Data Governance Interview with ChatGPT

Back in 2021 I wrote this blog about why you need to have Data Governance in place before your organisation embraces Artificial Intelligence (AI).

There have been so many advances in easily accessible AI technologies recently that I am getting lots of questions about why you do need to worry about Data Governance first. So I started to draft a blog to address this and then decided that perhaps it would be interesting to ask a well know AI tool, ChatGPT, for its thoughts on the matter.  Please read on to discover our interesting discussion on the topic:

Please introduce yourself and explain why you are interested in Data Governance

Hello! I am ChatGPT, a large language model trained by OpenAI. I am interested in a wide range of topics, including data governance, because of the crucial role that data plays in our society and economy today.

Data is increasingly being recognized as a valuable asset, and organizations are investing heavily in collecting, storing, and analyzing data to gain insights and make better decisions. However, as the amount of data being generated grows exponentially, so do the challenges of managing, securing, and utilizing this data effectively.

Data governance provides a framework for addressing these challenges and ensuring that data is managed in a way that maximizes its value while minimizing risks. I am fascinated by the various aspects of data governance, including data quality, compliance, and ethics, and how they intersect with various industries and domains.

Furthermore, data governance is crucial for the development and deployment of AI, which is becoming increasingly prevalent in our lives. As an AI language model, I understand the importance of data governance in ensuring that AI models are developed and deployed ethically, responsibly, and with a focus on fairness and accountability.

In conclusion, I am interested in data governance because of its importance in managing data effectively, promoting ethical and responsible AI development, and ensuring that data is used in a way that benefits individuals, organizations, and society as a whole.

What are your thoughts on the value of Data Governance to an organisation?

As an AI language model, I can tell you that Data Governance is an essential practice for any organization that wants to effectively manage its data assets. Data governance is the process of managing the availability, usability, and integrity of the data used by an organization.

Here are some of the ways in which data governance can add value to an organization:

Improved Data Quality: With proper data governance, an organization can ensure that its data is accurate, complete, and consistent. This, in turn, can help to improve the quality of decision-making and enable the organization to achieve its goals more effectively.

Compliance: Data governance can help an organization to comply with data-related regulations and laws such as the GDPR or HIPAA. By establishing policies and procedures for data collection, usage, and retention, an organization can avoid legal and financial penalties associated with non-compliance.

Cost Reduction: Proper data governance can help to reduce the costs associated with managing data by eliminating duplicate or unnecessary data, ensuring that data is stored in the most appropriate and cost-effective way, and optimizing data management processes.

Overall, data governance is a critical practice that can help organizations to effectively manage their data assets, improve decision-making, comply with regulations, and reduce costs.

Is Data Governance important for AI?

Yes, data governance is extremely important for AI. This is because AI algorithms rely heavily on data to learn and make predictions. If the data used to train an AI model is inaccurate, incomplete, biased, or inconsistent, the model's predictions and recommendations can be unreliable or even harmful.

Proper data governance is therefore essential to ensure that the data used to train AI models is of high quality, accurate, and representative of the real world. This involves establishing policies and procedures for data collection, storage, and usage.

Additionally, data governance can help to address the issue of algorithmic bias in AI. Bias can be introduced into an AI model if the data used to train it is biased or if the model's algorithms are designed with inherent biases. Data governance can help to mitigate these issues by ensuring that data is diverse, representative, and unbiased, and by establishing ethical guidelines for AI development and deployment.

In summary, data governance is critical for AI as it ensures the quality and reliability of the data used to train AI models, reduces the risk of algorithmic bias, and promotes ethical AI development and deployment.

Thank you for taking the time to answer my questions

You're welcome! It was my pleasure to assist you. If you have any more questions in the future, don't hesitate to ask!

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If you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or if you’d like to know more about how I can help you and your organisation then please book a call using the button below.

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What is a Data Office?

The answer to this question is going to be quite short and succinct, much like the question itself… A ‘Data Office’, in my opinion, is just another term for having a central team that looks after Data Governance. 

The vast majority of clients that I have worked with over the years would probably call this a Data Governance team or a data quality team, but I do know a number of organisations who would call that a Data Office.

In my experience, a Data Office has perhaps a scope that is maybe a little wider than a traditional Data Governance team. They are the team which is supporting Data Governance and Data Quality within the organisation but may have a wider remit and also support Master Data Management and other data management activities.

So, it is perhaps a term that is not a direct comparison to a Data Governance team, but it's very similar.

If you have a central Data Governance team and you hear people talking about a Data Office, you're not lacking in something. As long as you've got a central support for your Data Governance Framework, it doesn't really matter what you call it.

But I find this is a good example of something that we do an awful lot as Data Governance professionals… we spend the majority of our days talking to business users and asking them to draft definitions for the data that they use, but at the same time, we use terminology that we don't always define or perhaps we use terminology that we don't use consistently and I think this is something that we definitely need to improve upon.

So, if you have a Data Office, fabulous. Make sure you explain what that means when you're sharing it with the business and if you have a Data Governance team, likewise.

You need to explain what that means to the business and use that definition consistently.

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or if you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Tiankai Feng - Data Governance Interview

Tiankai has been working in data all his career with experience in Data Analytics, Data Science and most recently, Data Governance. He’s most passionate about the “human” part of data – collaboration, communication & creativity – and uses entertaining unconventional ways to make data more fun, for example through his music.

How long have you been working in Data Governance?

I switched from Data Analytics to Data Governance in January 2022, leading a Data Governance team dedicated to Product Data as a domain.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

The hiring manager that recruited me for Data Governance has explained to me that this role needs somebody who makes the area more visible and more understood by others – and I really liked that challenge.

My learning curve had not been as steep as before anyway already in my previous role, so this change was very welcome and I was excited to “switch sides”.

What characteristics do you have that make you successful at Data Governance and why?

I think I have three key characteristics that make me a different Data Governance as leaders: people focus, communication and transparency.

People focus – I don’t only pay attention to all data governance team members being motivated by working on things and enjoy, I also try to make sure cross-functional collaboration is based on intrinsic motivation, which has to be based on relationships, not just a “transactional” view of business.

Communication – I like to communicate and get really creative in my communication, which especially Data Governance could benefit a lot from. No matter if it it’s a rap song about data governance, launching an internal talk show  to upskill the wider organization or making a “data heroes” campaign to propagate data ownership and data stewardship – I keep surprising people with Data Governance content and thereby make it memorable.

Transparency – Data Governance is all about transparency, but seeing small progress in Data Governance as wins is also part of it. Combining a “making progress” mindset with high transparency means showing impact continuously, thereby changing perception of Data Governance into being a value driver, not just a cost center.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

“Disrupting Data Governance” by Laura Madsen is a great book to have impulses for a new way of doing Data Governance. It especially shows the contrast of “old world” information vs “new world” data and how Data Governance has to be adapted. It inspired my Data Governance a lot.

What is the biggest challenge you have ever faced in a Data Governance implementation?

Resistance from misunderstandings – everybody agreeing that Data Governance should be implemented and embedded in business processes, but than realizing that everybody has very different expectations towards what Data Governance should and should not do.

Spending a lot of efforts in communication and upskillings is key.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I think different industries come with different challenges that are very exciting, so I would not say I am against any specific industry. I’m a continuous learner and optimist so I hope my career will surprise me!

What single piece of advice would you give someone just starting out in Data Governance?

I would have three points of advice:

1. You can not overcommunicate in data governance – do it from the very beginning and don’t stop

2. Leadership sponsorship is a make or break – always keep that sponsorship relationship and ensure that your DG efforts are supported

3. Build active advocates in your organization – external word of mouth is always more effective to drive collaboration than “you tooting your own horn”

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

Recording a Data Governance themed Christmas Song with Nicola herself was an absolute highlight for me!

‘tis not the season right now, but the performance and our lyrics are still great. Have fun.

Is Access Management part of Data Governance?

I was emailed the question recently and, in my haste, to provide an answer, I simply replied ‘no, it is not - Access Management is part of Data Security, so it's not a Data Governance activity, and the asker very rightly came back to me and challenged me on this.

They said, “Oh, well, that's really odd because I thought that Data Owners were responsible for Access Management” and I realised that I should have perhaps given a more full answer to the person who'd asked the question – so let’s do this now, first by look at what Access Management really is.

What is Access Management?

Access management is primarily an information security, IT and data governance process used to grant access to valid users and prohibit invalid users.

Typically, AM is used in conjunction with identity access management (IAM). Identity management creates, provisions and controls different users, roles, groups and policies, whereas AM ensures that these roles and policies are followed.

An AM-based application/system stores the different user roles and their profiles, and process user access requests based on the data/profile/roles.

A common misunderstanding

Access Management, deciding who should or shouldn't have access to our data,

is definitely part of a Data Security activity, along with agreeing perhaps the levels of encryption and security classifications. But the worlds of Data Governance and Data Security do overlap.

Now, if you read the blog I posted a few months ago about the difference between Data Governance and Data Management you will remember that I talked about the DAMA DMBOK wheel.

Data Governance is in the middle of that wheel which lists all the other data management disciplines in it because Data Governance provides a foundation for many other data management disciplines and, in the case of Data Security, it is around the roles and responsibilities because we will have worked hard to find the right people to be Data Owners for the data.

Now, we might want to only talk to these people in terms of whether the data is good enough quality, whether we have definitions for it, and how we resolve issues with that data but if we've found the right people, then they're also the right people to make other decisions about that data.

It is quite often the case that I will work with the Data Security team at my client's

to make sure that their approach is aligned with the Data Governance framework and that it is the Data Owner that is asked to approve access requests to their data so, you can see how this confusion arises because it would become the responsibility of the Data Owner to approve access requests. However, if we're being purist about it, it's not a Data Governance activity.

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or if you would like to know more about how I can help you and your organisation then please book a call using the button below.

Critical Data Elements and How Much Data Do We Have to Hold About Them?

Such are the demands of modern business it is important to understand that we cannot put the same level of data governance onto all data that we have in our organisation. There is probably neither the appetite nor the resources to do that and nor would it probably bring enough benefits.

Therefore, we need to make sure we are working out which is our most important data and putting most of our efforts into that. I believe there is quite often the temptation to believe

that the data governance team are responsible for finding the critical data and I would challenge that.

Would you expect one person or a very small number of people in your organisation to know every single piece of data and to know which is the most important? Surely it is the people in the business that understand what data is the most important to them. So, I think it's really important that you get the business to identify what is important to them.

How do we know which data is the most important?

We need to do this in a consistent and logical manner. Your organisation should agree to some set criteria for how you're going to identify which is the most important data within your organisation and there are many ways of doing this.

I personally like to identify three different levels. For example, you could have critical, important and “everything else”, or a tiered system like high, medium and low.

But whatever you chose to call them always aim to have three levels because this is how you are going to differentiate three different amounts of effort:

High is the really important data and that's where you put all your effort, you will want definitions, data quality rules and data lineage in place.

Medium is data that's fairly important, you'd like some controls and understanding of it,

but we don't need to go quite so overboard as we do the really important stuff.

And when it comes to everything else… Well, we might never get to do data governance on everything else. Maybe we will when we sorted out the other things, but we've clearly identified that it's really low in the pecking order.

Once you’ve agreed your categories you can write some criteria according to what your organisation does and how they generally measure things anyway and allow business users to consistently evaluate their data against it to come up with the right classification for their data.

Setting out criteria for measuring the importance of data

This method means you need to work with different people across your whole business early on in your data governance journey.  I call this a stage Data Discovery.

You should run workshops with your key stakeholders for probably every function or department in your organisation and get them to tell you what data they use and what data they produce. Once you've done that, you can agree which data they may be the data owners for and you can also then ask the data owner to tell you which of that data is the most important to them.

Now, some data will be used by multiple people in different parts of your organisation and so it's important that this is done with every single function because one function might say “that data is absolutely critical to us” and another department may only refer to that data as more of a contextual reference and therefore it won’t be as high on their list of priorities.

So make sure you get all viewpoints so that you can identify its overarching level which will be the highest level that anybody has classified it as.

The second part of the question is, what do you hold about it?

This is the kind of information that you're going to document in your data glossary, and I've seen people go totally overboard in collecting all sorts of stuff.

As with all things that are data governance, I believe the best thing is to start with something really simple. This isn't an academic exercise.

We are documenting this data so that it can add some value so that the organisation can understand what it is, where it is, what is being used for and whom we should go to make decisions on it.

Cover your basics, capture that information and you should have a really good starting place and then after that talk to your business users, ask them if they were looking up that data to find out more about it but don't start trying to capture everything. You'll be giving people lots of work to do and then they're going to rail against you, whereas, in reality, we want them to understand, this isn't too onerous a task, it’s going to have some really valuable benefits in the long term.

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Who owns the Data in a Data Warehouse?

A lot of the confusion that rises around data governance is organisations trying to figure out who owns what data and who is responsible for that data – and I am willing to admit that even I made the odd mistake when I first started out. Thankfully, I have the benefit of hindsight and I’ve made some of the mistakes so that you don’t have to!

So, let’s look at data warehouses… I can understand that people think it would be so much easier if we could just have one person owning that data in the data warehouse. That's exactly what I did when I was first starting out in Data Governance because it was really easy. We already had a business owner of the data warehouse so I went to them and asked them if they would own the data as well - and they agreed!

However, it was only after that that I started finding that this actually caused more problems than it solved, because if you have one person owning that data how could they possibly understand everything about that data and know everything about it?

I would ask the person to make decisions on that data and they’d go

“Oh, I don't know about it because I don't know why that data is collected. It just happens to be in the data warehouse.”

Another scenario that I would frequently see is the owner of the data warehouse coming to me to complain that some of the data in the data warehouse was poor quality. I'd go to the person who owns the data in System A where that data was coming from, and I'd say “the data warehouse data owner says you're sending rubbish data” and they would reply “No, I'm not. My data is wonderful. They're corrupting it when they load it and transform it into the data warehouse.”

And in this no-win scenario, the data governance officer is piggy in the middle getting sent between systems whilst each side blamed the other for the poor quality data and nothing got solved and nothing got sorted. This is why I realised the hard way that having one person owning the data in a data warehouse really doesn't help.

What we have to do is have one data owner for one data set wherever it is in your organisation, because we need one person who can make decisions about it and be held accountable for the quality of that data - not multiple people making different and potentially conflicting decisions about data that actually just happens to reside on multiple different systems.

Simply, find the most appropriate person to own the data – and do not just let this role fall on the owner of the data warehouse; often they don’t have a complete overview or understanding of the data.

It really is worth the effort of finding the right person upfront. It takes a little bit more effort, but you will be putting in place a good foundation for your Data Governance framework.

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or if  you would like to know more about how I can help you and your organisation then please book a call using the button below.

Data Governance Interview with Gary Maw

Gary has over 40 years in IT with experience across a variety of industry sectors including developing and leading teams across IT Services, Project and Programme Management, Application Development, and over the last 10 years or so more focus on data - MI, BI, Data Warehousing, Governance, Quality etc.

How long have you been working in Data Governance?

Maybe around 6-7 years in practice, longer unknowingly as a necessity and bi-product of other roles.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

It came about out of genuine interest and frustration. I think frustration when delivering BI and DW services where governance was immature and therefore had an impact on results. The interest came about with a desire to put this right and contribute to enabling data as an asset and to remove similar frustrations for other people.

What characteristics do you have that make you successful at Data Governance and why?

Tenacity, endurance, perseverance, knowledge of data and associated issues, and sense of humour.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

There are many but anything produced by DAMA and also Precisely is good.

What is the biggest challenge you have ever faced in a Data Governance implementation?

Making it business-led and gaining acceptance and support across the business.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

It doesn’t really matter to me which industry it is as the principles are the same, it’s the reasons for doing it and obviously the business itself that differs. However, it would be good to implement this somewhere that has a positive impact on the planet, humanity, nature or people.

What single piece of advice would you give someone just starting out in Data Governance?

First of all, believe in what you are doing and don’t give in. If there are valid business reasons for doing it then seeing the benefits come to fruition is worth the pain. A basic understanding of people in terms of psychology will help to explain the behaviours and reactions that you will experience.

Secondly, gain an understanding and/or experience across several disciplines such as Data Management, Data Quality, Data Modelling, BI and Business Strategies in order to understand the need for data as an asset and the issues and difficulties involved in getting it there.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

They are all challenging but since my whole nature and approach is based on humour (not always directed well) I can recall many things but one that springs to mind is…

In the early days of people being introduced to computers on their desks, I found one older employee who didn’t need all the information presented to him. Subsequently, I found that he had tip-exed out on the screen all the information he didn’t need, not realizing that other selections would present different information.

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Don't forget if you have any questions, you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Data Governance 2022 Round-Up

Happy New Year! I hope you all had a lovely festive season and are looking forward to 2023 as much as I am! 

I have had a well-earned rest and I’m now raring to go - I have BIG hopes for 2023 and plan to help as many people as possible understand and manage their data. 

Which is why I thought I’d get off on the right foot and share with you my 10 most popular blogs from 2022. 

These are the blogs that have resonated and supported my readers and are perfect to have a flick through before you get underway for the upcoming year. 

1. Data Literacy: So What?

2. The Six Principles for Successful Data Governance

3. Do you know what is in a Data Governance Framework?

4. The Rocky Horror Data Show: Disastrous data definitions…

5. What is a Data Domain?

6. Data Governance Interview with Rob Saundby

7. Five Common Data Governance Misconceptions

8. What Makes a good Data Governance Consultant

9. Do I really need Data Governance when I’m doing a master Data Governance?

10. What the data and tech industries are doing to support Ukraine?

If you want to get a head start in 2023 with your organisation's data governance why not book a free call with me? 

You can also find out more about all the different courses and training options I offer.