Breaking Down Product Data Silos
Why Your Team is Doing Low-Value Work (and How to Fix It)
Ecommerce teams should be the powerhouses of digital retail—driving discovery, optimising product visibility, and fine-tuning the user journey to turn browsers into buyers. But instead, too many teams find themselves buried under a mountain of low-value tasks: endlessly reformatting product data, manually fixing mismatches between systems, and performing repetitive data entry. Why? Because somewhere along the way, data silos—isolated, rigid hierarchies created for operational ease—have crept into the structure, dictating how information (or in most cases how it doesn’t) flows from one part of the business to another.
When product data is held hostage by silos, everyone loses: retail teams waste precious hours doing “busywork,” while potential growth opportunities slip through the cracks. And yet, in many brands, these silos persist because they’re simply seen as the way things have always been done.
In this article, we’ll explore why traditional product hierarchies can hold your team back and how to break down these silos by rethinking the role of attributes, cross-functional collaboration, and smarter automation. The goal? To help brand side teams reclaim their time and refocus on high value work, rather than being stuck doing low value data rework.
The Hidden Cost of Data Silos in Ecommerce
Data silos aren’t just a minor inconvenience—they’re a bottleneck that can undermine your team’s productivity and potential. When product data is fragmented across systems or isolated within rigid hierarchies, teams end up spending more time on administrative tasks than on strategic work that drives growth. This hidden cost of siloed data structures becomes clear when we look at where the teams’ time actually goes.
Consider a typical scenario: the ecommerce team receives product data from the product development system, but because each team uses different structures and naming conventions, that data doesn’t fit cleanly into the ecommerce platform. Instead of focusing on merchandising or optimising the on-site experience, ecom teams waste hours reformatting and cleaning up product data. It’s repetitive, low-value work—essentially acting as an intermediary between incompatible systems.
The result? Teams are stuck doing “busywork” that adds little to no value. Every minute spent aligning, re-tagging, or double-checking data is a minute taken away from what really matters—driving engagement, boosting conversion rates, and delivering a seamless shopping experience. And the problem doesn’t stop there. Data silos often lead to duplicated work, where various teams re-enter or adjust data separately in each system. This repetition increases the risk of errors, further impacting data quality and leading to inconsistencies across customer touchpoints.
And when teams are forced into repeated manual data entry, mistakes are inevitable. A mistyped category, an overlooked product attribute, or a missed update can skew critical reports and budgets. For instance, if data isn’t correctly aligned between departments, merchandise and inventory forecasts might be based on incomplete information, resulting in stock imbalances or misallocated budgets. At best, these errors waste time. At worst, they lead to misguided decisions that impact profitability and inventory levels.
For retail brands, the stakes are high. While teams are bogged down with manual data tasks, opportunities to enhance product discovery, streamline the user experience, and respond to real-time trends go untapped. The silos aren’t just slowing you down; they’re holding you back.
Breaking down these data silos is the first step toward freeing teams to focus on impactful work. But it’s not as simple as lifting restrictions. Brands need to rethink their approach to data, creating structures that support seamless data flow across departments without the need for constant manual intervention.
The Role of Cross-Functional Collaboration
Breaking down data silos requires more than just tweaking hierarchies and attributes—it demands cross-functional collaboration. When different teams operate in isolation, each with its own data structure and priorities, the results can be frustratingly fragmented. Ecommerce, merchandising, retail, product development, and marketing all have unique ways of categorising and managing product data. But without alignment, their efforts lead to duplicate work, data inconsistencies, and ultimately, a disjointed customer experience.
For a brand to overcome this, cross-functional collaboration isn’t just helpful; it’s essential. By bringing together representatives from each team to form a “product data squad,” brands can create a unified approach to data that respects each team’s needs while promoting a more seamless data flow. This squad doesn’t just establish initial standards; it plays a continual role in managing and updating data structures, making it easier to keep hierarchy lean and attribute management flexible across departments.
Why Cross-Functional Teams Are Key to Efficiency
Each team interacts with product data from a unique perspective. The ecommerce team needs attributes that optimise product discovery and search, merchandising cares about how products are categorised for inventory and budgeting, and product development might require technical specifications that support manufacturing and quality control. Without regular cross-departmental communication, these needs clash rather than complement one another, often leading to duplicative data entry and a lack of clarity on who “owns” the data.
By establishing a collaborative approach to data structuring, brands can bridge these gaps. A cross-functional team can agree on a consistent hierarchy, determine essential attributes, and prevent unnecessary layers of data complexity. This reduces the back-and-forth and guesswork that arise when teams interpret data requirements differently.
Practical Tips for Building Cross-Functional Data Teams
Define Clear Roles and Responsibilities: Each member of the data squad should understand their role and how their insights contribute to the bigger picture. Ecommerce, merchandising, product development, and marketing representatives should collaborate on data decisions rather than work independently.
Regular Alignment Meetings: Establish regular check-ins, especially around product launch or seasonal cycles, to align on any changes in data needs. Consistency in these meetings helps ensure that updates are communicated across all teams, keeping data accurate and useful across the board.
Shared Data Playbook: Create a shared “data playbook” that outlines the hierarchy, defines attribute standards, and includes guidance on data entry processes. This playbook becomes a reference point that anyone on the team can consult, ensuring fewer discrepancies and reducing the likelihood of data mishaps.
Empower Team Members to Raise Issues: Make it easy for any team member to flag potential data issues or propose adjustments to the hierarchy or attribute structure. By fostering open lines of communication, brands can address issues proactively, preventing them from cascading into larger, more complex problems.
Cross-functional collaboration transforms product data from a source of friction into a competitive advantage. With aligned teams and shared standards, brands can create a foundation where data flows seamlessly from one department to the next, reducing redundant work and unlocking potential for higher-value activities. This collective approach not only streamlines processes but also ensures that the entire brand benefits from the insights and priorities of each team.
Actionable Steps for Streamlining Product Data and Reducing Low-Value Work
Breaking down silos and freeing up your team’s time requires more than just collaboration. It’s about setting up a product data structure that prioritises flexibility, efficiency, and scalability. Here are four actionable steps to simplify your product data management and empower your teams to focus on high-impact activities.
1. Adopt a Lean Hierarchy Model
The first step is to rethink the hierarchy itself. Rather than overloading product categories with endless sub-levels, focus on a lean hierarchy with only the essential top-level categories. Aim to keep the hierarchy “skinny” by limiting levels to broad groupings that can be applied across all departments—think “product type,” “category,” and, if necessary, “gender.”
For example, instead of drilling down into specifics like “summer shorts” or “winter jackets,” create a high-level “outerwear” category that can be used consistently across teams. This approach simplifies data management, reduces manual adjustments, and ensures that product classifications are easy to understand and apply consistently across the business.
2. Expand and Standardise Attributes
Attributes provide the flexibility that rigid hierarchies lack, allowing brands to add relevant details to products without permanently altering the structure. Once you’ve streamlined your hierarchy, focus on expanding and standardising attributes that support each team’s goals. For instance, ecommerce teams may benefit from attributes like “season,” “occasion,” or “material,” while merchandising teams may need attributes like “cost price” and “margin.”
Standardising attributes with a shared “data playbook” (as discussed in above) is crucial. This ensures everyone uses the same terminology and follows consistent rules when tagging products, reducing ambiguity and making it easier to pull accurate, meaningful reports across departments.
3. Automate Data Flow Wherever Possible
The more data entry processes you can automate, the less time your teams spend on low-value tasks. Automation reduces the risk of human error and allows data to move seamlessly from one system to another without manual re-entry. Integrate tools that sync your product data across key systems like PLMs, ERPs, and ecommerce platforms, ensuring updates made in one system automatically reflect in the others.
For example, set up integrations to sync product data directly from your PLM to your ecommerce platform, so teams aren’t stuck copying and pasting information or reformatting fields. Automation can also be used to trigger data checks at key stages, flagging any inconsistencies before they become issues further down the line. By reducing the time spent on data “busywork,” teams can focus on adding value through strategic decisions and customer-facing improvements.
4. Create Data Checkpoints to Ensure Accuracy
Even with automation in place, occasional data checks help catch any lingering discrepancies that could affect reporting, inventory management, or budgeting. Implement data checkpoints at key stages, like before product launches or seasonal campaigns, to verify that all information is accurate and aligned across systems.
Data checkpoints can be as simple as spot-checking a sample of records or as thorough as running automated validations across the database. By establishing these checkpoints, teams can catch errors early and prevent them from cascading into larger issues that affect budgeting, forecasting, and customer experience.
5. Foster a Culture of Continuous Improvement
Streamlining product data management isn’t a “set it and forget it” process. Encourage teams to regularly evaluate and refine data processes, and make it easy for them to suggest improvements to hierarchy, attributes, or automation tools. Over time, this proactive approach to data management builds resilience into your data structures and reduces the risk of low-value work creeping back in.
The Payoff: Teams Focused on High-Impact Work
By implementing these steps, brands can eliminate data silos and reduce low-value work across ecommerce and other teams. A streamlined data structure, powered by lean hierarchies, flexible attributes, and automation, enables teams to focus on the initiatives that genuinely impact growth—like enhancing product discovery, personalising the user journey, and optimising conversion rates. In a competitive market, freeing up time for high-impact work isn’t just a nice-to-have; it’s essential.
Data silos don’t just slow down your team—they limit the entire brand’s potential. By holding onto rigid hierarchies and siloed data, companies risk losing out on the efficiency and insights that drive modern retail success. Fortunately, the solution lies in rethinking how product data flows across the organisation, aligning teams around a shared, streamlined structure that allows everyone to focus on the work that truly matters.
Breaking down silos starts with a lean, cross-functional approach: adopt a minimal hierarchy, invest in flexible attributes, and use automation to keep data flowing smoothly between systems. With these elements in place, ecommerce teams can leave behind the low-value “busywork” and turn their attention to impactful strategies that enhance the customer experience, boost engagement, and ultimately drive revenue.
Transforming product data management may take some upfront effort, but the payoff is a streamlined operation and a team that’s free to focus on growth—not grunt work. So, is it time to break down those silos? The answer might just transform your brand’s trajectory.