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

As the saying goes, “If you’re not sure of the source, don’t drink the water.” 

It’s a similar principle for the governance of data enrichment. 

Because if you choose to drink that dodgy water, it’s not just you you’re potentially infecting. 

That water will flow through your organization, getting into many systems and exposing itself to people who might assume they’re fine to use it, and that it’s reliable and accurate.

But we’re here to help you stop that from happening. Continue reading this guide to see how you can drive business transformation by improving your data enrichment process with master data governance.

Back to basics—what is data enrichment?  

In a nutshell, data enrichment is the process of enhancing raw data with additional information to make it more valuable and useful for analysis or decision-making.

It involves adding context, insights or attributes to existing data, which can include demographic details, geographic locations, social media activity or other relevant data points.

Essentially, it's like adding extra layers of information to basic data to make it more meaningful and informative.

Third-party sources are often the best way to enrich your data set when a user looks into an individual record, or a set of records, but finds information missing from certain fields.  

For example, they might want to enrich a customer data set with addresses, company information, financials, credit scores, or risk data. 

When it comes to data enrichment, good data governance—where you have the right controls and approval processes in place—will ensure that people are careful about where they get the data from and what they use it for.  

Otherwise, instead of benefitting from data enrichment, your business might unwittingly be suffering from data impoverishment, and the commercial risks that go with it. 

Why data enrichment matters     

When data gets personal: personally identifiable information (PII) 

You only have to spend approximately 0.241 seconds on an online store these days before getting a popup offering a 10% discount on your first order—in exchange for you subscribing to their marketing emails.  

They’re pretty much all at it, due to the planned deprecation of third-party cookies.  

Put simply, there’ll be less third-party information available about the interests and behaviors of consumers. Or rather, there’ll be less widespread trade and re-trade of this information. Instead, you’ll be buying it directly from the businesses who collected it and got permission to sell it on to you. 

This cookie data is used extensively in digital experience personalization.  

Say you’re someone who watches videos of snowboarder Shaun White for three hours every week, as he throws “hella” crazy Triple Cork 1440s.  

Then it’s a safe bet that you’re more likely to be in the market to buy a new snowboard than your average Joe—and so you’ll be more likely to see targeted ads from snowboard brands.  

Likewise, if 3ft of fresh snow is forecast to fall next week at the ski resort that’s just two hours from your known address, then there’s probably another targeted ad coming your way.   

It’s a good time to look back at previous rounds of data enrichment—will the rules you used still be good going forward? Or do they need modifying or scrapping? It’s essential that your data enrichment process has checks and balances to safeguard personally identifiable information (PII). 

It also makes sense to look at what’s going on with the data once it’s in your systems.  

Is it easy to find data when it needs to be disclosed, modified or deleted? Or can you guarantee it was matched to the right person in each system? You don’t want to be sending Jerry Maguire snowboard promos when it’s Geri Maguire who truly loves to carve up the mountain.

When you don’t know who you’re dealing with: prospect and customer management 

If you’re a chief data officer working in a B2B organization, you and your team are uniquely positioned to understand the pains of poorly managed data enrichment.  

It can cause all sorts of issues when it comes to targeting the right prospects and customers, including: 

• Data being mapped to the wrong entity, e.g. Acme Ltd vs. No More Acne Ltd. 
• Data being enriched once and then left to degrade for two years, e.g. Sharon left Acme nine months ago (after getting offered a great package with a competitor) and you’re annoying her boss who still has to check her emails. 
• Validated customer data in the CRM being overwritten with speculative and generic information from a third party. 
• Data breaking processes across various systems because it’s in the wrong format (see how to stop this happening by checking out our data quality guide). 
• Information on things like revenue, industry, employees and location is inaccurate (possibly because the marketing team signed up to a free demo of a second-rate data tool). 

When you need to scope out your carbon emissions: regulations and sustainability 

Third-party data plays a key role in reporting the carbon emissions your business is responsible for, across both your own operations and the suppliers you work with.   

So, to be confident in how sustainable your product is, and that your suppliers are sticking to all standards and regulations, the data needs to be accurate, reliable and coming from a trustworthy source.  

Why data enrichment and data governance go hand in hand 

If your data isn’t enriched to a certain standard, it’ll be useless for downstream processing, because either:  

• It doesn’t fit the technical quality requirements and so won’t facilitate straight-through processing, or 
• It doesn’t meet the business quality requirements, so it’s not giving the system or user what they need to effectively complete the task at hand.  

So, why mightn’t your data enrichment have proper governance? It’s most likely because you don’t have the correct MDM software and resulting workflows, or you haven’t yet made sure the enriched data fits your data model. We also have a guide on data modelling—surprise, surprise—which you can read here.  

That means field-level validation is essential for enrichment. Every field of data needs to be: 

• Accurate  
• In adherence to the data model 
• From a trustworthy source 
• In line with all appropriate regulations 

So, anyone can enrich a record, but without strong data governance, you’ll get data quality problems—turning data enrichment into data impoverishment.  

>>Start building your foundation for better data enrichment, with this checklist.<<

The real-world costs of bad data governance 

If you’re still not convinced about just how important data governance is when it comes to data enrichment, here are some real-life examples of how businesses can suffer.  

We’re warning you though, things are about to get scary. 

Instagram and TikTok failed to protect children’s data 

In September 2022, Instagram was fined a whopping $403m for mishandling data (email addresses and phone numbers) belonging to children. The data in question was made more visible to others when users upgraded their profiles to business accounts to access Instagram’s analytics tools. 

Exactly a year later, in September 2023, TikTok was fined $370m—for similar reasons to Instagram. It was ruled that TikTok had failed to comply with multiple articles under GDPR, relating to how it processed the data of minors, as well as data security and data protection by design.   

IKEA and H&M accidentally committed greenwashing  

While Ikea was ahead of the game on sustainability initiatives, the furniture company inadvertently found itself involved in a scandal. In 2021, it was found to be selling wood that its supplier had illegally sourced from Russia. However, the blame was eventually placed on the Forest Stewardship Council (FSC), which had wrongly certified the wood for sustainability.  

In 2022, the fashion retailer H&M was accused of false advertising, when it was found that customers were given misleading sustainability information based on the Higg Sustainability Profile—a metric gauging how much carbon a material’s manufacturing releases into the atmosphere compared to traditional materials. In one instance, it was claimed that an item of clothing required 20% less water to create, when it was actually 20% more. H&M blamed it on a technical error. 

Getting it right with master data governance 

What you just read above might have left you in a bit of a cold sweat. So, now it’s time to make sure the same doesn’t happen to your own organization.  

As the chief data officer, it’s your responsibility to make sure everyone in the business feels empowered to: 

• Safely source the kind of enrichment data that helps the business operate in a fundamentally better way. 
• Prevent the organization from being impoverished through the ingestion of data that’s inaccurate or poor quality. 
• Ensure that any data that comes into your organization fits the data model, to keep all your systems running smoothly.  

When chief data officers get this right, they enable teams and departments to fill their knowledge gaps by mapping valuable attributes to known entities. They can then create more interesting data relationships that they can use to drive business performance and enhance the customer experiences.  

In other words, you'll become a source of guidance for your business.

The benefits of strong master data governance 

One of the most satisfying parts of working on your data governance, is that it’s easy to tell when you’ve got it right. Here are some of the things you’ll notice: 

• Third-party data behaves in a way that’s compatible with straight-through processing across all the systems in which it moves. 
• Processes that rely heavily on understanding customers, accounts and suppliers really well, behave just as they should—meaning fewer complaints and less time trying to help teams either fill their knowledge gaps or ‘just make do’. 
• You can give customers hyper-personalized experiences, and revenue teams can effectively experiment with new and better experiences on a regular basis. 
• E-commerce and digital teams can easily experiment with more appropriate offers, increasing conversion rates and lifetime value. 
• The business can react quicker to new opportunities and get into market faster, because it’s ingesting information that makes it easier to spot growth opportunities. 
• You can avoid the wrong opportunities before any business is done, e.g. by checking for politically exposed persons or accounts likely to default. 

Take control of third-party data 

Without the right governance in place, you could quickly lose control of third-party data, especially with the high volume that can be ingested into your systems, and the speed at which it can progress through them. 

But put these basic steps in place, and your domain leads will be free to take advantage of valuable third-party data while being selective with trust and credibility.  

It’ll give them the confidence to start hunting for data to fill knowledge gaps, which helps the business run better. 

Here’s what you need to do as a chief data officer: 

• Ensure the data coming in is checked for trustworthiness, accuracy and data model compliance before being ingested into business systems. Require a holding bucket and an assessment to be passed before data can be appended to records. 
• Make sure everyone understands who the data custodians are and their responsibilities. Use a clearly defined RACI and workflow, but make sure the workflows can still run if one data custodian isn’t available—you don’t want to have any single points of failure! 
• Make sure that the fields have clearly defined “Create, Read, Update, Delete (CRUD)” rights so that not just anyone can bring third-party data into your systems (or modify it once it’s in there). 
• Do all the above on a per-domain basis, to ensure all the necessary business use cases for that data are catered for. 
• Once all this is defined, ensure any third-party sources are integrated to bring in data in a regular, automated way. This will prevent unnecessary manual work and rework. 

What to look out for in a master data management platform 

When you have the right master data management platform, that supports proper data enrichment practices, these are just some of the things you’ll be able to achieve: 

• A history and audit trail for updates to data: The ability to roll back to previous versions means that next time someone comes to you all sweaty, because they accidentally rewrote your CRM with an import of some wonky revenue data for 10,000 companies, you can quickly reverse it. 

• Automated or assisted process resilience: The system will be aware of issues, flag them and take action so it can be rerouted/executed. It’ll help you predict, avoid and react to single points of failure, for example, or flag and solve issues with straight-through processing. 
• Audit and error message functionality: You should be able to monitor events that happen in the system—not just changes, but when blockages occur too. It then helps governance teams identify how to improve enrichment processes going forward. 

Get a head start on with your data enrichment, by downloading our handy checklist here.