I want to tackle a hot topic in the realm of data science – the rise of connector roles. These roles, designed as bridges between leadership and analytics, translate business problems into analytic requirements and vice versa. But here’s the million-dollar question: Are these connector roles a savvy investment or an expensive gamble?
Connector Roles: A Quick Overview
First, let’s understand what connector roles entail. These individuals sit between your business leaders and your data whizzes. Their job? To ensure that what the business needs is what the data team works on. Similarly, connectors ensure the analytic insights generated are understandable and actionable by the business side. Sounds great on paper. This is especially true if you’re not up to speed on your analytics. But let’s dig a little deeper.
High Cost of Adding Layers
Introducing connector roles means adding another layer to your organization. That layer includes salary, benefits, and overhead for employees whose costs are comparable to those of mid-level or senior managers.
And as any seasoned exec knows, more layers often mean more bureaucracy, slower decision-making, and, importantly, higher costs. You’re essentially paying a premium for a translation service – one that, with some training, your existing teams could handle themselves.
Potential for Miscommunication
While the intent of connector roles is to reduce miscommunication, ironically, they can sometimes add to it. These staff are, after all, still human.
Information, like in the game of telephone, can get distorted as it passes through more hands. The risk here is that critical nuances of complex data insights or specific business needs might be oversimplified in translation. Worse still, critical information could still be lost altogether.
Dependency and Bottlenecks
Relying on connector roles can create dependencies, where analysts and business leaders stop interacting directly. This can lead to bottlenecks, especially if your connector becomes a gatekeeper rather than a facilitator. And if your connector hire lets a little power go to their head, they could create silos where none existed. It’s the old saying: too many cooks in the kitchen can spoil the broth.
Barriers to Building a Data-Driven Culture
One critical goal for modern organizations is fostering a data-driven culture. Connector roles, however well-intentioned, might signal that understanding data is ‘someone else’s job’.
This can hamper efforts to encourage all team members, especially leaders, to develop basic fluency in data analytics. Furthermore, it can hamper efforts to develop knowledge among analysts about your business problems, client needs, and strategies.
Opportunity Cost
Investing in connector roles means resources cannot be spent elsewhere. And the return is likely to be lower than training existing staff or finding analysts or leaders with dual competencies. The opportunity cost here is significant – could these funds be better used in upskilling your team or bringing in multifaceted talent?
Innovation at Risk
Innovation often happens at the intersection of different fields and perspectives. Direct interaction between data scientists and business leaders can spark ideas that a third party might not envision. By their very nature, connector roles could limit this organic interplay of insights and ideas.
The False Security of a Quick Fix
Connector roles might seem like a quick fix to a deeper challenge – the need for better communication and understanding between two worlds. However, quick fixes can give a false sense of security, masking the underlying issues that need addressing.
Connectors might remedy the problem in the short term. However, you’ll be back at square one if they leave the organization.
The Alternative: Investing in Training and Hiring
So, what’s the alternative? Invest in training your leaders and analysts to speak each other’s language. Encourage a culture where continuous learning is valued. When hiring, look for candidates who bring a blend of business acumen and analytic prowess.
Sure, hiring will take longer. But you’ll develop a stronger team of valuable and innovative players instead of playing the misguided game of plug-and-play staffing.
Even better than hiring, upskilling leaders and analysts strengthens both groups at a fraction of the cost. The investment in training staff on business problems and strategies demonstrates that leadership values their skills and contributions.
More importantly, leaders who take on training in analytics signal that they are active participants in the organization. Staff will see leadership setting an example rather than continually displaying a lack of understanding or appreciation.
Embracing Technology: Tools Over Roles
In an age where technology is advancing at a breakneck pace, numerous tools can help bridge the gap between business and data science. For example, investing in user-friendly data visualization tools can make insights more accessible to non-technical team members.
Although to be clear, an even more powerful combination is investing in both training and technology. After all, even the best tools will underperform in untrained hands.
The Bottom Line
Connector roles are not inherently flawed, but they are a solution that comes with significant drawbacks and costs. In a dynamic business environment, the emphasis should be on building versatile teams that can easily navigate both the business and data realms.
Conclusion: A Call to Action
The head of my mother’s team used to say, “If you can throw money at it and it goes away, then it’s not really a problem.” Well, that’s partially true. The expensive gamble on connector roles is that the problem may not disappear entirely.
Staff eventually turnover. The analytic landscape continues to change rapidly (although the fundamentals we all rely on remain consistent). Spending your hard-earned revenue on connectors may simply be trading an obvious and immediate problem for hidden long-term problems.
As leaders in a data-driven world, our goal should be cultivating environments where business acumen and data literacy coexist seamlessly within teams. Let’s rethink our approach – instead of adding layers, let’s build bridges through education, culture, and technology.
Until next time, here’s to innovating, integrating, and inspiring in the world of data!
