In this blog series, Jason Larson, head of content at IIA, sits down with IIA experts who serve as sparring partners for Research and Advisory Network clients. IIA’s RAN expert community, with over 150 active practitioners and unbiased industry experts, is dedicated to advising data and analytics leaders on key challenges unique to their enterprise. 

Instead of trying to hunt down the elusive unicorn data scientist— you know, those ninja modelers who also possess superior business acumen and communication skills—Asheeka Hyde, technology director of data and analytics at SSP Group, has invested her efforts in building unicorn teams. Why? Because unicorn data scientists simply don’t exist, nor should they. For Asheeka, a unicorn team possesses cognitive diversity where each member excels in a specific area, enabling them to work more effectively and innovatively than any individual could alone. She emphasizes that creating such a team requires rethinking traditional hiring practices, prioritizing deep expertise over general skill sets, and fostering an environment where learning, mentoring, and challenging discussions thrive.

In this wide-ranging conversation, Asheeka discusses the challenges and opportunities in building and managing her unicorn teams and thoughts on how data science teams will evolve as AI becomes a leading conversation in the board room.

We've all heard of unicorn companies and individuals, but you bring a unique perspective with the idea of a unicorn team. Could you explain what that means to you and how it differs from the elusive hunt for a mythical unicorn data scientist?

Asheeka Hyde: Yeah, it all started with this idea of the "unicorn data scientist"—someone who could translate business needs into analytics requirements, do all the data wrangling, model, engineer features, visualize the data, and tell the story. I found myself wondering, "Who is this person?" Even if you had someone capable of doing everything end-to-end, the process would be incredibly slow in terms of workflow.

Given my background in retail, I know it's tough to find talented people, and retail doesn't always offer the highest salaries. People join retail for different reasons, like a passion for the business and customer interaction. This led me to appreciate the power of cognitive diversity, where diverse teams think differently and achieve better results. I recall working on university projects where our diverse socio-economic backgrounds enriched our problem-solving approaches. That’s where the magic happened.

Over time, I realized that while having a data analyst team was once a competitive advantage, it now only brings you to parity with others. However, a truly innovative data and analytics team can still offer a competitive edge through innovation and cognitive diversity.

This realization led me to the concept of a unicorn team—a team where each member excels in a specific area. This concept challenges traditional hiring practices. I've argued with HR for candidates who excel exceptionally in one area rather than being average in all. I look for individuals who are not only experts but also willing to mentor and learn from others. This fosters an environment where team members are comfortable enough to engage in challenging conversations.

To build a unicorn team, it’s about assembling a group of people with distinct skills and perspectives who are open to learning and willing to respectfully challenge each other. And funnily enough, the idea also drew inspiration from my daughter's Lego figures—a knight, a unicorn…bringing a playful element to it. The notion of a unicorn team, therefore, becomes much more accessible and, I believe, more effective than relying on a single unicorn individual. This team-based approach distributes capabilities across several hands, enhancing effectiveness and innovation.

So, I’m imagining we have this team of specialists, each a rock star in their own right, addressing a critical skill. Ideally, they learn from each other, collaborate, and stay humble. Sometimes, though, experts prefer to stick to what they know best, avoiding collaboration. Building this unicorn team sounds quite challenging, pulling these specialists together to learn and collaborate. How do you manage that?

Asheeka Hyde: For me, my role is to find what motivates people and get them excited about their work, showing them that collaboration is the key to success. People who excel want to see their efforts make a real impact in the business. Even those who are more research-focused want their work to be realized, not just valued. I try to demonstrate that collaboration facilitates this.

To make this happen, I take them on a journey, explaining why working together is essential. It's about showing them that they can focus on what they do best by delegating other tasks to their teammates. It’s crucial to find what motivates each individual and create an environment that nurtures this.

Top performers are often motivated by the chance to work with peers who are equally skilled. This mutual respect can foster easier collaboration. Part of my job is to facilitate social interactions that build these personal connections, making it easier to challenge and push each other in a work context.

I spend a lot of my time crafting an environment where people feel close enough to talk openly but also respected enough to challenge one another constructively. It’s about stretching their capabilities without stressing them out, ensuring the work is engaging and provides continuous learning opportunities.

For instance, I implement three-week sprints that end on Thursday noon, giving the team the rest of Thursday and all of Friday off before the next sprint starts. This break allows them to pursue their interests or develop new ideas that could benefit the business. If someone brings a compelling idea that the business hasn’t considered yet, I try to support them in developing a proof of concept.

It's also important to allow team members to innovate on company time. Expecting them to only pursue personal development or innovative projects in their own time isn’t fair if it benefits the business. Some of our best work has come from these kinds of projects where team members are given the freedom to explore their ideas.

What other strategies or tactics have you used as a leader to foster a unicorn team environment?

There are several strategies I focus on as a leader to foster a unicorn team environment. One important aspect is to cultivate a perception of analytics as a value center rather than just a cost center. It's about changing how others see our work, emphasizing that we're not just a support function but a key contributor to the company's success.

Engagement with stakeholders, especially getting in with your CFO, can change the narrative. They view operations through the lens of cost versus value. Demonstrating that analytics can drive value transforms their willingness to invest in our initiatives. This perspective shift requires us to be effective in communicating the benefits of our work, which is where the selling of analytics comes into play. We often fail to market our achievements adequately. It's not enough for our work to speak for itself. We must articulate its value in a compelling way.

For instance, when a team I led developed its first Power BI dashboard, the excitement was internal. However, externally, what mattered more was its application—like its deployment in over 200 stores. Highlighting such applications is more impactful than just celebrating technical milestones.

Creating a brand around our team helps too. Simple things, like a unique logo or standardizing the presentation style, can really help with recognition and trust within the organization. It positions our work as a mark of quality, which others start to rely on.

Also, fostering an environment that encourages collaboration is essential. It involves facilitating social interactions and informal discussions that build relationships and trust. These relationships are foundational for team members to feel comfortable enough to engage in productive, challenging discussions without feeling threatened.

Learning and development are also key. Like I said earlier, we provide structured "me time" during work hours for personal projects, encouraging innovation. If someone has a promising idea, we support them to develop it, even if it's not immediately on the business's radar. This approach supports continuous innovation without over-burdening the team.

Finally, I’m a big advocate for an MVP approach, where stakeholders are involved early in the project. This early involvement helps in aligning the project outcomes with business needs and facilitates smoother adoption since stakeholders feel a sense of ownership over the solutions.

These strategies collectively help in creating a dynamic environment where a unicorn team can thrive, characterized by shared learning, mutual respect, and a strong alignment with business objectives.

Absolutely, practice must be the key ingredient in all this, right? The constant repetition of these strategies you've mentioned seems necessary.

Asheeka Hyde: Absolutely, practice is essential. I wouldn't make any drastic changes or introduce new strategies without discussing it with the team first. These specialists are intelligent and eager to learn; imposing changes could be counterproductive. It's all about gradually building trust so that they're open to changing their methods. For instance, in advocating for agile methodologies like MVP, it's important to first ensure that the team is on board.

In one of my previous roles, I asked the senior analysts to take the lead on refining our processes. What they developed was not only effective but also superior to anything I might have suggested. It was an enlightening experience for me, reinforcing the importance of being flexible and open to learning from your team. This approach allows for genuine, beneficial changes.

That sounds incredibly empowering for your team. Allowing them that level of trust to take initiative, create, and then bring something valuable back is a fantastic experience, both at the individual contributor level and for the team as a whole.

Asheeka Hyde: I've always believed that it's fundamentally unfair to hold someone responsible without granting them the authority to influence the outcomes of their work. Many people find themselves in situations where they are expected to deliver but lack the power to shape the process. That's not a position I want to put my team in. If I'm asking them to stretch their capabilities, it's only right that they also have the freedom to approach their tasks in a way that feels right to them.

Given the surge in AI, I'm curious about your perspective on the evolution of data science teams. Everyone's talking about AI these days, from GenAI to its various applications, and there's a lot of pressure on leaders like you. Organizations are eager to implement AI solutions quickly. How do you see data science teams evolving over the next three to five years? What might your unicorn team look like in the future?

Asheeka Hyde: Absolutely, GenAI and advanced analytics are constantly evolving. Interestingly, GenAI excels in language processing but not so much in mathematics, where traditional analytics and basic algorithms still play a crucial role. I believe that about 90% of business problems still require these fundamental analytical approaches. However, GenAI is transformative, particularly in making data and analytics more accessible. It can simplify interactions, internally and with customers, by translating complex data into natural language, which enhances understanding and engagement.

And GenAI can automate mundane tasks, potentially reducing human errors that occur with repetitive work, thereby freeing up time for more meaningful and creative tasks. This automation could be a game-changer, especially in routine-heavy sectors like legal, where standard contracts share many similarities.

Yet, as promising as GenAI is, it's not without challenges. For instance, copyright issues could slow progress, especially as companies try to train GenAI models on their proprietary data, which often isn't sufficient, leading to inaccuracies or hallucinations.

This brings us to the role of data governance and data quality. To leverage AI effectively, the underlying data must be accurate and well-governed. This is an ongoing challenge, but it's also an opportunity to emphasize the importance of solid data foundations.

Looking forward, the skill sets required in data science will likely become more diverse. We'll need not just technical experts but also translators who can bridge the gap between complex data insights and business applications. I envision future data teams as interdisciplinary, with professionals transitioning from business roles to data roles, enriching the team with their domain expertise. I could even imagine disciplines like psychology playing a factor in desired skill sets.

But zooming out, fostering a culture where data-driven decision-making is the norm requires a significant shift in how decisions are made. It's about empowering those closest to the data to make informed decisions, which is a profound change for many organizations. This transition to more decentralized decision-making can be challenging but is essential for agility and responsiveness in today's fast-paced business environment.

Once, I had an executive say a brilliant thing to me. Basically, he said: You keep talking about data-driven decision-making, but what that really means to me is that I have to delegate my decision-making to people in the org who have the data. And I have never delegated a decision in my life. I got to where I am because I make good decisions and now you expect me to hand over that capability to others on my team.

I will never forget that conversation—it hits the change management challenges on the nose.

Yeah, it's almost like data can be viewed as an adversary instead of an ally.

Asheeka Hyde: The mindset shift may partly resolve itself as younger generations, who've grown up in a digital environment, join the workforce. They are inherently comfortable with technology and will naturally drive change. However, a significant mindset shift is needed at all levels of the business to truly embrace data-driven decision-making. This shift will allow faster, less risky decision-making, unlocking greater value than if it's confined to certain levels of the hierarchy. It’s also crucial that executives and senior leaders undergo this journey. Data literacy must be championed organization-wide. That's a role in its own right, not just an add-on.

Right, instead of data literacy being an afterthought, it needs to be a dedicated role.

Asheeka Hyde: It’s clear that driving this change requires skilled influencers and educators within the executive team, capable of compelling storytelling to lead people on this data literacy journey. You can't simply mandate such a transition. It must be enticing and well-guided. This role needs to be senior because convincing others of its necessity is challenging. Whenever I suggest that we might need a senior manager or head for this role, I often get surprised reactions, but it underscores the level of commitment needed.