The Product Data Challenge

The Product Data Challenge

Every brand's journey begins with getting product data from the design team to the wider business efficiently. The success of everything that follows hinges on this data moving smoothly and accurately.

Key to this process is visibility. It helps spot delays or challenges early, ensures everyone is updated about the product lineup for planning, and fosters effective collaboration across teams without endless meetings for alignment.

Yet, decision-making often occurs in isolation across different departments:

• Designers might scrap a product they dislike.
• Merchandising could cut it due to budget constraints.
• Sizing or costings might change last minute.

Such critical decisions are typically trapped within separate, siloed Google Sheets, making coordinated action challenging.

Alignment and access to the latest information are crucial. But what happens when product codes or SKUs change? Simple errors, like mislabeling with the final color name, can throw off product information, creating chaos up to launch.

The complication deepens if the product isn't logged in a central system until the purchase order is raised. By then, it's often too late to prevent unapproved products from syncing with platforms like Shopify, highlighting the need for a system that can manage product evolution carefully and keep ecommerce syncs in check.

The Dark Stack

Imagine a sprawling empire of Google Sheets and Excel spreadsheets, each one an isolated island of data. This realm, characterised by endless copy-pasting and disconnection, is what I call the "Dark Stack" - the current, chaotic standard for managing ecommerce product data.

In this landscape, the air is thick with the demand for constant alignment. Teams find themselves trapped in an endless loop of meetings, instant messages, exhaustive reports, and impromptu calls, all in a bid to stay on the same page. For newcomers, the frustration is palpable, encapsulated in the refrain, "I can’t work like this - everything needs a meeting."

Meetings, rather than being productive decision-making arenas, become marathons. Their sole focus? To corral scattered information into one place. The irony? The more people you add to the mix across merchandising, buying, and production, the greater the pressure on the product development team to communicate effectively. Yet, as the team sizes remain static amidst expanding responsibilities and new product categories, the system strains at the seams.

To gauge the extent of entanglement in the "Dark Stack," one only needs to request every document used from conception to purchase order. The revelation often shocks those ensnared in its web, unaware of the sheer volume of documents they're juggling.

This is the reality for many in ecommerce today: a mire of inefficiency and confusion, where the introduction of new team members or strategies only serves to complicate an already convoluted process. The "Dark Stack" stands as a testament to the urgent need for a new, cohesive approach to product data management.

The Enterprise PLM Pitfall

Enterprise companies have traditionally tackled product data management by adopting a full Product Lifecycle Management (PLM) solution. However, urgency and PLM systems rarely align; the average PLM project spans six months, reflecting its slow deployment nature.

PLM software aims to provide end-to-end management of a product's lifecycle, ensuring control over development data and facilitating handoffs and approvals. This process necessitates migrating all product and materials information into the PLM, often requiring an overhaul of existing workflows, including tech packs, fittings, measurements, and costing processes.

The transition to PLM can be complex. For instance, tech packs, which start as PDFs and evolve through the sampling process into detailed spreadsheets, require significant adjustment in workflow, increasing both risk and cost. Moreover, the data necessary to populate a PLM is frequently scattered across various documents and spreadsheets, lacking a centralized database. Compiling, cleaning, and organising this data for PLM integration is a daunting task, often beyond the capacity of design teams.

This complexity places a heavy burden on product teams, adding a layer of challenge to their roles. PLM projects carry high risks due to their complexity, the rigidity of their workflows, and the variance in team processes. Failures are not uncommon, attributed to a lack of resources, poor project management, inadequate data quality, and inflexible services from providers.

Should a company successfully launch a PLM, users often encounter a difficult interface and a steep learning curve, complicating the transition. Decision-makers relying on past experiences with similar systems may find the PLM doesn't suit their company's unique workflow and strategy. As a result, PLM initiatives can stall, leading to continued reliance on outdated and inefficient processes.

ERP: A Misaligned Solution

Relying on your ERP to manage your bill of materials without it being designed for that purpose spells trouble.

Older ERPs aren't built to handle the nuanced product data sales channels need. These systems were made for tracking purchase orders, sales, and finances, not the detailed product information required by platforms like Shopify.

Consider Shopify merchants: they deal with numerous Metafields and Metaobjects for product listings. Manually entering data on each product page is not only slow but prone to errors that can cost sales and damage credibility.

Merchants typically resort to one of two approaches:

1. Custom Fields in ERP: Some try to integrate all product details directly into their ERP. This often complicates the ERP's product data import process, potentially delaying the entire system's implementation. Using ERPs as makeshift PIMs (Product Information Management tools) is a misuse of their intended purpose.
2. Bulk Updates: Others use tools like Matrixify to bulk upload product data to their e-commerce platform. While this method is more efficient than manual updates, it still relies heavily on manual checks to prevent errors.

Both strategies highlight the need for a more streamlined, error-proof system for managing product information across sales channels.

PIM: A Step Forward

Shopify stores expanding internationally often turn to Product Information Management (PIM) solutions to manage data across different markets, like moving from the UK to the US or ANZ. Suddenly, the workload and potential for errors multiply.

Global brands use PIMs to streamline their online presence, integrating with platforms like Salesforce, Magento, and BigCommerce. For those platforms lacking extensive app support, having a tech-savvy team to blend various solutions becomes essential. This approach, known as 'composing', helps brands maintain consistent product information across all channels.

PIMs (Product Information Management systems) are designed to centralise and manage product information across multiple channels and platforms. However, they sometimes struggle to keep pace with the dynamic and feature-rich environment of Shopify for several reasons:

1. Integration Challenges: Many PIMs have difficulty integrating smoothly with Shopify, especially when it comes to syncing real-time updates or leveraging Shopify's latest features like Metafields and Metaobjects. This can lead to gaps in information or delays in updates reaching the storefront.
2. Complexity and Cost: Traditional PIMs can be complex to set up and expensive to maintain. Small to medium-sized businesses might find these systems too cumbersome or beyond their budget, limiting their ability to efficiently manage product data.
3. Lack of Flexibility: PIMs are often rigid in their structure, which can clash with Shopify's more flexible and customizable nature. This rigidity can hinder a brand's ability to quickly adapt their product information to meet market demands or to customise their setup for unique business needs.