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Right Thing #6 – Good Product Data, Bad Product Data
Jan 24 2022

Right Thing #6 – Good Product Data, Bad Product Data

Flora, our Co-Founder and Head of Product writes the Right Thing every month, covering everything from systems thinking to innovation and sustainability. Want to be the first to read it when it’s first released? Subscribe here.

This month I dove head-first into product data.

We all know what bad product data looks like because it’s such a massive issue in fashion. If bad product data is killing your ????vibe ????, this is the Right Thing for you.

The more duplicate information, the more places information can live and the more difficult it becomes to manage and update. This is a problem that doesn’t go away as a business grows.

This isn’t just a problem for businesses—the fashion industry as a whole is crying out for better data and greater alignment and standardisation.

Who should care about Product Data? Well, actually everyone.

Product information is constantly changing and touching so many peoples’ hands (design, product development, production, sourcing, merchandise, buying, suppliers, freight handlers, eComm, customer service..the list could go on!!). It needs rules, it needs structure and basically needs to be foolproof. Excel ain’t gonna cut it.

There’s too much unstructured data flying about

Most information within fashion businesses, particularly product and supply chain data, is unstructured.

It typically lives across many disconnected spreadsheets, emails, WhatsApp messages, video calls and generic workflow tools. There’s a lack of consistency and standardisation of fields, values and formats.

Good data = structured data 

Good data needs defined rules, structure and a consistent format.  This makes it easier to find, easier to search, and easier to analyse.  You’ll have columns and rows with pre-defined fields and formats, and entities can be grouped together with relationships to each other (if you want more – check out relational databases)

There’s also semi-structured data too. Take an email, for example. The content is unstructured because you have the freedom to write anything you want in any way. But, it has some classifying characteristics that make it semi-structured such as email, name, date sent etc. It can still be pretty hard to find information or analyse data across several emails.

Read this Forbes article explaining the differences between unstructuredsemi-structured and structured data.

How can good data unlock value for your brand?

Well-structured product data can unlock values in so many areas such as;

Sustainability claims

Substantiating sustainability claims and for circulatory- with legislation changes requiring it). The CMA reports the industry relies on such poor data that 40% of environmental claims could be misleading.

Data-informed decision making

Insights and smart, actionable data that help you work out where you can reduce cost, waste, and improve efficiency

Productivity of your people

Save time, find stuff faster and make sense of the data quicker.

Positive People

Low team morale is one we come across a lot. Teams are demotivated by spending far more time than they’d like navigating and updating messy data and doing repetitive admin tasks when they would actually love to be working on the more exciting stuff.

4 steps to giving good product data a foundation in your business

1. Give someone ownership

Make one person accountable for Product Data in your business. If many people are accountable then no one is accountable.

2. Form your product data team

Form a cross-functional team with the key stakeholders who manage product data through its journey. This may require hiring new people with expertise or just looking within pre-existing teams to understand who has the appetite to lead and be part of a kind of tech squad with representatives from all parts of the business. This will ensure new initiatives and tech decisions are not made in silos.

3. Get everyone on the same page

Map the journey of product data in a visual mapping tool like Miro so everyone understands the steps, the documents and the challenges involved.

4. Now give Product Data a home

If product data is starting life in a Google Sheet that may be O.K for a short while. But Google Sheets can be duplicated and the data can become siloed between teams. I would recommend assigning product data to systems in this order:

  1. PLM / PIM if you have one. (SupplyCompass, whoop!)
  2. ERP if you have one. Think NetSuite, Brightpearl or Zedonk
  3. E-COMMERCE PLATFORM. Think Shopify or BigCommerce.

As the industry digitises and moves away from its heavy reliance on spreadsheets, the smartest brands are assessing their tech stacks, looking to develop better in-house tech capabilities, playing emphasis on data management and are looking to bring better systems and tools into their business.

If you’re enjoying reading the Right Thing and you find yourself recommending SupplyCompass to friends and colleagues sign up for our Co:Laborator Community. Sign up in 2-minutes now and you’ll get paid £500 for anyone you refer who becomes a customer.
Flora Davidson
Co-Founder at SupplyCompass
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As Co-Founder and Head of Product at SupplyCompass, Flora Davidson is responsible for the vision and direction of this platform. With a background in innovation and ethnographic market research, she’s wholly focussed on being customer-driven (brand and factory!) in her approach to what technology they build, how and why. She’s obsessed with good design and excellent, intuitive user experience. Flora is passionate about reimagining how the fashion ecosystem can work more openly and collaboratively together, and sees the shift in emphasis towards stronger partnerships between brand and factory, as foundational to the transformation of the industry.


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