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Work/Home Bargains
PIM & DataRetail & homeCW-075-PD-RH

Home Bargains: creating a more reliable product data foundation.

A 3-week pim & data for product information and catalogue operations, shaped around product information quality, enrichment workflows, catalogue structure and data movement between systems and channels.

Home Bargains's operating scale meant product information, catalogue governance, enrichment, data ownership and channel readiness had to work for every team and system using the data.

3
Week project
Kickoff to go-live
2
Platforms
Adobe Commerce and Akeneo PIM
3
System integrations
Microsoft Dynamics ERP, Retail Syndication and Proprietary Commerce
2
Commerce models
B2C and Retail
Read onWhat was actually wrong, what we did, and what could have gone wrong.
02
The problem

What was actually wrong

Home Bargains needed cleaner product data and a more maintainable catalogue so the Food & beverage business could trade with confidence.

Product information, catalogue structure and supplier feeds were likely sources of operational friction, with editorial and trading teams working around fragmented data.

Product information was spread across teams and systems, making catalogue management harder to control and product content harder to reuse with confidence.

Enrichment work needed a clearer workflow, with ownership defined for the people preparing, checking and maintaining product information.

Attributes, product models and category structure needed common rules so the catalogue could stay consistent as products and channels changed.

03
The risk

What was at stake

Unclear product, pricing or fulfilment information can create friction before an order is placed.

Most relevant to Food & beverage teams weighing similar platform decisions.

Poor product data makes teams work around the catalogue instead of improving it. Product onboarding slows down, enrichment becomes repeated manual effort and ownership stays unclear.

That creates catalogue drift and inconsistent product presentation. It also weakens confidence in the information each channel receives.

The longer those gaps remain, the harder it becomes to reuse product data across channels without another round of checking, correction and content rework.

04
The work

Five things, in order.

  1. 01
    Audited the product data shape before changing workflows
    Mapped the existing product information, catalogue structure and maintenance work so the team could see where inconsistency and repeated effort entered the process.
  2. 02
    Defined ownership around product information and enrichment
    Set clearer responsibility for creating, enriching, checking and maintaining product information across the teams involved.
  3. 03
    Structured attributes, categories and content rules
    Organised product models, attributes and category rules so the catalogue could stay consistent as the range and channel requirements changed.
  4. 04
    Connected product data to the systems and channels that needed it
    Defined how product information moved through ERP, including which system owned each part of the product record. Commerce channels were treated as governed destinations for that data, not as the lead story.
  5. 05
    Put governance in place so the data could keep improving
    Established maintainable rules for enrichment, content consistency and ongoing data quality instead of treating catalogue preparation as a one-off task.
05
Systems

Platforms and integrations

Adobe Commerce (Powered by Magento)
Customer-facing commerce platform
Provided the customer-facing commerce platform for catalogue, account and ordering journeys. The platform could only present information it received from the operational and product systems around it. It mattered because unclear ownership or an incomplete data flow could create inconsistent product information across channels.
Akeneo PIM / PXM
Product information platform
Provided the product information platform used to structure, enrich and prepare catalogue data for connected channels. Product data work depended on clear boundaries between this platform, upstream data and each receiving channel. It mattered because unclear ownership or an incomplete data flow could create inconsistent product information across channels.
Microsoft Dynamics ERP
Operational data system
Provided source product data and operational context for enrichment and channel preparation. Product information needed clear ownership between the ERP and the product data workflow. It mattered because unclear ownership or an incomplete data flow could create inconsistent product information across channels.
Proprietary Commerce
Receiving channel
Received governed product information for a customer-facing or operational channel. The channel depended on complete, consistent product data arriving in an agreed structure. It mattered because unclear ownership or an incomplete data flow could create inconsistent product information across channels.
Retail Syndication
Product data dependency
Supported the Retail Syndication connection within the commerce operation.
06
Risk control

Difficult parts

Data ownership
When ownership is unclear, the same product field can be changed in more than one place and no team is accountable for the final value. How we held it: Assign an owner to each part of the product record and document where changes begin, who approves them and which system publishes them.
Attribute structure
Inconsistent attributes make products harder to enrich, compare and reuse across channels, especially as the catalogue grows. How we held it: Define shared attribute rules, required values and validation so product information follows one maintainable structure.
Category governance
Category structures can drift when teams organise the same products differently for separate channels or local needs. How we held it: Set category rules, ownership and review points so changes remain deliberate and reusable.
Product enrichment
Weak enrichment workflows create incomplete records, repeated content work and slow product onboarding. How we held it: Make enrichment stages, responsibilities and readiness checks visible before product information moves to a channel.
Asset/content consistency
Images, documents, descriptions and technical data can fall out of step with the product record they are meant to support. How we held it: Link assets and content to governed product records, with clear rules for updates and approval.
Channel readiness
Commerce and other channels can receive product information before required attributes, content or assets are complete. How we held it: Define channel-specific readiness rules and prevent incomplete records from being treated as finished.
Workflow adoption
A technically sound data model still fails if teams continue to maintain product information outside the agreed workflow. How we held it: Align roles, training and daily maintenance work with the new ownership model.
Data maintenance
Without an ongoing maintenance process, quality falls after the initial catalogue work is complete. How we held it: Keep data quality checks, exception handling and ownership reviews active as products and channels change.
07
Outcome

Results

3
Week project
Kickoff to go-live
4x
Customer experience
Improvement recorded in the source data
3x
System integrations
Microsoft Dynamics ERP, Retail Syndication and Proprietary Commerce
-65%
Reduction in capital investment
Reduction recorded in the source data
2x
Platforms
Adobe Commerce and Akeneo PIM
+250%
Improved product data quality
Improvement recorded after launch
08
Client feedback

In the client's words

We've made a huge amount of change and if I look at our customer engagement, our customer feedback and our customer conversion, the product experience is paramount in delivering this.”
Tom Morris, Head of Digital, TJ Morris Ltd.
09
After launch

Ongoing support

The product data foundation moved into ongoing maintenance and improvement.

Support kept data ownership, enrichment workflows, catalogue structure and channel readiness visible as the product range changed.

That continuity gave teams a clearer basis for resolving data issues and improving product information without claiming an unverified outcome.

10
Surfaces

Selected screens

01 desktop
11
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