In today’s data- and AI-driven world, chief data officers face the challenge of structuring an enterprise data team that is trusted by all stakeholders across the organization and delivers against business goals. Moreover, data leaders need to be able to clearly articulate to their stakeholders with whom they should communicate as well as when, where and why. To build an adaptable and effective team, data leaders should focus on the functions required to achieve business outcomes, rather than getting caught up in boxes on an org chart.
Specifically, the process of crafting organizational charts can be tedious and uninspiring, but when we shift our mindset to think of building a data team as drafting an all-star sports team, the process becomes much more exciting. Just like in sports, a winning data team is not just about individual positions but about the diverse skill stacks, strengths and unique lived experiences the players bring to the table. By approaching data team building as a celebration of talent and collaboration, data leaders can create a dynamic and effective data organization structure.
The myth of the unicorn do-it-all data professional
This is not necessarily a new idea. In his 2012 TED Talk, investor and entrepreneur Ernesto Sirolli reminds us that no single individual can excel at all aspects of running a business. Just as in sports, it is unrealistic to expect someone to be the coach, the goalie, the frontline offense, the rear defender and the ball all at once. Similarly, in the world of data, expecting professionals to possess expertise in every aspect of the field is unrealistic and counterproductive — rarely do you find a great Python programmer who can also do exceptional data storytelling. By acknowledging this, data leaders can set more realistic expectations and build teams with complementary skill sets.
But data leaders still need to help others visualize the invisible functions and value that data team members bring to the table so they can get budget and buy-in from nontechnical stakeholders to work with their data team. Data leaders must be able to articulate what groups of professionals others should speak to — with whom, when, where and why.
The six functions for a modern enterprise data organization
To structure an effective data organization, avoid being distracted by fancy titles and false hopes pinned on the best practices of other organizations. A one-size-fits-all approach does not exist when crafting effective organizational charts for data teams; instead, look beyond the title to find the intrinsic function and purpose of every role to help craft your use cases and your eventual dream team. It is crucial to identify and embrace the six essential functions of a modern data team.
1. Designers
These individuals work closely with stakeholders and makers to integrate business requirements into data solutions. They play a vital role in developing frameworks, services, products, datasets, reports, applications and slide decks that align with business needs.
2. Makers
Responsible for building and implementing data solutions, makers synthesize insights from data to drive actionable outcomes. Their activities cover a wide range, including developing machine learning models, building data pipelines and creating data visualization dashboards.
3. Communicators
Successful data organizations prioritize the value of data fluency and related solutions. Communicators play a crucial role in translating this value to foster awareness and adoption across the organization. By effectively communicating the benefits of data-driven approaches, they drive organizational buy-in.
4. Operators
These individuals configure and manage the systems supporting data functions. They maintain production data applications and AI models, monitor systems continuously, perform regular maintenance and optimize system performance. Operators ensure the smooth functioning of data operations.
5. Iterators
Responsible for driving the long-term data strategy of the organization, iterators continuously refine and optimize data priorities. They integrate innovative learnings from other domains into the data ecosystem, keeping the organization at the forefront of data-driven innovation.
6. Regulators
Data governance is crucial for maintaining data security, access controls and ethical practices. Regulators develop and enforce data governance policies, oversee data security measures and ensure compliance with sustainability and ethics standards.
High-performance IT teams pave the way for enhanced curiosity velocity and AI enablement
As executive boards push for the adoption of artificial intelligence, data leaders must navigate the complexities of structuring their organizations effectively to enable employee curiosity velocity. By focusing on the six essential functions of a modern enterprise data organization, data leaders can build teams that align with business outcomes and help their organization thrive in the era of AI. Embracing the strengths and skill stacks of many diverse data team members, rather than expecting data leaders to hire one or two do-it-all unicorns, will lead to the creation of winning data teams that drive success in the data-driven world.
This article was written by Kim Herrington, a senior analyst within Forrester’s business insights research practice team, providing expertise in data leadership, organization and culture. Her research coverage includes data literacy, data storytelling, data leadership and culture, insights-driven businesspeople, insights-based organizational models, chief data officer research, and insights communication. A former data journalist, Kim holds a master’s degree in healthcare administration from D’Youville University and a bachelor’s degree in biology from SUNY Oswego.
Learn more about the crucial components for establishing an effective data and AI team at Forrester’s Technology & Innovation Summit North America, taking place September 9–12, 2024 in Austin, Texas, and digitally.
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