Data Governance Know How

Embarking on a journey in Data Governance can feel isolating and overwhelming. It’s very common in this field to struggle to find a sense of connection and understanding in your day-to-day work. But there are places to feel less lonely, share best practices, and meet new Data Governance friends including ‘Data Governance Know How’!

Data Governance Know How is a group that a few connections and I came together to form and is a supportive community focused on Data Governance.

When first venturing into the realm of Data Governance, many of us find ourselves navigating uncharted waters. The lack of camaraderie and shared experiences can make this journey feel solitary and discouraging. Despite attending various industry events, professionals often struggle to establish meaningful connections with like-minded peers. Plus, Data Governance can be a solitary role within an organisation, where you are fighting for change and improvements that others might not fully comprehend. This sense of isolation and the challenges it brings served as the catalyst for the creation of Data Governance Know How.

The group is a space where professionals can come together, share experiences, and collaborate. It’s a therapeutic outlet, allowing Data Governance professionals to discuss their challenges and find solace in the company of others who understand their struggles. We also try to help each other find practical solutions to the issues faced in Data Governance and cultivate a collaborative environment for our members to exchange knowledge and support one another.

Since being formed, Data Governance Know How has evolved and made a huge difference in the DG community, it’s even been described as a "big data hug!” The support also extends beyond formal events, with members readily connecting on professional networking platforms like LinkedIn and Slack.

The group's structure facilitates networking opportunities and the creation of collaborative outputs and resources, providing valuable references for individuals on their data governance journeys.

Data Governance Know How is a pure joy to organise and we all love working with people as passionate about Data Governance as we are. We all find a huge amount of satisfaction in supporting others with their career growth.

We always try to make our events interactive and engaging and try to stay away from the traditional webinar structure, involving our members and catering to their preferences as much as possible. We take topic suggestions and welcome members as speakers to share their insights. By combining practical learning elements with networking opportunities, our events provide a balanced and enjoyable experience for participants and our virtual events allow Data Governance professionals from around the world to participate and benefit from the community.

For those interested in joining, membership is accessible through our Meetup group. We actively encourage new members to join and engage with the community and we have a dedicated channel to foster ongoing conversations and knowledge exchange between events.

If you’re a Data Governance professional seeking support, networking opportunities, and the chance to contribute to the data governance community, I promise you will find a welcoming environment within this group!

What are the key components of a data culture?

Data culture is the collective behaviours and beliefs of people who value, practice and encourage the use of data to improve decision-making. As a result, data is woven into the operations, mindset and identity of an organisation. A data culture equips everyone in an organisation with the insights they need to be truly data-driven.

However, developing a data-driven culture requires a comprehensive approach that involves training and education, infrastructure and tools, organisational support, and a continuous emphasis on data-driven decision-making and learning from a senior level all the way through the ranks.

But, whilst establishing a data-driven culture can seem challenging, if a company is able to achieve it, the benefits are huge. A strong data culture can lead to better insights, improved decision-making, innovation, and a competitive advantage for organisations.

The difference between data culture and data governance

Maintaining an effective, shared data culture can feel like a balancing act between control, compliance, and data access. As data ownership rightfully moves from the exclusive hands of IT into lines of business, companies struggle to implement and enforce organisation-wide policies that balance data access with control and compliance.

Data governance is an important part of data culture because it provides the framework for organisations to balance the need for data control and the necessity of removing gatekeepers to enable data democratisation and expedite the broad use of data. But data culture itself is a far larger initiative, touching every aspect of business life and every employee and data user.

What are the key components of a data culture?

The key components of a data-driven culture are:

Data Literacy: This refers to the ability of an organisation's workforce to read, understand, and interpret data. A data-literate workforce is essential to building a data-driven culture.

Data-Driven Decision Making: A data-driven culture is one where decisions are made based on data insights rather than intuition or assumptions. Data-driven decision-making ensures that decisions are based on facts, not opinions.

Accessible Data: Data should be available to everyone who needs it in the organisation. This includes ensuring that data is stored in a centralised location and that employees have the necessary tools and training to access and use it effectively.

Data Quality: Data quality is crucial to the success of a data culture. To ensure that data is accurate and reliable, organisations must establish standards for data collection, processing, and storage.

Continuous Learning: A data culture requires a commitment to continuous learning and improvement. This includes ongoing training and education for employees on the latest data tools, techniques, and best practices.

Collaboration: Collaboration is essential to building a data culture. This includes sharing data insights across teams and departments, breaking down silos, and encouraging a culture of transparency and open communication.

Accountability: An effective data culture requires accountability. This includes establishing clear goals and metrics, tracking progress, and holding employees and teams accountable for achieving their objectives.

By incorporating these key components into their values, beliefs, and practices, organizations can build a strong data culture that supports data-driven decision-making and helps drive business success.

Benefits of a strong data culture

In an increasingly complex data landscape, a lack of data literacy, due to skills gaps or an inconsistent understanding of data across the organisation, has thrown data access and control out of balance. This is characterised by either data gatekeeping with too many restrictions hampering the use of data, or a data free-for-all with too much access threatening data quality and compliance. Either situation leads to widespread frustration across the organisation.

A strong data culture is the solution to all these problems. With a common understanding of the meaning, importance, and applications of data, the entire organisation is positioned to get the most from its biggest asset

How do you create a data culture?

There is no doubt that creating a data-driven culture is a challenge. There is no single answer or magic solution, but a successful strategy for culture change takes all the various stakeholders into account, understanding their needs and where they fall in the rollout.

When it comes to data, culture change starts with the data team, IT leaders, and the chief data officer (CDO). They need to establish the organisation-wide data strategy necessary to balance appropriate control of the data with data access that will support the business.

With a common set of expectations, a common language for data across the organisation, and a shared emphasis on the importance of data for business decisions, it’s easy for the rest of the business to follow suit.

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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 if you would like to know more about how I can help you develop a data culture, including data literacy training, then please book a call using the button below.

How the COM-B Model for behaviour change can be used when implementing Data Governance

The biggest mistake I see in organisations implementing Data Governance failing to address culture change as part of their initiative. This mistake is by far the most common I see and it can ultimately lead to the complete failure of a data governance initiative. I have seen situations where people have actually designed a really great framework that is ideal for their organisation, but it's been not successful because it's not implemented properly - they failed to address the culture change side of things.

The result of that is your business users, your stakeholders, just feel that data governance is being done to them and not for or with them as it should be. In this scenario, they tend to do as little as possible of what you're asking them to do, or even nothing at all, if they can possibly get away with it.

Simply, you can't start to manage your data as an asset and realise its value if you don't address that culture change.

How to avoid it

The first and most simple thing is to apply some really good change management techniques and if you are not well-versed in them, I'm sure there are people in your organisation who are, but it boils down to lots of very good quality communication with all of your business stakeholders.

This is going to be different communications for the different groups of stakeholders about their role in the data governance implementation and making sure there is good training in place for everybody in your data governance framework who has a role to play like data owners or data stewards.

It's really important that you bring these people along the journey with you because if you don't address the culture change your data governance initiative is never going to deliver the benefits you were hoping for.

The COM-B Model

One approach that can be used to facilitate behaviour change in Data Governance is the COM-B model. The COM-B model is a theoretical framework used to understand behaviour and behaviour change.

The COM-B model proposes that behaviour is influenced by three main factors: capability, opportunity, and motivation. Capability is a person’s individual's ability to perform the behaviour. Opportunity refers to the external factors that facilitate or inhibit the behaviour and motivation is the internal factors that drive behaviour.

When implementing Data Governance, the COM-B model can be used to identify the specific factors that may be preventing individuals or teams from adopting the new behaviours and practices that come with a new data governance initiative. By identifying these factors, Data Governance teams can identify ways in which to address behaviour to best support the implementation of data governance.

Capability and Data Governance

Capability is an individual's physical and psychological ability to perform the desired behaviour. In the context of Data Governance, this may include knowledge and skills related to data governance, such as data quality or metadata management. If individuals or teams lack the necessary capabilities, they may struggle to implement effective Data Governance practices.

To promote behaviour change in this area, it may be necessary to provide team training to help individuals develop the necessary skills and knowledge to succeed in the part they have to play in implementing a Data Governance initiative.

Opportunity and Data Governance

Opportunity refers to the external factors that facilitate or inhibit behaviour change. In the context of Data Governance, this may include access to resources, such as tools and technology, or organisational support.

To promote behaviour change in this area, it may be necessary to provide individuals or teams with resources to support effective Data Governance practices. This could include providing access to data management tools, such as data cataloguing tools, or providing organisational support, such as a dedicated Data Governance team.

Motivation and Data Governance

Motivation refers to the internal factors that drive behaviour, including beliefs, values, and attitudes. In the context of Data Governance, this may include beliefs about the importance of data quality or attitudes towards the role of Data Governance in the organisation.

To promote behaviour change in this area, it may be necessary to address individual or team beliefs and attitudes towards Data Governance. This could involve developing targeted communication campaigns or engagement strategies to help individuals understand the importance of Data Governance and its role in the organization.

Conclusion

The COM-B model provides a useful framework for understanding behaviour and behaviour change when implementing Data Governance.

By identifying the specific factors that may be preventing behaviour change, practitioners can develop targeted strategies to promote the adoption of new behaviours and practices.

By improving capabilities, providing opportunities, and addressing motivation, organizations can promote effective Data Governance practices and ensure the accuracy, availability, and security of their data assets.

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.

Can we use Process Owners instead of Data Owners?

Today’s blog has been inspired by a debate I saw on LinkedIn where somebody had suggested that we really don't need data owners because they're really hard to find and we could use process owners instead because they already exist at your organisation.

I obviously got involved in this debate and felt it important to do a blog on to explain fully why, while I think process owners can be useful, they are not a substitute for data owners. They are not interchangeable and there are two reasons for this.

Reason One

The first one is that in my experience that some sectors and even some companies within sectors are not that mature when it comes to processes, so if your organisation hasn't got your processes documented and mapped then it also doesn't have the concept of process owners in place.

In this case, you're off to a non-starter before you even start…

Reason Two

Let's consider the opposite of that and perhaps your organisation is really good at documenting its processes and has very well-embedded process owners as a role. These are people who understand that they get to have some responsibilities around that process and the inputs into it.

When I work with clients like that I think ‘this is great; this is going to make it easier for me to find my data owners’, but I never think let's use the process owners instead of data owners.

What I do is to look at the process owners as being likely candidates to become a data owner for me and this works really well.

They're usually suitably senior. They've got the authority to make decisions about the process so then it's an easy and logical next step to get them to take responsibility and have the authority to make decisions about data.

Now my problem with just using process owners instead of data owners is that some data is only used in one process in one place in your organisation. In this case, the process owner is likely to be the data owner. However, with data that is used in multiple processes, across your organisation that then gives you multiple data owners who are unlikely to think of consulting with each other to make sure that they're making consistent decisions about the data.

This in turn will mean that you're actually no better off because you won't have any common understanding of what the data means. You'll have nobody making consistent decisions with that overview of what that data means across the whole organisation.

By all means, if you have process owners, look at them as likely candidates to be your data owners, but please, I would encourage you not to think you can abandon trying to find data owners.

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.

Why is Data Governance Coaching and Training Expensive?

Unfortunately, Data Governance Coaching is not free. In fact, sometimes it can be quite the opposite… but there’s an easy answer to the question: ‘Why is Data Governance Coaching so expensive?’ Simply, it can add massive value to your organisation.

Not to mention, products and services that are “cheap” tend not to be cheap at all. They usually end up being very expensive because even though you thought you were getting a bargain you’ve just wasted your money and gained nothing from it.

It all depends on what you want to accomplish and the value you place on accomplishing it. Data Governance Coaching is no different.

If you want a Data Governance Coach with the skills, knowledge, and experience to help you achieve your business and data goals and give you all the tools you need to stay resilient and successful long after you’ve finished working with them, then you need to reframe your thinking – it’s an investment, not an expense.

If what they’re providing is the information, guidance and support that will get you where you want to go, that fee might initially seem expensive, but it isn’t really. It’s worth every penny.

Look at it this way… when you work with an effective, professional coach – someone who can add real value to you, your team and your business and help you realise your complete potential – you’re not just benefiting from the time you spend with them every week or month. You’re benefiting from all the knowledge they’ve accumulated over years of hard work and constant study.

But if you still need convincing here are my top four reasons to invest in Data Governance Coaching or training…

1.I know where you’re coming from because I’ve been there!

There’s lots of advice available online and in books, but how do you take this huge quantity of sometimes conflicting theory and turn it into something practical?  You might have tried sharing some of this theory with your senior stakeholders, but you are struggling to articulate what it means to them and your organisation.

What’s more, if you fail to convince them to support your Data Governance initiative and you don’t start to actually deliver value, the Data Governance initiative will fail, and you will be blamed. I get it. I have been there. We can work through it together. By investing it coaching, you are benefiting from the mistakes I’ve made in the past – so you don’t have to make them!

2. Real solutions to your problems!

Training gives you the opportunity to share the challenges you are experiencing with your Data Governance initiative and get pragmatic solutions. You also get the opportunity to share knowledge and network with others in a similar situation.

It’s all about turning theory into practical actions and you will get the opportunity to ask detailed and specific questions about implementing Data Governance in your own organisation and receive advice on how to overcome the challenges you may be facing.

During training, you’ll have the opportunity to share your questions and experiences to find the right approaches to resolve your Data Governance dilemmas, too.

3. You’ll gain confidence in your own Data Governance Initiative

It’s one thing thinking or hoping you’re doing the right thing - it’s another to know it! And to have the confidence to sell it to senior stakeholders.

There is no such thing as a standard approach to Data Governance but there are some clear steps that everyone needs to follow to gain senior stakeholder buy-in and to design a framework that is right for them.

Over 20 years of designing and implementing Data Governance Frameworks I have developed a practical approach that takes you through all the steps needed to be able to successfully design and implement a Data Governance Framework that is right for your organisation.

4. Data Governance can feel lonely - let’s network!

Attending a Data Governance course or Mastermind Day gives you the opportunity to meet other people who are in the same position as you and will be able to connect and workshop ideas beyond the course.

It’s a great opportunity to feel a little less alone in this big data governance world - especially if you’re brand new to it or the only person in your organisation whose remit it is.

You can find out more about my upcoming Live Online Data Governance Training and Clinic here and also my upcoming 1 Day Data Governance Mastermind Workshop here.

I hope that was helpful and 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.

The Rocky Horror Data Show: AI

Data shouldn’t be a wild and untamed thing, but sometimes it is just that - wild… and untamed. And unfortunately for our friend Tim, he’s about to find out just how wild and untamed data can be. As this is ‘The Rocky Data Horror Show’… where the data is not what it seems.

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Things at the Magical Wish Factory have been ticking along nicely since we last checked in with our Data Governance heroes, Janet and Tim – they’ve built and are in the process of implementing a Data Governance framework that’s had great senior stakeholder buy-in and things seem to be going well.

But, as with all things at the Magical Wish Factory, it’s not long before someone comes along to upset the apple cart – it wouldn’t be a Horror Data Show if not, eh?

Just when Tim thinks he’s finally got a handle on all the data the MWF produces, he’s called into a meeting with the board who drop a bombshell… they want to use AI to speed up the processing and granting of wishes!

Tim isn’t surprised the higher-ups are pushing for this move – trying to run before they could walk was what got the Magical Wish Factory into the data disaster that Tim has spent most of his time here untangling… there’s also a huge buzz around the technology just now but, is it right for every organisation? And more specifically is it right for the MWF? Tim doesn’t think so. In fact, he can see some major red flags – not least of all, the fact that although Data Governance is in full swing, it’s not yet quite established enough to be introducing such new technologies into the mix.

Tim tries to explain: “It’s no surprise that there's a rush of companies trying to adopt this technology Janet, but there’s only so much resource to go around, and that means that for some, the question of what should come first, AI or Data Governance, can be a little like the chicken and egg debate…”

Head of IT Janet picks up: “Artificial intelligence works by mimicking human processes by ingesting large amounts of ‘training’ data and analysing it for correlations and patterns and using these patterns to make predictions about future states.

“For example, a chatbot - the kind you may encounter on an online retailer’s website or in place of technical support - is fed examples of text chats and can learn to produce lifelike exchanges with people and provide help and assistance, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

“All AI starts out as a program or algorithm written and taught by a highly skilled programmer. The learning aspect of AI programming focuses on acquiring data and creating rules for how the AI will turn the data into actionable information. These rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.”

The reasoning aspect of AI programming focuses on choosing the right algorithm to reach your desired outcome and the self-correction process is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.

So, as a consequence, if you've got missing or inaccurate data, your wrong and potentially inaccurate data can and will guide these exciting technologies that your organisation has spent a fortune on in the wrong direction and so they will make the wrong decisions and the consequences could be costly and maybe even disastrous.

That all means one thing: AI needs the right data in order to learn. And when you’re halfway through a Data Governance initiative that is really not the right time to be introducing AI – the data simply isn’t ready for it.

For the Magical Wish Factory, that means the correct wishes may not get granted if AI decides the wrong criteria against which to assess them and some people may get wishes they do not deserve or more than their quota.

So, what will the senior stakeholders at the Magical Wish Factory decide to do…

Stay tuned for episode seven of The Data Governance Coach’s series ‘The Rocky Horror Data Show’ and follow the adventures of Tim and Janet as they try to implement a successful data governance initiative at the Magical Wish Factory.

How to identify Data Owners, where multiple areas of the organisation use the same data?

Identifying Data Owners is something that many people struggle with when they are starting out in Data Governance. When multiple different teams use that data it can often confuse matters, which is why I think this is a good question to answer.

It is something I come across an awful lot when helping my clients do data governance.

and I'm sure you will too, because unless the data is only used by one area, it's often not clear-cut who the data owners should be.

If you have read any of my other articles about data ownership you will know that In my experience, it really is important that you only have one data owner per data set, because if you have any more you end up with a situation where a number of people get together and they discuss and debate the data, but they rarely come to any conclusions and they just totally derail your data governance initiative. Simply, it doesn't work.

I believe very strongly that you should have just one data owner per data set and, yes, it can be challenging if you have multiple people using the same data and even more challenging when they all want to own the data but there are a number of different ways of dealing with this.

My preferred way is to see if I can break down the data and identify different chunks of it that can be split across multiple data owners. For example, for one organisation, which was an insurer, we had a big debate over who owned the customer data and the head of underwriting believed quite strongly that they owned it, but the head of marketing also believed that they owned customer data and when we sat and talked to them we actually agreed that they owned different subsets of it.

We broke it down and we had customer risk details owned by the underwriting area

and customer contact details were owned by the marketing team and that worked very well… for a few months.

They got on and did everything we asked them to do as part of doing data governance

until we got to the day when somebody reported a data quality issue with postcode data

and my heart sank because I had this horrible feeling that I knew what was going to happen.

I was right, when we asked them both who owned the postcode, they both said they did, and they both believed it was them.

Interestingly, because we had split the data out, they’d both been doing the role and got their head around it for a few months. We got them back together and discussed it

and at that point, the head of marketing said “You know what, I actually don't think I am the data owner. I think I'm a key consumer of the data and I want my needs and requirements of that data to be considered, but I don't think I am the right person to be the data owner.” We then put all customer details back into one data set and had one data owner.

The moral of that story is… splitting it sometimes works, and sometimes it doesn't.

Simply, you have to be flexible and understand that you may need to change it again

further down the line, but it doesn't stop you from trying.

The other way of doing it if splitting the data into subsets it isn't an option, is to look which area or team really dictates the standards by which that data is captured. Do you have somebody that is setting the rules and saying this is how we do this? Because in which case they should be the data owner.

If you've got a number of other teams elsewhere in your organisation that are allowed to set the rules around that data (and that is a very rare circumstance) I sometimes come up with a two-level data owner model, but I prefer to use that as an absolute last resort because adding any complexity to your data governance framework makes it harder to embed and harder to make it successful.

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 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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