When a chief data officer achieves excellence in master data governance, they’re transformed from someone who spends their time reactively fixing things when data gets messy or breaks a system, to a proactive, all-seeing business enabler. This series explores why master data governance is vital for: data modelling, data quality, rich content, industry standards and data enrichment.  

Series introduction

Tietojen hallinta alan standardien mukaiseksi on vähän kuin kouluttautuisi puhumaan universaalia kieltä. 

No, wait. That wasn’t very helpful, was it? Unless you speak Finnish, of course… 

What we should have said is, “Governing your data to meet industry standards is a bit like training yourself to speak a universal language.”  

Because you can’t expect thousands of suppliers and customers to understand your parts and products if you’re all using different words for them.  

That’s how you annoy people, slow down business and make mistakes when things get lost in translation. 

So, you need to publish in compliance with the relevant industry standards, by ensuring there are checks and balances at the point of data entry.  

Continue reading to discover how chief data officers can go drive business transformation by using master data governance to comply with industry standards. 

Playing nice with others: why industry standards matter 

Industry standards are something all large businesses will need to comply with, if they want to get anywhere in the world.   

What do industry standards look like? Let’s take ECLASS as an example. It’s a worldwide data standard for the classification and unique description of product master data.  

One of ECLASS’s strengths is in enabling cross-company process data management in engineering. It means people and organizations can speak in the same language when it comes to their products and services, even if they’re in different countries.  

When you’re speaking the same language, you benefit in all sorts of ways, including efficiencies, reductions in disputes and improved relationships with trading partners. 

But if both sides aren’t adhering to industry standards, then there’ll be friction. You might send data to a supplier or customer in your trading ecosystem, but they refuse it. Because if your data isn’t mapped to the standard, working with you won’t be worth the effort, especially if they’re too big or too busy. 

Here are a couple more specific examples where industry standards have a big effect.  

If the shoe fits: onboarding retail suppliers and customers 

Remember the last time you got a new pair of shoes? You go into a shop, see a pair you like, and ask the shop assistant if you have them in your size. So, they bring them in your size, you try them on, and they fit perfectly.  

That’s because we have standard shoe sizes, that everyone agrees to (and even though the UK, US, and EU annoyingly all use different numbers, they’re easy to translate from one to the other, even by the average consumer).  

But industry standards like those need to be applied to all sorts of things, like weight, color, size, currency and food ingredients and allergens. This could all apply to dozens or even hundreds of items when you’re making an agreement with a retailer to stock your products. 

Otherwise, you won’t be able to properly describe, buy in, ship, stock, advertise and sell across hundreds of stores. 

It’s a lot easier when people and systems in both parties mean the same thing.  

The fast and furious world of ecommerce: submitting data for new products or product updates 

Now let’s move away from brick and mortar shops, and look at ecommerce. Online shopping moves fast and is geared towards efficiency. And in the case of drop shippers, the vendor might never even get their hands on the items they’re reselling, leaving all the logistics to third parties.  

The bigger vendors could have tens of thousands of SKUs crossing multiple borders. And the items often have to make a return trip, when you make use of their often-generous returns policies. 

The trouble comes when you don’t have one industry standard to map to. If you do business with retailers in over 100 countries, you potentially have to communicate across dozens of standards to buy or sell parts and products. 

And as your business likely has its own language for how it stores information about parts and products, you’ll need to translate from your internal language to that of the industry standards you’re dealing with.  

>> Get a head start on with your data model, by accessing with this handy checklist. <<

What happens if you can’t effectively translate at scale?  

It’s one thing to translate a single piece of data between different industry standards, but what about when you’re dealing with thousands of records, fields and attributes?  

Without streamlining and automating your processes, you’ll spend all your time resolving disputes or correcting fields with your supplier, which is complicated and expensive. 

Here are just a few of the ways this friction can affect the success of your business.    

It impacts your trading relationships 

Distributors and retailers need to be able to trust that you’ll maintain data in accordance with the standards you’ve agreed on—otherwise, they’ll be looking elsewhere for trading partners. 

If you don’t complete attributes to data standards, you’ll cause all sorts of issues for processes and supply chain efficiency. For example, what if you get 1 inch mixed up with 1 meter?  

Warehouse workers will have a nightmare trying to load up trucks with your products—they might need 20 trucks when they were expecting to fit everything on two.  

It slows down your speed to market 

Category owners and account managers want to get products out to market as fast as possible—that’s best for both your business and retailers. And that needs to happen at scale across many products. 

Without seamless straight-through processing, translating your internal data language into multiple external industry standards can be manual, full of errors or non-compliant. That slows everything down to a crawl, meaning delays and dissatisfied customers, leading to fewer sales and higher cost to serve.   

It could mean Christmas is cancelled 

To illustrate the point, let’s think about seasonal variations in your products—Christmas is the best example here.  

There are many areas where your organization needs to invest for a successful Christmas season, such as your ad campaign or in-store point-of-sale promotions.  

But if you’re spending hours upon hours manually updating data to match the industry standards used by the retailers, you’ll miss the Christmas window, meaning all the money you’ve spent so far goes to waste.  

For businesses operating in fast-moving consumer goods (FMCG) industries, a bad Christmas can be fatal. 

Getting it right with master data governance 

Oof! Okay, we get that the idea of ruining Christmas is pretty depressing. So now it’s time for something more cheerful.  

When chief data officers ensure robust master data governance, they can empower product managers, account managers and other teams to push data out through all the relevant industry standards seamlessly and at scale.  

Proper data governance gives your teams access to validation, meaning that there’s always a ‘valid’ or ‘invalid’ result returned for every situation where data is communicated to retailers, or ingested from suppliers. 

It also makes life much easier for the person who’s responsible for staying on top of changes to industry standards—just think about regulation changes that could cause you problems, such as allergens reporting.   

The benefits of proper data governance 

Strong master data governance means you can enable straight-through processing, so that data passes smoothly from suppliers to you, and from you to retailers and distributers.  

Data will stay true and correct right from the original manufacturer, no matter whether it’s getting translated into your internal language, or back out into an industry standard. 

And that keeps teams moving efficiently, so they’re focused on value-adding work, like designing, producing, selling and shipping better products.  

It also helps them give better aftermarket service. Imagine you build and sell commercial airplanes, which are made up of millions of components. What if, say, one of the airlines you sell them to finds a particular bolt that needs replacing?  

It’s going to be almost impossible for a third-party engineer to order the right one if they call it something different than your business does internally.  

How Siemens Building Technologies eliminated 98% of data errors 

After emerging from multiple companies following several acquisitions, Siemens Building Technologies (SBT) found that its siloed management prevented efficient reuse of data. And with the same products being presented differently in the marketplace, it was impossible for SBT to achieve a unified brand image. 

By implementing Stibo Systems’ Product Master Data Management (MDM) solution, SBT’s head office now enters the data for all global products into a single digital hub in English and German. Other regions can access the data globally, with the ability to add further product data for their own regional use. 

Read the full case study to discover how Siemens Building Technologies used Product MDM to accomplish its goals to:  

• Save time and costs of data maintenance 
• Improve consistency across all channels and regions 
• Reduce data errors and inefficiencies 
• Speed up time-to-market 

In simple terms, ‘data quality’ refers to how good or reliable data is. It's about whether the data is accurate, complete, consistent and relevant for its intended use.  

High data quality means that the information is trustworthy and can be used effectively for making decisions, analysis or other purposes.  

Low data quality, on the other hand, means the data may contain errors, inconsistencies or missing pieces, which can lead to incorrect conclusions or unreliable outcomes. 

But it can get complicated. When your data gets used for lots of different purposes, each might have a distinct set of requirements.   

So, you can’t measure your data quality without knowing everything the data might be used for—in other words, its context.  

Because it’s about meeting particular needs, data could be high quality when used in one app, but low quality when used in an app that needs something different from the data.

What to look out for in a master data management platform 

When you have the right master data management platform, that supports your compliance with industry standards, these are just some of the things you’ll be able to achieve: 

• Make the life of your data stewards simpler as they manage workflows, processes and permissions around industry standards that are relevant to the business. 
• Gain the ability to map and transform attributes from your internal view of data to multiple industry standards. For example, if a product is entered as "white toilet” in one system, you can transform the data from one field into two to work in another system, mapping it as “white” in the color field and “toilet” in the name field.   
• Easily flag exceptions and put them into a bucket for reviewing and fixing, using an automated process for data mapping and transformation. 
• Keep track of updates to industry and regulatory standards, with the platform vendor’s industry experts ensuring their service is updated accordingly. 
• Roll back to whichever version of the industry standards you require once updates have been released. 

Get a head start on with industry standards data model, by downloading our handy checklist here.