One of the things I've always found most valuable when explaining Data Governance is a good analogy. They help bridge that gap between abstract concepts and everyday understanding, making Data Governance feel less like technical jargon and more like common sense. Helen Cullis’s take on "happy data" does exactly this, and I think you're going to love her puppy analogy as much as I did when she first shared it with me.

Over to Helen..

When I talk about happy data, I’m not trying to introduce anything revolutionary. This is really about a way of framing Data Governance that helps people across an organisation understand why data matters, why they should care about it, and what “good” actually looks like.

Because let’s be honest - in modern organisations, data is hard to see. It’s spread across cloud platforms, tools, pipelines and models. Unless it’s visibly breaking something or slowing people down, most people don’t feel connected to it in a meaningful way.

And that’s a problem.

So… what is happy data?

To explain this, I like to use an analogy: adopting a rescue puppy
(If you’re more of a cat or reptile person, don’t worry - the idea still works!)

When you first adopt a rescue puppy, it might be sad, unloved or untrusting. It may have health issues, bad habits or behavioural problems. But with the right care, attention and consistency, that same puppy can become healthy, confident and a much-loved member of the family.

Data is no different.

I came up with the idea of happy data when I was trying to find better ways to get people to care about the data they own - especially those in roles where working directly with databases or data pipelines isn’t part of their day-to-day work. Personifying data helps people understand both the current state of their data and what Data Governance was trying to achieve.

When I’m working with organisations at the start of a Data Governance journey, people often ask:
“But what exactly do you need me to do?”

Framing it as “making the data happier” works surprisingly well. I’ll often suggest that people ask themselves at the end of each week:
Have I done something to make the data happier?
Over time, that simple question helps turn data governance into a habit - small, consistent actions that people can feel good about, even when the progress feels incremental.

So, back to our rescue puppy analogy. What does happy data look like?

Clean, high-quality data
The puppy has had flea treatments, trimmed claws and regular baths and grooming.
Data that is actively looked after
Preventative and remedial activities protect quality both in pets and in data! Like feeding the puppy properly, taking it for walks and regular vet check-ups.
Trusted, valuable data
The puppy is well trained, has good recall, and adores the family.

**Why does happy data matter?**

Unhappy data causes real problems.

It makes organisations inefficient, creating manual workarounds, broken processes and delays. In some cases, data arrives too late to meet key deadlines. This is the equivalent of the puppy chewing your shoes or destroying the furniture.

Unhappy data also leads to poor decision-making. Genuine data-driven decisions are impossible without trusted data. In fact, leaders may end up relying more on experience and instinct than on low-quality, unreliable data.

There’s also a lost opportunity cost. With happier data, organisations can move faster, spot trends earlier and gain a competitive edge. This is becoming even more critical as AI and advanced analytics rely on high-quality, well-governed data to deliver meaningful outcomes.

Happy data also enables data democratisation. When data is trusted, well understood and appropriately governed, it can be shared more widely across the organisation. Combined with the right skills and training, this unlocks effective self-service and innovation.

Finally, there’s risk and compliance. While it shouldn’t be the only driver for Data Governance, it’s still important - like buying pet insurance or using a good harness or lead to keep the puppy safe.

How do you make your data happier?

There are three equally important pillars:

Data Quality
Data Culture
Data Governance

They’re deeply interconnected, and you need all three. You can have excellent data quality controls and governance frameworks, but if people don’t care about data - if the data culture is poor - you’ll never realise the full value. Data culture is often overlooked - not because it isn’t important, but because it’s harder to define and takes longer to change. It’s a slow burn.

Back to our puppy:

Have you created a safe environment where it can thrive?
(Do you have the right systems and tools in place?)
Does it have fleas that need treating?
(Have you cleaned up poor-quality or obsolete data?)
Are you doing regular vet check-ups?
(Do you do preventative and remedial data quality activities?)
Are the walks interesting and well planned?
(Do your data flows make sense?)
Can the puppy be trusted?
(Is your data easy to find, understand and reliable?)
Have you read up and taken professional advice?
(Do people have the skills and knowledge to fulfil their Data Governance responsibilities?)
Has it had training classes?
(Do metadata and governance create trust in what the data is telling you - especially when feeding AI models?)
Did you buy pet insurance?
(Are the right controls in place?)

A final thought

Happy data doesn’t happen by accident. It’s the result of many small, shared, consistent actions across quality, culture and governance.

So here’s my challenge: what could you do this week to make your data happier?

If you’d like to continue the conversation, feel free to connect with Helen on LinkedIn.

Helen is a Data Governance specialist with a real knack for bringing strategy and execution together. With extensive experience in leadership and data in complex organisations, she brings a collaborative, stakeholder-focused approach that combines technical knowledge with the ability to engage, influence, and drive meaningful change. She empowers organisations to get real value from data and confidently take advantage of transformative technologies like AI. What really stands out about her is her ability to build trust and strong relationships at every level. She has worked across the healthcare, higher education, and financial services sectors and is passionate about diversity and inclusion.