and spend as long as you remain flexible to the needs of your business. As we witnessed these past several months, anything and everything can change at the drop of a hat (or should I say, a single data point).
Don’t get hung up on titles.
Given their relative novelty, the terms “data scientist” and “data analyst” can mean any number of things depending on your industry, company, or even team, and it’s important to acknowledge this ambiguity.
In fact, according to Annie Tran, Director of Data Science at Figma, the term “data scientist” as it relates to industry really wasn’t a thing until the late 2000s, when LinkedIn and other big tech companies first started hiring them to better understand user behavior on their platforms. Annie spent the first several years of her career as a data analyst at Willis Towers Watson and later Zynga, before joining Uber as the first data analyst embedded in their product organization.
“When I was hired at Uber, my role was as a data analyst, but so was everyone else’s,” she said. “Some of the data scientists working on our Marketplace team were also data analysts. A couple months in, they changed everyone’s title to data scientist.”
What your data team looks like (and what titles you use) will vary depending on the size of your company and the volume of data you’re using. If you’re a small startup, you may hire a few data generalists, who over time, can start specializing in a different discipline or area. If you’re spinning up a data team at a 500-person advertising company, you may want to start by hiring marketing analytics experts who can hit the ground running with their fancy Marketo dashboards.
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