I’ve been running a data team, or actively running point on data operations, for over 20 years now. That’s 8 years in B2C and 12+ years in B2B.
To say that things have changed is a bit of an understatement.
The expectations, roles, tools, technologies, team structures, and required skills—in fact, the whole approach to data—have undergone a seismic shift. Today’s data teams deal with more complexity, more data sources, more responsibility, and more influence on business strategy.
In this article, I’ll break down the differences between the data teams of the past and the ideal configuration for today. I’ll walk you through how companies should build a data team—skills to look for, mistakes to avoid, and challenges to anticipate. Let’s get to work.
Back in the day, we data people were seen as report generators. Order takers, really. We used to be measured by the number of reports we produced instead of how those reports were being used to contribute to business success.
These days, data teams play an integral role in the organization. Business leaders look to us to provide the insights required to become a data-driven business.
Here are just a few ways the environment has changed for data teams:
Tools: Data teams once had limited computational resources and on-premise data storage. Today, we work with scalable, on-demand computational power and cloud data warehousing.
Types of data: In the past, we only handled structured data in siloed warehouses. Today, we’re dealing with structured and unstructured data from an ever-growing number of sources.
Modeling: Data modeling has never been easy, but it used to be simpler. Now, data teams face the complex challenge of modeling data for business users rather than fellow data engineers.
Integrity and governance: Modern data teams must find ways to ensure data is secure, consistent, and accessible. At the same time, they need to meet increased data privacy regulations (like GDPR and CCPA) for increasingly complex data systems.
Delivery: Now, a handful of static reports won’t cut it. Every team in the organization wants data, and they want it yesterday—not in three weeks. The demand for self-service analytics live data is growing, and teams that lack the tools and processes to meet this demand are drowning.
Change: Change management in the data world used to involve a few major updates a year. As businesses shift to an agile mindset, the only constant is change. The demand for data is growing exponentially, not incrementally.
Skills: When I hired data analysts and engineers in the past, I looked for stellar data management skills—and not much else. But modern data professionals need to understand everything from business strategy and data science to artificial intelligence (AI) and machine learning (ML).
Whether you’re a Chief Data Officer (CDO), a business intelligence (BI) director, or the leader of an analytics team, how you build and manage your team is the key to remaining competitive. Here’s a simple blueprint to guide you:
To create a modern data team, you need to audit where you are now and define where you’re trying to go. Here are a few questions to ask yourself:
Roles and responsibilities: Who’s on your team, and what do they do?
Experience level: What kind of work experience does each person have?
Breadth of skills: What specific technical and business skills do they each have?
Cultural fit: How well do your team members fit into your company culture?
Efficiency and productivity: How quickly can your team go from data to insight?
Technologies, systems, and processes
Current data stack: What tools do you use to store, model, and process your data?
Process maps: What do your current processes look like?
Investments: What will you be investing in next, and what are your plans for the future?
Strategy: How do you balance data consumption versus insight creation?
Growth: What’s your projected growth, and what might your company need down the road?
Target audience: Who’s the target audience for your data?
Requirements: What do they need from your data? What would success look like for them?
Effectiveness: How well are you currently meeting their needs?
For each area above, note any gaps and see where you can spot opportunities to improve.
Of course, the way you organize your team depends on your existing structure, but I recommend a hybrid approach.
Here’s what I mean by that:
In this org chart, you have a data team, reporting directly to the CDO or Chief Data Analytics Officer (CDAO). You also have data analysts within each business function, illustrated here with the Finance team. They report directly to the head of the business unit, in this case the VP of Finance, but also have a dotted line of reporting to the CDO. That way, you get the best of both worlds—a shared data team that gives you the economies of scale, coupled with domain expertise for each business unit.
Recently, a counter viewpoint has started to emerge: the idea is that independent functions of department-specific use cases can be fully supported within the function and/or department with their end-to-end resources. Taking this path reduces dependencies with a centralized data team and drives efficiency.
Ultimately, your data team’s structure and design is dependent on your business, goals, and resources. There is no one-size-fits-all solution.
The modern data professional must have a clear grasp of both business needs and the end-to-end data lifecycle. In addition to the traditional data skills, experience, and expertise, look for people with:
Communication skills—You need people who can use data to tell compelling stories, both within the team and with external stakeholders.
Solution-finding skills—Hire people with the ability to navigate ongoing changes related to new data sources and evolving business requirements.
Learning mindset—Recruit data professionals who enjoy learning new skills and adapting to changes in their environment, tools, and responsibilities at pace.
Business acumen—Your data team has to understand what the business needs, and then identify, define, and resolve business problems leveraging data. Look for people who can frame data insights in terms of business value.
I see companies making the same two mistakes again and again when it comes to building and managing a data team:
The biggest mistake you can make when you’re building a data team is to assume that hiring the right team will make you a data-driven company. If you have a data team but don’t use them correctly, you aren’t data-driven—you’re just data-aware. A data-driven company turns information into insights, which they use to make better strategic decisions. Data’s baked into the company culture. It comes up in every discussion and meeting. It’s easy for business users to access, and it’s part of the fabric of day-to-day life.
It’s not a static dashboard or a report you must wait three weeks for.
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If your data team takes a reactive role—generating reports and dashboards and trying to reduce the backlog of demands for more insights—you don’t have a modern data team. As you scale and demand grows, your data team will simply drown under the overwhelming number of ad hoc reporting requests.
Your data team should be a strategic asset, not a cost center. They should help you to identify business opportunities and spot cost savings. Their role is to curate modern insight experiences, not to generate basic reports or waste time on tedious manual data-cleaning tasks.
To avoid these mistakes, try:
Promoting a team culture focusing on customers, user experience, and innovation
Measuring your team’s impact by business benefits achieved, self-service adoption rates by non-technical business users, and return on investment—not the number of dashboards you build
Evangelizing the role of data and business user adoption at an organizational level
Assessing and removing the cultural blockers to modernizing the role of the data team at a leadership level
Positioning business analytics as a revenue driver by aligning incentives and funding to business value
Providing your modern data team with modern data tools that support self-service BI by non-technical users, so they don’t waste their time on tedious manual tasks and can focus on strategic value-adds
With the right structure, skills, and tools, you make the most out of your modern data team. Health tech company Wellthy did just that. By switching to ThoughtSpot, they increased the ROI of their data team by over 300%. Their new modern data stack lets them move more quickly, respond to growing demand, and provide business users with self-serve data insights.
If you’d like to see what your data team can do with the right tools, sign up for a free 30-day trial of ThoughtSpot.