data governance implementation analogy through gardening

Today I'm did a bit of gardening. As I was planning the work in my head, I realized there were quite a few analogies between data governance and gardening. So let's use the gardening analogy to understand how we could establish a good data governance program.

1. Understanding the driver

In my garden we're sharing a see-through fence with our neighbors and I feel that this current setup doesn't offer us too much privacy. I want to implement some changes and address this issue. So privacy is the main driver. I could change the fence, but due to the building strata laws I'm not allowed to do that, so instead I've opted to plant some greenery to aid with the privacy in question. Of course, a bit more greenery would also aid with the overall esthetics, and seeing how I love the outdoors and nature so much it would also help with my wellbeing. Therefore in the end you can say the 3 drivers for my gardening project are: privacy, esthetics, and wellbeing.

A similar driver can be identified for data governance as well. Most often, ensuring regulatory compliance such as GDPR is one of the main reasons that in the past  years, a lot of organizations started investing in data governance. Also, let's not forget of the 2020 Citibank fine. The Treasury Department's Office of the Comptroller of the Currency has hit Citibank with a $400 million fine for deficiencies in enterprise-wide risk management, compliance risk management, data governance and internal controls. That's definitely a good driver for investing in data governance, don't you think?

What's your take on this? What do you think it's a more effective driver? The carrot or the stick? Yes - another garden analogy here.

Why do we need to understand the drivers? Because it will show us what we should focus on. In the case of my gardening I know I want some sort of bushes to cover enough of the fence that would provide me with the privacy that I need. For data governance it will show us what we should prioritize because we can't spend resources on improving everything from the start. Maybe one of the first thing is to develop and enforce our data policies and procedures related to GDPR compliance (for example).


2. Prepping the foundation

Similar to prepping soil for gardening where we need to remove rocks and weeds from the soil, in preparing for implementing our data governance program we need to remove the obstacles that would create problems with our implementation. We need to remove as many barriers as we can and this means a lot of communication.

We need to make sure that our employees and colleagues:

  • Understand the value of data governance
  • Why we are implementing data governance
  • Who is taking part of the implementation
  • When is this being done
  • What is being done
  • What's in it for them (probably one of the most important things on this list)

Let's face it, a data governance implementation infers some sort of change. So we need to have all stakeholders on board for the journey so that we reduce their resistant to change.

Again, we need to pour in knowledge and information as to what will be done, what is being done, maybe even what has been done in the past and what's in it for them.


3. Planting (Implementing)

The third step is planting. For data governance you need to start developing and planting (implementing) the policies, procedures, framework, roles and responsibilities on how data is handled during its acquisition and creation, maintenance, dissemination, and even archival and destruction.

This sounds overwhelming as there's a lot to be done there. I don't disagree with that, but just as with planting, you need to do it one step at a time. You can't plant everything at once. Unless you have a lot of budget and can hire a lot of people and do all these changes at once, but that's not sustainable because data governance also requires a cultural change that takes time. Throwing a lot of money at it can't address this cultural change completely.

If you don't know what step to take first, I recommend looking into a data governance maturity model. And if you'd like to learn more about those, check out my course on Data Governance Maturity Models.


4. Maintaining

Once the planting has finished we need to maintain our garden. If we don't water it, if we don't ensure the soil has enough nutrients, if we don't take out the weeds, our plants could perish.

Similar, we need to maintain our data governance program. Everything that we sow, needs to be taken care of. Data governance is not a one-time investment. Data changes. Even if it's clean, certain data just by its nature goes bad. Like physical addresses and phone numbers - this data can change and it decays over time - it goes from good to bad just by the virtue of not updating it. Regulations change, technology evolves, our business needs can shift, and so our data governance program needs to reflect these changes and evolve with the times.


5. Reaping the benefits

This is the best step as in here we are now benefiting from all the hard work we put in. One of the key benefits of data governance is better decision-making. This applies to both the decision-making process, as well as the decisions themselves. Well-governed data is more discoverable, making it easier for the relevant parties to find useful insights. It also means decisions will be based on the right data, ensuring greater accuracy and trust. In turn, this can lead to better service offerings, more customers, and just an overall increased revenue.

Data governance enables increased operational efficiency, better data quality and hence improved results from any program and project that relies on it such as machine learning and AI. And of course regulatory compliance - let's not forget about that. That's what we started with as one of our main drivers, right?


Conclusion

What about my garden? Well, I can definitely now enjoy a higher level of privacy, I think it looks quite nice and it's definitely contributing to my wellbeing. Please watch the video version as well and don't forget to subscribe to not miss out on upcoming video content. 

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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