To support marketers and a highly personalized customer experience, data specialists create paths between data silos (collections of information isolated within a department or system).
Sometimes data is siloed for a purpose, for example to protect personally identifiable information or to support the specific needs of a specialized business team. But often it’s the result of other circumstances, such as legacy technology, separate data systems, or even independently maintained datasets in the same system.
Whatever the cause, data silos can get in the way of sharing useful data enterprise-wide, hindering efficiency and limiting potential data-driven insights. Plus, the exponential growth of artificial intelligence (AI) tools is rapidly expanding the pace and complexity of data use. Customers have come to expect the highly tailored messaging made practical by AI tools. The bottom line is that effective, efficient, holistic data use enterprise-wide is more crucial than ever before—and so is becoming adept at navigating data silos to make that possible.
Our decades of experience here at The Lacek Group have taught us that while data may be stored as digital code, it reflects our in-real-life humanity. To succeed in today’s marketplace, data-focused marketers need to lean into their empathy as much as their technical expertise.
In many enterprise environments, decentralized datasets have often developed organically by different teams, each with unique structures, use cases, and specialized context. Silos aren’t inherently bad. In fact, they’re often a byproduct of innovation, autonomy, and specialization. The challenge is turning a fragmented data landscape into a harmonized source of insight.
As part of the Data Intelligence team here at The Lacek Group, we’re often called upon to help our clients bridge data silos to achieve a holistic, system-wide view of organizational data that will support accurate analysis and insights, strong business decisions, and a streamlined customer experience.
These observations assist our world-class clients across sectors as they navigate their data and find ways to best leverage it to achieve, or surpass, their business goals.
A foundational step in any data collaboration is understanding what’s in the table—not just structurally, but contextually. Take the time to learn what data points represent, what assumptions are baked into them, and how they’ve been used historically. This discovery phase is as human as it is technical.
To illustrate: AI can help you categorize captured data, but it won’t illustrate the mindset of the person entering a particular data field. For example, perhaps someone focused on completing a sale is responsible for entering a piece of information. They may not be concerned about entering the right transaction code—or any at all—from a drop-down menu as they try to please a customer eager to be done. Knowing that gives data analysts vital context for how much weight to give that metric.
A thorough understanding of the available datasets will help you avoid shortcuts and MacGyver fixes to achieve a minimally viable product (MVP). Over the long term, MVPs don’t usually save time; instead, they tend to logjam into larger problems. Sometimes MVPs are a necessary step toward project completion. But it’s important to continue strategizing enhancements from a big-picture perspective. Creating a road map for how you’ll proceed helps you be more efficient—and often reveals potential obstacles and unexpected opportunities.
Work together with your enterprise data teams—and potentially brand partners providing additional data—to standardize the data’s documentation and organization as much as possible. That will help you create a strong framework to build on. That may require an incremental approach, but working toward consistency will pay off.
Teams that own datasets may feel protective or even defensive. We advocate for approaching data partnerships with empathy. Find ways to build genuine rapport and to clearly signal:
1. We're not here to displace you.
2. We trust that you're the experts of your domain.
3. We want to help you succeed.
That mindset helps you find the right allies and supports more open and effective collaboration. At Lacek we embrace this approach not just with data teams, but across disciplines. Staying communicative and forging meaningful relationships can smooth the day-to-day tasks of a collaboration—and forge a strong foundation for solving future challenges together.
Remember, your people skills are as important as your data chops. The way you engage your collaborating team can determine whether your work accelerates … or stalls. Entering the conversation with humility and genuine curiosity will help you gain access, context, and even champions who will vouch for you as you work to bridge silos. Treating people as collaborators, not gatekeepers, changes everything.
The client team will get you started on the journey of building a bridge between data silos—but be sure to invest in really getting to know the data yourself. A deep grasp of the available data, whatever its source, will help you leverage it to execute campaign goals and fuel success.
To illustrate: Lacek data specialists served as the connective tissue between a client’s marketing and data science teams, helping launch an always-on product replenishment campaign. The effort demanded a deep understanding of both the marketing team’s vision and complex modeling data. The result was a timely and tailored customer experience based on actual purchase behaviors.
Many data owners are overloaded, and often analysis isn’t their full-time role. You’ll probably get more traction and appreciation when you position yourself as a gap filler instead of someone there to duplicate efforts.
For example, at Lacek we often help our partners by taking on the analytic heavy lifting, especially when internal teams don’t have the bandwidth or when restructuring shifts their responsibilities. Providing analytics support helps our partners accelerate decision-making while reducing their frustration and respecting everyone's capacity.
We’ve also noticed that with some of our long-tenured accounts, our team members may have the most experience with—and thus the deepest understanding of—a dataset. In that case, our institutional knowledge saves our clients from needing to hunt down the right people in their own organizations and then wait for a response.
Great data analysts are detectives and diplomats, not just miners of information. And remember to stay a step ahead. If you circle back with questions that show you’ve done your homework, you earn credibility. When you ask lazy or uninformed questions, you risk losing trust.
Finding ways to flex your team’s collaborative skills alongside their technical capacities will help you build lasting relationships—and set the stage for long-term data strategies that contribute to future growth.
Sean Geist and Gene Sulewski are part of The Lacek Group’s Data Intelligence team. For more than 30 years, The Lacek Group has been perfecting the art and algorithms of brand devotion. We help world-class brands identify their highest-potential customers, engage them across channels throughout their lifecycles, personalize each relationship for optimal long-term results, and measure the true effectiveness of those efforts. The Lacek Group is an Ogilvy One company.