Inconsistent supplier and internal feeds aren’t just “bad data”. Usually the structure is unclear or unusable. This article explains the patterns—non-conforming fields, missing attributes, unstable hierarchies—and how a structure audit gives you a model that data can actually land in.
Perfect category trees don’t survive growth. This article explains how to design product structure that evolves: clear principles, governance, schema alignment, and controlled updates that support PIM, marketplaces, and automation.
PIM and AI don’t fix product data—they amplify it. If your taxonomy, attributes, variants, and governance aren’t coherent, implementations slow down and AI output becomes unreliable. Here’s what product structure must look like to support PIM operations and AI consumption.
Bad product data structure doesn’t fail loudly. It quietly breaks PIM, automation, and marketplace performance. This article explains how weak taxonomy, attributes, and variant models create manual work, delistings, and lost revenue—and what coherent structure looks like instead
Tools and hardware merchants and distributors manage thousands of SKUs with complex specifications, variants, and compliance requirements. This article explains why PIM is essential for organising large catalogues, improving accuracy, and scaling across B2B, D2C, and marketplace channels.
Discover how traditional distributors can use PIM as the bridge from static print catalogues to dynamic eCommerce. Learn how centralising product data, enriching it for search, and automating syndication unlock faster time-to-market, fewer errors, and a better customer experience.
In regulated sectors, product data is effectively a legal document. Discover how Product Information Management (PIM) provides a single source of truth, enforces governance, and delivers the traceability, localisation, and audit trails you need to stay compliant while accelerating time-to-market.
Data cleaning and data enrichment get lumped together, but they do different jobs. Cleaning makes your product records accurate, consistent, and unique. Enrichment adds the detail, content and context that improve search, conversion, and customer confidence. Here’s how to sequence both for measurable results.
Manufacturer data starts in engineering and ends in the catalogue—but the middle is often spreadsheet chaos. A PIM bridges ERP and PLM with marketing, compliance, and distributors. Discover how strong governance, automated syndication, and AI tools like SKULaunch and Descriptionwise cut manual effort and speed time-to-market.
Furniture and home goods teams juggle huge catalogues, complex variants, and mountains of imagery. A PIM provides a single source of truth, controls colour and material matrices, links the right assets to the right SKUs, and speeds multichannel launches. Here’s what that looks like in practice
Electrical and HVAC suppliers manage high-stakes technical data, certifications, and CAD/BIM assets. A PIM creates a trusted source of truth, standardises attributes, and automates supplier onboarding. Learn how PIM reduces errors, improves trade search and filtering, and speeds time-to-market for complex catalogues.
In PPE, inaccurate product data is more than a nuisance — it’s a safety and liability risk. A PIM system centralises standards, certifications, sizing and documentation, supports traceability for high-risk categories, and delivers consistent information across portals, tenders, and e-commerce. Here’s how it keeps your catalogue compliant and sellable
Legacy product data often lives in messy spreadsheets and ageing ERPs, slowing launches, and weakening search. This article outlines a pragmatic, phased approach to modernising your catalogue: define the future model, audit, and prioritise, cleanse and de-duplicate, enrich for modern channels, then lock in governance so the problem doesn’t creep back.
AI is now a practical must-have in PIM. This guide shows the features that genuinely save time and lift product quality: intelligent supplier data cleansing, GenAI content with brand controls, proactive validation, asset intelligence, semantic search, and personalisation supported by digital shelf insights. Use our checklist to avoid AI-washing.
If key specs live only in paragraphs, customers can’t find them – and neither can search, filters, feeds, or AI shopping tools. This article shows how to move facts into structured attributes, use PIM to govern them, and keep copy for persuasion. Expect better discovery, stronger SEO, fewer returns, and easier syndication
A PIM project fails or succeeds on people, not software. Learn the five core roles you need on your PIM implementation team, what each one actually does, and how they work together to deliver cleaner data, smoother integrations and real business value
If you mix up product attributes and categories, your navigation breaks and filters return “0 results”. This article shows how to design a clean taxonomy in PIM, avoid common modelling traps, and make it easier for customers to find what they want across all channels.
Supplier data onboarding is often the slowest step in getting products live. AI changes that. This article explains how modern PIM features and Start with Data’s SKU Launch reduce manual mapping, standardise messy inputs, flag errors early, and speed up approvals. Expect quicker launches, better consistency, and fewer late-night spreadsheet battles.
Complex products quickly overwhelm spreadsheets. Learn how PIM data models, inheritance and rules-based configuration tame variants, cut manual work and give customers clearer choices across every channel.
Moving product data from spreadsheets, ERP and homemade tools into a new PIM can feel like open-heart surgery. This article sets out clear, non-technical steps to audit, cleanse and migrate your product information so your PIM goes live clean, trusted, and ready for growth