How long does it take to implement a Data Governance framework?

A question I get asked frequently is about the duration of time and the complexities involved in implementing Data Governance - especially if it’s someone’s first time implementing Data Governance.

First things first - Data Governance is not just an agenda that can be crossed off once implemented. 

Data Governance is a long-term ongoing practice that constructs a healthier data environment in an organisation to support its goals. Data Governance extends beyond a single activity or project. 

With that in mind, let's address the question of duration.

Does Size Matter? 

Undoubtedly, the size of an organisation, the number of systems in place, and the complexity of the organisation and its systems will impact the timeline for implementing Data Governance. 

I always recommend a phased approach, this allows prioritisation and the gradual application of Data Governance principles across your organisation.

The Art of the Rollout

Many people think that the implementation of Data Governance can be approached in one of two ways.  That it can be introduced all at once - like a seismic shift.   Or , alternatively, you might decide to tackle it by business function, data domain, or a combination of both, depending on where the greatest need for Data Governance lies within your organisation at the time.  I’m a fan of the latter approach - having next seen it be successful when the first approach is attempted!

What is the Time Commitment?

Designing your Data Governance initiative and beginning to realise any benefits can typically take between a year to 18 months. However, this should be considered a ballpark estimate - as your organisation's complexity and size are variables that could extend this timeframe. For large multifacted organisations achieving comprehensive Data Governance could span several years.

Reflections

Embarking on the Data Governance journey requires thorough planning and a measured, phased execution. The size and complexity of your organisation will influence the timeframe. That being said, don't let this discourage you! Data Governance is an ongoing practice, and keeping the wheels in motion is critical in ensuring your organisation's approach to data remains adaptable and robust.

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 Danielle Titheridge

I met Danielle when she attended my training course and loved her enthusiasm, so I thought she would be a wonderful person to interview for this blog. Danielle describes herself as a ‘people’ person, sociable and likes to find solutions to problems.

She’s from Dorset and enjoys long coastal walks with her Hungarian Vizsla and likes spending time with family and friends too.

Like many of us, Danielle has had a number of different careers from a primary school teacher to working in HR and now Data Governance.

How long have you been working in Data Governance?

I have been working in Data Governance for approximately 6 months as a Data Governance Analyst. It is quite different to my HR Advisor role but there are similarities with working with people and being another support function for the organisation.  

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

I was working at the RNLI in the People department and then I saw an advert for a Data Governance Analyst and wondered what it was. It sounded really technical but after speaking with the Data Governance Team I learnt that a lot of it was about managing change and stakeholders and not having to be too technical at all. I applied for the job and was fortunate to be offered the role.  

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

People skills! I was put off originally thinking it was a really technical role (because I am not technical), however you don’t need to be really technical to be good at data governance. Don’t get me wrong, you need to understand some areas so you can understand how it can benefit the organisation but a lot of what we are focusing on is change management and increasing people’s confidence. We want them to have an understanding of what data governance is and how it can benefit them, their teams and the organisation as a whole. Once people understand the ‘why’ then they are more likely to engage and want to make positive change and efficiencies.

You do also need to have a lot of resilience at times and be able to adapt.   

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

Your website and blogs I found really helpful as well as your training course. It really cemented ideas in my head and really started to eliminate the imposter syndrome so I honestly can’t recommend it enough.

Also my wonderful colleagues in my immediate team but also our wider team to take the time to explain to me what we do and how it fits into the bigger picture and organisational strategy.

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

People not wanting to take accountability, specifically relating to data roles and the importance around this.

The ‘Why’- people still don’t really understand it. I think because it links with data protection and information security, people struggle to know the difference.

People see it as us asking them to do something else in their already busy day but the ironic thing is once they have it will provide them with more time as it creates efficiencies – work smarter not harder!

A recent project we have been working on is around classifications (sensitivity labels) and from a technical, system perspective there are a few difficulties but the main thing is the engagement and culture of people. So we have definitely had to redirect our focus and next steps and timing is definitely a crucial factor we have had to consider too.  

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

As I am working for a charity now I would be interested to see what it would be like to work for a private organisation one day and the difference in tools, culture and behaviour towards data governance.  

Finally, what single piece of advice would you give someone just starting out in Data Governance?

You don’t need to be an expert at it all and as we know technology changes, has updates etc so you will never 100% be up to date so learn to be ok with that… I am still learning to be ok with it.

It’s all about story telling and change management - so show people in a way they can relate to, pull on heart strings and then they will see the benefit.

What’s the Difference between a Data Governance Clinic and a Data Governance Mastermind?

With over 20 years of experience in Data Governance, my focus has been on sharing knowledge through coaching, consulting, and delivering courses and workshops. Two of the most popular programmes I offer are the Live Online Data Governance Training and Clinic and the 1 Day Data Governance Mastermind. 

Both these sessions are designed to help elevate your Data Governance capabilities, each catering to different needs and learning styles. I often receive questions about the differences between the clinic and the mastermind, as both involve collaborative learning. In today’s blog, I’ll be exploring the differences and sharing some tips on how to know which programme is right for you.

What is the Data Governance Clinic?

The Data Governance clinic is part of my Live Online Data Governance Training and Clinic. This programme is spread over two days - the first day is my Getting Started in Data Governance training course and the second day is the clinic. You can either sign up for both days or you can attend either just the training or just the clinic (if you understand the theory of Data Governance but are finding it challenging doing it in practice).

The Data Governance Clinic is a workshop format where you get the opportunity to ask questions to better understand Data Governance concepts or to work out how to make Data Governance work in your organisation.

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

There are always lots of questions to be covered so all questions are collated and prioritised by the attendees so that we focus on the issues that matter most to them.

Key Features of the Clinic:

• Workshop Format: This hands-on session allows you to discuss your specific Data Governance challenges and receive practical advice.

• Interactive Learning: Collaborate with peers facing similar issues, share insights, and network.

• Practical Application: Turn theory into actionable strategies with focused guidance on implementation within your organisation.

• Expert Facilitation: Benefit from my 20+ years of experience in Data Governance, ensuring in-depth insights and personalised advice.

This structured approach allows participants to gain actionable insights and build skills crucial for effective Data Governance practices in their respective organisations.

What is the Mastermind?

In contrast, the 1 Day Data Governance Mastermind is all about collaborative learning, collective problem-solving, and peer support among data professionals. 

Participants come together to share experiences, discuss challenges, and brainstorm solutions related to implementing and maintaining effective data governance practices within their organisations. My 1 Day Data Governance Mastermind is run once a quarter and attendees can come along to just one session or as many as they like. 

Everyone at the Mastermind gets time in the hot seat where they get to share their current challenges or questions and everyone on the mastermind (including me) share advice and guidance to help resolve the problem or suggest a way forward.

Key Characteristics of the Mastermind:

• Peer-to-Peer Learning: Members contribute diverse perspectives and insights from their experiences in Data Governance, fostering a rich exchange of ideas and strategies.

• Accountability and Support: The mastermind provides a supportive environment where members hold each other accountable for implementing Data Governance best practices and achieving organisational goals.

• Holistic Approach: Discussions encompass various aspects of Data Governance. 

• Long-term Growth: Masterminds focus on continuous improvement and adaptation to evolving Data Governance challenges, promoting ongoing professional development and skill enhancement.

Choosing Between the Clinic and the Mastermind for Data Governance

Data professionals aiming to implement or enhance Data Governance practices should consider their specific learning preferences and objectives:

• Choose the training and clinic If… 

• You require structured instruction and guidance. 

• You are focusing on mastering specific aspects or techniques within Data Governance implementation.

• You prefer a curriculum-driven approach to learning about Data Governance frameworks and practices.

• Choose a Mastermind If… 

• You value peer support, collective problem-solving, and collaborative learning experiences.

• You seek accountability and motivation from a group of peers to implement and sustain data governance initiatives.

• You want to engage in discussions that encompass broader aspects of Data Governance strategy and implementation.

Both the clinic and the mastermind offer valuable opportunities for data professionals to deepen their understanding and implementation of Data Governance. By choosing the right forum, data professionals can effectively navigate the complexities of Data Governance, contribute to organisational success, and drive data-driven decision-making. 

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If you’re still not sure I've designed a short quiz that evaluates your current expertise, goals, and learning preferences to help determine the most beneficial path for you.

Data Quality: The Secret Sauce for AI and Generative AI Success

I’m so please to introduce this guest blog written by the fantastic Tejasvi Addagada. Tejasvi is a seasoned business strategist, data and software architect with an impressive track record. In this blog, Tejasvi delves into the critical importance of data quality in training Large Language Models (LLMs) and the profound impact it has on the accuracy and reliability of AI-generated predictions and decisions.

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We often marvel at the sheer scale of Large Language Models (LLMs). These behemoths owe their ‘largeness’ to the vast volumes of data they are trained on, collected from a myriad of sources. The lifeblood of these models is the quality of this big data. It’s through this data that the models learn the intricate dance of language patterns, enabling them to generate coherent and contextually accurate responses.

However, like a grain of sand in a well-oiled machine, inadequacies in data quality can introduce noise into the model training process. This noise can lead to spurious outcomes, much like a radio catching static between stations. This noise significantly impedes the model’s ability to generate the correct embeddings - the mathematical representations of words in high-dimensional space. This, in turn, affects the model’s capacity to comprehend and generate accurate and meaningful context. In essence, while the size of LLMs is impressive, it’s the quality of the data they’re trained on that truly determines their effectiveness. It’s a reminder that in the realm of AI, quality often trumps quantity.

Considering the impact of data quality on AI outcomes, how might erroneous training data lead to unreliable predictions, and what steps can be taken to ensure the integrity of AI-generated results?

As a data executive, I’ve often found myself fascinated by the intricacies of artificial intelligence and the relation with quality of data. However, it’s important to remember that AI, like any tool, is only as good as the data it’s trained on.

Consider this: Inaccurate Predictions: If an AI model is trained on data that’s full of errors or inaccuracies, it’s like trying to navigate a maze while blindfolded. The model may stumble and falter, leading to predictions that are unreliable or downright incorrect. It underscores the importance of using accurate, high-quality data when training these models.

Then there’s the Ripple Effect of Biased Outputs: Imagine feeding an AI model data that’s skewed or biased. The model, in turn, might churn out results that perpetuate these biases, leading to outcomes that are unfair or skewed. It’s a stark reminder of why we need to use unbiased data when training AI models.

And what about Non-usable Content? If the data fed into the model is incomplete or inconsistent, it can leave the model confused. The result? Outputs that are gibberish or make little to no sense.

Lastly, let’s not forget the potential for Misleading Information: If the AI is trained on erroneous data records, it could end up generating information that’s misleading. This could be harmful, especially if such information is used for decision-making.

In conclusion, the quality and integrity of the data used in AI training are paramount. It’s a topic that deserves our attention as we continue to explore the vast potential of artificial intelligence.

How can poor data quality impact customer satisfaction and loyalty?

In organizations, we often discuss the marvels of artificial intelligence and data-driven decision making. However, an often overlooked aspect is the quality of data that fuels these systems.

The Cost of Poor Data Quality: Imagine a scenario where the quality of data is compromised. This could lead to inaccurate predictions and decisions, which in turn could result in significant financial losses. What is the confidence that an organization can have on it’s financial statement, regulatory returns or key-strategic decisions that it takes. All such aspects are assumed to be 100% accurate basis the quality of data that fuels them. It’s akin to building a house on a foundation - the structure is bound to be supported if it’s qualitative.

The Role of Data Quality in Generative AI: Generative AI, a branch of artificial intelligence that excels at creating new data from existing datasets, relies heavily on the quality of the input data that is used for training as well as fine-tuning using techniques like re-inforced learning. The better the data, the more accurate the insights it can generate.

The Data Scientist’s Dilemma: According to data researchers, data scientists spend a whopping 80% of their time just preparing and organizing data. This underscores the importance and the challenge of maintaining high-quality data.

The Impact on Customer Satisfaction and Loyalty: Poor data quality can also have a ripple effect on customer satisfaction. Inaccurate predictions can lead to wrong decisions, which can leave customers dissatisfied with the product or service they receive. This could, in turn, decrease customer loyalty.

The Solution: Systematic quality control and verification of data can help mitigate these issues. It’s like having a robust quality check in a production line, ensuring that the final product meets the desired standards.

In conclusion, the quality of data is not just a technical issue, but a business imperative that can impact financial outcomes, customer satisfaction, and loyalty. As we continue to navigate the data-driven landscape, let’s remember - quality matters.

Why is data quality crucial for accurate predictions and decisions in both traditional analytics and Generative AI?

Some use cases for AI and generative AI include natural language processing, image recognition, and automated generation of content. Generative AI can also be used to automate the process of data analysis, allowing for faster and more accurate results. Generative AI has a wide range of applications in a variety of industries.

Financial Document Search and Synthesis:  Generative AI can assist banks in finding and summarizing internal documents such as contracts, policies, credit memos, underwriting documents, trading agreements, lending terms, claims, and regulatory filings. It can quickly summarize complex documents like mortgage-backed securities contracts.

Personalized Financial Recommendations: AI can provide personalized financial advice by analyzing customer data, investment portfolios, risk profiles, and market trends to generate tailored investment recommendations. This can help clients make informed decisions about asset allocation, risk management, and financial planning.

Enhanced Virtual Assistants: Generative AI-powered virtual assistants can automate tasks, handle customer inquiries, and provide real-time support. This frees up human agents to focus on more complex tasks, improving customer service efficiency.

Which dimensions of data quality are important for AI and Generative AI?

The dimensions of quality that a data office has to prioritize for data collection are as follows:

Ø  Accuracy - The term “accuracy” refers to the degree to which information correctly reflects an event, location, person, or other entity. How well does data reflect reality, like a phone number from a customer?

Ø  Completeness - Data is considered “complete” when it fulfills expectations of comprehensiveness. Is there complete data available to process for a specific purpose, like “housing expense” to provide a loan?

n  Column completeness – Is the complete “phone number” available?

n  Group completeness – Are all attributes of “address” available? Is there complete fill rate in storage to process all customers?

Ø  Validity: The “Validity” dimension of data quality refers to the extent to which data conforms to a specific format or follows predefined business rules. For instance, many systems require you to enter your birthday in a specific format, and if you don’t, it’s considered invalid.

The use of Artificial Intelligence is increasing to generate insights that advance customer journeys. Use cases like credit decisions, personalization, and customer experience are increasingly using AI. The quality of data across the diverse collection of data-sets must be assured to reduce the vulnerability of data-driven models.

Is there a direct implication of less quality data on outcomes of AI models?

Data quality significantly dictates the efficacy of machine learning models. The creation of accurate AI models hinges on the availability of high-quality data, which requires stringent quality control and verification measures. The influence of qualitative training and testing data can be particularly emphasized. As accurate training can result in accurate outcomes when the model is implemented. The importance of automated data quality assessments for AI has been underscored, with a variety of data-oriented techniques and tools being recommended to facilitate this process.

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Tejasvi Addagada is a seasoned business strategist, data and software architect with an impressive track record. He has held prestigious positions such as Head of Data & Analytics, Chief Data Officer (CDO), and Privacy Officer in global organizations. Tejasvi is also a bestselling author, having written two books on data management and risk. His expertise extends to assisting over fifteen organizations in developing winning business models. He provides contingency-based strategies, culture-oriented operating models, and customized organizational structures, all while leveraging cutting-edge technology engineering.

Why Aligning Data Governance with Corporate Strategy is Essential

An interesting question I've been asked a few times is whether or not you need to worry about corporate strategy when focusing on Data Governance. This is a really great query and one which I'm going to answer very honestly.

When I first started data governance, I didn't give corporate strategy a second thought.

The first few years that I was doing Data Governance, I didn't think about the corporate strategy of the organisation I was working for. I felt it just wasn't relevant to me as a Data Governance Manager because clearly, I needed to be getting on with designing a Data Governance framework and starting to roll out Data Governance. But, with the benefit of hindsight, I realised that I had totally missed the point - I needed to align ALL my activities with the corporate strategy.

The key to success

For me, not aligning with the corporate strategy led to limited and unsustainable successes. Senior stakeholders were just not interested in the issues I decided to fix and so they didn’t engage with or support my efforts. Instead, I found the key to success of a Data Governance initiative is bringing along senior stakeholders and keeping them engaged. The only way to do this is to focus on the things that interest them - delivering things that will help achieve the corporate strategy objectives that they are responsible for.

Talking to stakeholders about how Data Governance can help achieve these objectives changes the narrative. It's not about best practice or doing Data Governance because it's trendy, it's about aligning it with organisational objectives.

Top Tips

It's vital not to miss out on the alignment between data governance and corporate strategy. Here's my top 4 ways you can ensure you stay on track.

1. Spend time early on in your Data Governance journey to understand your organisation's corporate strategy and how Data Governance can support achieving them. You can achieve this by engaging with key stakeholders to gain insights into the organisation's goals. For example, if a corporate objective is to reduce costs by a certain percentage, Data Governance can play a significant role in achieving that by addressing issues like rework, mistakes and inefficiencies caused by poor data management.

2. Assess whether your data is currently good enough to help meet corporate objectives. If the strategy says that you are going to review current customers/sectors and withdraw from certain ones, is your data good enough to make sure that the right decisions are made?

3. Check in periodically with your Data Governance framework to ensure it focuses on the right things. Corporate strategies change and you need to make sure that your Data Governance Framework and approach evolves to continue to support the needs of your organisation. In order to gain senior stakeholder buy-in and ongoing engagement we need to help them achieve their strategic objectives.

4. Corporate strategy is not someone else's problem; it's integral to the success of Data Governance.

To summarise

Aligning Data Governance with corporate strategy is essential for success. Learn from my past mistakes and take time to work through and align your Data Governance approach with your organisation's corporate strategy.

If you would like help aligning your Data Governance approach with your corporate strategy, which not book a call using the button below to find out how I can support you?

Data Governance Interview with Carl Beaman

My latest Data Governance Interview is with the fantastic Carl Beaman.

Please can you introduce yourself, Carl?

I feel data has always been of interest to me in my working career, but it hasn’t always been straightforward in working with data how I would have liked. In previous employment, I took it as a responsibility of my own to manage data quality and implement processes and methods to ensure data was managed correctly throughout its life. This was in a customer service role which I believe helped me to gain an understanding of the importance of the information we work with both from a customer perspective and an employee perspective.

Outside of work, I enjoy playing football and going on walks, so being active is important. I also enjoy gaming and problem-solving (a key factor for me working with data and looking to implement Data Governance).

How long have you been working in Data Governance?

I’m still early into my role of Data Governance Officer at Plus Dane and will be coming up to 1 year in position in July. I have worked in data quality prior to this officially for around 2 years but have worked with data for well over 10 years.

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

 I’ve worked with data for a number of years and have always had an interest in information and data and working to ensure it is the best it can be, updated, managed etc. and this led to a desire to move into an official position working with Data Quality at Plus Dane. I love working with information, facts, figures but ensuring it all works as it should and having confidence in this.

 Performing the Data Quality Officer role had a natural progression for me in applying Data Governance and working on the implementation of this. I’ve worked in customer facing roles and with information and have seen firsthand how missing or incorrect data can have a massive impact on people both inside and outside of the business.

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

I enjoy the challenge this role brings every day, not just the data itself, but how people view data and Data Governance itself. So having these conversations and being able to present the reasons why we are doing what we are doing matters.

So, the characteristics I feel I have which are important in this role are communication and confidence, being able to convey the correct message to various people within the organisation and doing this comfortably digitally and personally. Also, patience, this is not a quick fix or a straightforward process, Data Governance is a journey, and, in some cases, you need to be understanding of people and them not being engaged with what we want to achieve.

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

Personally, I have tried to use as many resources available to me as possible to build on the skills I’d already developed and to learn new skills that I can carry forward. I have used LinkedIn to follow Data Governance experts, used webinars provided by various organisations, attended training courses – I found your training courses that I have been able to attend useful.

 One thing I do feel is extremely beneficial and again was part of your training course, was to be able to interact with people from other organisations and have conversations around where they were on their journey implementing Data Governance. This gave me reassurance in what we are doing but is great to hear that others have faced similar challenges or are facing them as well.

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

Getting people engaged or to “buy-in” to Data Governance. When I have spoken about Data Governance there have been occasions where you can see the other person switch off because you mention Governance and that can be disheartening. But that is part of the challenge and I have had instances where people have told me they weren’t fans of data or didn’t have an interest in Data Quality or Governance but now they can see the benefits and why we are implementing Data Governance into the business.

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

I used to work for a mobile communications company so it would be interesting to see how they manage their data and what level of Data Governance is in place.

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

Don’t be disheartened when you are faced with people who aren’t on board with Data Governance because they will be there. Have confidence in yourself in what you want to achieve, and that challenge will be worthwhile.

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

A memorable experience for me is having feedback on the work that has been performed so far with Data Governance and how this has changed the mindset of people and how they view our data and the importance of Data Governance.

To face the challenge of people not being interested or feeling that this has little to no importance is tough considering this is my line of work, but to be told that these feelings have changed, and they can see the benefits of the work we are carrying out is great. But the best bit – this is to hear that rather than dread the Data Governance meetings or updates, they look forward to these and enjoy participating and engaging with us, that helps me to maintain focus and keep driving forwards.

What are good key performance/risk indicators for data?

In my many years of working in data governance, this question comes up quite a lot…Can you provide examples of standard Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) for data?

So in today’s blog I’m going to share my thoughts on this question.

The challenge of stating what a ‘standard’ KPI/KRI is

The trouble with trying to state what is a good KPI or KRI is that there is no standard approach to doing Data Governance. Different organisations are implementing if for different reasons so what you will measure will be different. If I share examples based on past client experiences, they might not work for your organisation.

As I have mentioned many times before there is no one-size-fits all approach to Data Governance and this also extends to KPIs and KRIs. This means that there's no fast track to coming up with your KPIs or KRIs because involvement from those who consume or use the data is needed.

That said, there are some points for consideration I can offer to get you thinking about the direction of your organisation's KPIs and KRIs.

Have a clear data governance framework

Use the roles that you have established as part of your Data Governance Framework. Engage with stakeholders to identify and establish the KPIs that matter to them and your organisation. Involving the right people is critical to ensure that the resulting KPIs measure the things that your stakeholders are interested in. No one will act on them if they are measuring something that they do not care about.

Collaborate, collaborate, collaborate

When it comes to KRIs, collaboration with your operational risk team and data owners is key. Often, data owners also have the role of risk owner, making them pivotal in understanding the risks associated with critical data. Facilitate discussions around potential risks and the controls needed to mitigate them. This lays the foundation for deciding which KRIs are necessary and useful.

I understand the desire for a shortcut, but I’m afraid that there isn't one. But putting in the effort pays off. Meaningful KPIs and KRIs, aligned with your organisation's unique needs, are the result of collaboration and dedication. Designed and agreed correctly, these indicators will provide important insights and prompt action if they are not being met.

To conclude

While I may not have handed you ready-made examples to simplify your task, please believe me that taking shortcuts when setting your KPIs and KRIs will not make your life easier and deliver useful measures in the long run. If you're asking me what makes good KPIs/KRIs, then I say embrace the process, engage your stakeholders and work with them collaboratively. This is how you truly get to put in place KPIs and KRIs that mean something to your organisation specifically.

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If you have more topics or questions you'd like me to explore in future, feel free to email your suggestions to questions@nicolaaskham.com. 

Silent Disability and Inclusivity in The Workplace

Did you know it's Deaf Awareness Week? 

In honour of this brilliant initiative to raise awareness about the issues affecting deaf people, I wanted to tell you something you may not know about me - I am totally deaf in my right ear and have been since I was 14 years old. 

My deafness is considered a silent disability. Because I can still hear with my left, this means that, if I had not just told you upfront about my disability, you may not otherwise have known I had one at all. 

Silent disabilities can be tricky to navigate in the workplace. I know from experience it can be hard for the person, their coworkers and team leaders alike to know where they stand and how, or even if, to respectfully approach the subject. 

Therefore, after years of not telling that many people about my deafness, instead of discussing Data Governance topics as is my norm, in this article, I want explore silent disabilities in the workplace, with reference to my own experiences.

A bit about me 

Many years ago, I had aspirations of becoming a lawyer. However, my journey took a different turn due to a rather extraordinary accident which happened to me in school. The incident resulted in a dislocated jaw and significant damage to my right inner ear, causing me to miss a year of my O-level studies and also part of my A-level studies. 

Some might say having one functioning ear isn't severe, but it was isolating and painful for me. I withdrew from school and hobbies. Lip reading classes, though well-intentioned, proved challenging as everyone there was more profoundly deaf. Feeling like I didn't fit in either the hearing or deaf world, returning to school became traumatic despite the support I had.

Nevertheless, I did return and ended up excelling in my studies. But with ongoing surgeries on my horizon, I questioned the feasibility of pursuing law as I didn’t feel that I’d be able to get my degree with my health issues. Instead, I joined Lloyds Bank's management training scheme which led me to a successful career in the bank.  After an interesting route through very different roles,  I eventually got into project management, which led me into the realm of data governance. 

My health hurdles and deafness may have delayed aspects of my career, but they also fueled a resilient determination to overcome obstacles and pursue what became a passion for data governance.

The impact of my silent disability on my data career

Due to my deafness, I believe my career journey had a delayed trajectory. Whilst I did achieve the role of assistant manager at the bank at a very young age, a lack of confidence, because of my deafness, slowed my career trajectory after that point.

Despite being labelled as shy by my bosses, I wasn't as shy as they thought; I just hesitated when unsure about the conversation. I selectively shared my deafness, explaining it to those I built relationships with rather than announcing it right away. I was (and still can be) paranoid about where I could sit in meeting rooms, so I could hear as much as possible. One incident during an office move highlighted the challenges. My boss unintentionally placed me with all of the team sitting on my deaf side.  This meant that I couldn't hear my teammates, requiring a change of plan and a seat swap with another colleague who didn't quite understand why he had to give up his window seat! But it wasn't about preference—it was about being included in team interactions. Not everyone understands that and it can be isolating for deaf people and act as a barrier to confidence and inclusion. 

 Perhaps my stubbornness, fueled by experiences of being told "no" due to deafness, motivated me in the end. But initially, I kept my deafness private at work and it was a real confidence issue for me. These days, I see it very differently. 

Whilst participating in a workshop about sharing origin stories, I realised that being deaf is part of my narrative, influencing who I am. This shift in perspective prompted me to openly discuss my hearing challenges during a Women in Data panel, resonating with a fellow attendee who thanked me afterwards for exposing the struggles of networking events with hearing loss.

Handling silent disabilities with respect

From my experience, I believe people tend to make assumptions about capabilities, not exclusive to deafness. I’m guilty of it myself.

In a recent incident in Basel, without thought, I jumped to my feet to offer my tram seat to a person on crutches, but the man politely declined and walked on. He didn't want or need the seat. It emphasised to me the importance of asking and not presuming, as I've learned from my sensitivity about hearing loss.

In a similar light, I also unwittingly discovered a colleague's deafness when he was reporting to me when I was acting as an interim data governance manager. Despite sailing through interviews seamlessly, subtle signs like selective seating and delayed responses made me suspect he had hearing difficulties. Approaching the issue delicately, I often tried to reassure and encourage him by speaking of my own deafness around him. For example, I wear a hearing aid and I would often make a big deal of this when talking to him as a way of indicating, ‘Look, I'm deaf, you can talk to me. I get it’. Despite my efforts, he did not decide to discuss the topic with me.

I eventually asked him outright. Though initially hesitant, he did share his deafness with me and eventually with wider colleagues and it worked out for the better. As more colleagues were informed, he excelled in his career, eventually becoming the head of data governance at another organisation. This experience emphasises further the importance of fostering an inclusive and respectful environment where acknowledging and accommodating differences can lead to personal and professional growth.

Having the confidence to talk about silent disabilities in the workplace 

Building confidence to discuss my deafness in the workplace became crucial for me, especially as a consultant. I found that if I didn't disclose it, the alternative was that people thought I was stupid! Fearing misconceptions, I realised the positive reactions outweighed the negative ones.

Over the years I have come to realise that it is better if everyone understands why I want to sit where my “good ear” is in the direction of the majority of people and that if I say pardon it wasn’t that I wasn’t paying attention, I genuinely didn’t hear what they were saying. 

It reinforced the idea that acknowledging our challenges doesn't diminish our capabilities, and that is the message I'd like to emphasise to anyone reading this and looking for confidence in talking about their disability. 

It may shape your narrative but it doesn't have to impact your success.