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    <title>Umlaut Consulting Blog</title>
    <link>https://umlautconsulting.co.uk/blog</link>
    <description>Practical insights on data strategy, AI, governance, and organisational change.</description>
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    <item>
      <title>The Four Eras of Data</title>
      <link>https://umlautconsulting.co.uk/blog/the-four-eras-of-data</link>
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      <description>Over the last 30 years, I have watched organisations&#39; relationships with data transform continuously. From the days of ERP and operational reporting, through...</description>
      <content:encoded><![CDATA[Over the last 30 years, I have watched organisations' relationships with data transform continuously. From the days of ERP and operational reporting, through the rise of data warehousing and BI, to today's cloud-native data platforms and AI, the pace of change has been constant. I think about this as four distinct eras, each with its own business objectives, its own technology drivers, and its own definition of what data is actually for.

First, is what I call the Business Process Automation Era (1990s to the early 2000s) which saw organisations investing in ERP, CRM, and other systems to automate business processes. The goal was efficiency and control, not data and insight. Data was considered a by-product of digitised processes and was mainly used for backward-looking operational reporting. The technology drivers were SAP, Oracle, Siebel, and Excel-based reporting (maybe even Lotus 1-2-3. I'm definitely showing my age!)

Next came the Data Management and Decision Support Era (Mid 2000s to 2015). Organisations now fully recognised that data could support better decision-making. We experienced the rise of data governance, data warehouses, and BI to bring structure and access to data. Data became an asset to be stored, managed, and analysed, mostly for tactical and departmental decisions. The technology drivers were data warehouses, ETL tools, reporting applications, and dashboards.

The third, and current era is the Data Value Era (2015 to the recent present). There has been an explosion of data sources, cloud platforms, AI/ML, and stakeholder expectations. Boards now ask "How is our data driving growth, innovation, and resilience?". Data is now recognised as an investment for driving competitive advantage, monetisation, customer experience, and business agility. We have seen the emergence of cloud platforms like Microsoft Azure, Amazon AWS, Snowflake, and Databricks. We now regularly talk about concepts such as data products, data ops, and data mesh.

We are now on the cusp of moving to a fourth era, what I would call the Trusted Intelligence Era (from the present day onwards). We will be experiencing autonomous and adaptive data systems with organisations focused on investing in building trusted data ecosystems and establishing human-centred AI capabilities. The technology drivers are large language models, agentic AI systems, AI governance frameworks, and real-time data analytics. But the
challenge of this era is less about technology and more about trust and business value. Not simply whether we should do this, but why are we doing it. What value does it deliver? Also, how do we ensure that stakeholders such as employees, customers, and regulators trust the systems making decisions?

Looking back across these four eras, my experience has been consistent. The technology has never been the hardest part (although it definitely was more difficult before the internet!). What has always lagged is the human side, the governance, the culture, and the trust. In the era we are now entering, getting that right is key. The organisations that will succeed are those that invest as seriously in their people as they do in their technology. 

Which era do you think your organisation is in?]]></content:encoded>
      <pubDate>Sun, 07 Jun 2026 14:30:15 GMT</pubDate>
      <category>Data Strategy</category>
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      <title>Why Business Transformation Efforts Fail</title>
      <link>https://umlautconsulting.co.uk/blog/why-business-transformation-efforts-fail</link>
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      <description>How does an organisation attempt a large transformation project and fail three times? Why does a new hire who has joined a department to lead a change in the...</description>
      <content:encoded><![CDATA[How does an organisation attempt a large transformation project and fail three times? Why does a new hire who has joined a department to lead a change in the way of working end up resisting the new paradigm after only a couple of months in the position? A multi-million Pound organisation has been trying to improve its operational processes for more than 10 years and has still not succeeded. Why?

It’s because organisational culture beats strategy every time. This concept (or a variant of it) is typically attributed to Peter Drucker, although it’s difficult to find any conclusive evidence to support this. Regardless of the source though, the significance of this is huge. Strategy is typically implemented via projects, and if these projects are going to fail, then strategy can be very difficult or impossible to implement.

One might be tempted to attribute this to the theory of the strategy gap. The variance between a strategy and the actual outcome, usually due to the lack of understanding of the strategy by those that are meant to implement it. While this does exist, the manifestation of culture beating strategy is something altogether different.

Culture can be broadly defined as a set of ideas, customs, and social behaviours of a particular people or society. This can often be a force working against change.

Take a simple example from my office. The auto on/off sensor on the hand dryer in the men’s bathroom stopped working, so users resorted to switching the unit on and off using the main switch on the wall. This probably continued for at least six months until the office underwent refurbishment and the dryer was replaced. If was very obvious that the office had been refurbished, especially with the presence of the new stainless steel Vulcan hand dryer (the old one was white and asthmatic). Can you guess what happened? Yes, users continued to switch it on and off at the wall switch.

As these changes become bigger and more ambitious, so the complexities increase in making the changes stick. How do we then implement strategy or any change for that manner? Here are a few guidelines.

**Don’t rely on your business-as-usual people to implement your transformation initiative.** The people that you trust to run your day-to-day business should not be trusted with driving transformation. This includes contractors, who are quickly assimilated into the organisational culture. It’s not because they are bad people. It is because they are tasked with keeping the lights on and change is a threat to what they do. There is a good reason that organisations employ consultants to drive change.

**Identify your champions and engage them.** One of the key actions of any transformation initiative is the identification of stakeholders. A common mistake is to assume that the only stakeholders are the sponsors of the initiative or the recipients of it. A stakeholder needs to be defined as anyone who can either negatively or positively influence the change. Individuals or groups that can positively influence others and the transformation outcome need to be identified and engaged. These individuals and groups can articulate a positive message for initiative and become leaders of the change needed to ensure the initiative is successful. This becomes even more powerful when these champions are well respected in the business, regardless of rank.

**Don’t think that technology is a silver bullet.** Too many organisations see success as the implementation of a new technology. Success is realising the benefits that a new technology supports and enables. Don’t forget about the people and process changes that are more important than the technology changes.

**Realise benefits early.**. Avoid going for the big-bang approach and running long project cycles. Prove to your stakeholders that value can be delivered relatively quickly and once this is done, you’ll enjoy more latitude to deliver bigger changes. Don’t forget that every initiative needs to deliver measurable business value and have a supporting business case.

**Business leaders need to show up.** Whether it’s eating our own dog food or drinking our own champagne, the value of business leaders transforming by example in invaluable. I’ve been in product launch sessions where the business sponsor hasn’t shown up and the message this sort of thing sends is clear - if the sponsor isn’t bothered, why should I be? Sometimes a sponsor is the drive behind an initiative, sometimes they inherit ownership. Either way, coaching business leaders and management teams to lead change is an effective approach.

**Learn to spot passive resistance.** It’s sometimes not as obvious as a toddler saying “no” or a teenager blatantly arguing with a parent. Sometimes it’s often a “sorry, I don’t have time to look at this” or “sorry, I missed the meeting, I had to deal with a problem”. Or it’s ignoring emails or saying yes at the boardroom table and doing nothing. In the same way that you need to spot champions of change, so you need to identify detractors. Once they are identified, it’s usually easy to understand the reason why, and then put a mitigating plan in place.

**Think carefully about how you resource transformation projects.** In my career, I’ve been both a consultant and a contractor, as well as working with both. I once had an interesting discussion with a contractor, contemplating the difference between the two. I jokingly replied that it’s the same as comparing an alligator to a crocodile. He replied, “yes, I agree - one has a bigger mouth”. I’m not sure who won that debate but the reality is, there is a difference. The incentives are different. A consultant is engaged to achieve an end goal, to bring about a change, and then move on. Success is measured on this basis. The incentives for a contractor are different. While client success does matter, it’s important to contractors that they are renewed. While this is congruent with a business as usual engagement, it isn’t to a transformation project.

**Tie results to something measurable**. In the same way, as we discussed the consultants and contractors incentives, the same is true for other stakeholders. Whether they are delivering the change, enabling the change, or the recipients of the change. It is important to ask “What’s in it for me?” It might be making their job easier, earning their bonus, or keeping their boss happy. Make sure that there is a benefit for each stakeholder and make sure this benefit is clear.

Transformation needs to be revolutionary and not evolutionary. By nature, this is disruptive and not an easy thing to do. Hopefully these guidelines help.]]></content:encoded>
      <pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate>
      <category>Organisational Change</category>
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      <title>Data Governance Is Not About Governance</title>
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      <description>You’ve just been made responsible for improving data governance in your organisation. So you do what any sensible person would do. You research frameworks,...</description>
      <content:encoded><![CDATA[You’ve just been made responsible for improving data governance in your organisation. So you do what any sensible person would do. You research frameworks, create policies, and build a RACI. You even establish a Data Steering committee.

Six months later, the framework exists, the policies are published, and the steering committee meets monthly.

**BUT**, nobody’s behaviour has changed.

This is what many organisations get wrong about governance. They treat it as a design problem when it’s actually a organisational change opportunity.

The framework is never the hard part. Getting hundreds, sometimes thousands, of colleagues to change how they work with data every day is the hard part.

The organisations I’ve seen get this right do three things differently.

1. They start by listening, not legislating. They find out where the real pain is before writing a single policy.
2. They make governance feel like help, not oversight. If the first thing people hear about governance is “you’re doing it wrong,” you’ve already lost them.
3. They invest as much in communication and training as they do in tooling and processes.

Data governance IS organisational change. If you’re not running it like a change programme, you’re just building a library of documents nobody reads.

What’s been your experience? Has data governance ever landed well without a deliberate focus on bringing people along?]]></content:encoded>
      <pubDate>Fri, 13 Mar 2026 00:00:00 GMT</pubDate>
      <category>Data Governance</category>
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      <title>Yet Another Data Breach</title>
      <link>https://umlautconsulting.co.uk/blog/yet-another-data-breach</link>
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      <description>At an event yesterday evening, I talked to several business owners and business leaders, from small and mid-size organisations, about their data challenges....</description>
      <content:encoded><![CDATA[At an event yesterday evening, I talked to several business owners and business leaders, from small and mid-size organisations, about their data challenges. There were a number of themes that emerged, but the common one was that of customer data. How best the data can be used to drive business value and improve customer experience, but more importantly, how to do this in a safe and legal way.

These conversations left a positive imprint on me, knowing that small and medium-sized organisations are recognising, not only the value of data, but the importance of protecting it and managing it accordingly.

Switch to this morning, where one of the first emails in my inbox is from Westfield (Shopping Centre), reading “We have recently been made aware of unauthorised access to one of our databases containing certain information relating to customers...”. It seems the information involved (a bit of a euphemism) is my name, email address, telephone number, postcode, and date of birth. Just enough to make identity theft easier for criminals.

It would be unfair to single out Westfield here, because they are just one organisation in a long list of personal data breaches; Soundcloud, Substack, Trello, Planet Ice, Twitter, Canva, MyFitnessPal, Adobe, Dropbox, and the list goes on.

As we all know, there are harsh penalties for running afoul of the relevant regulator. The Information Commissioner’s Office lists 204 enforcement actions against organisations and individuals, including 55 monetary penalties and three prosecutions.

Why then do organisations continue to suffer these data breaches, with alarming regularity?

Taking off my data hat and putting on my organisational change hat, I would reframe the question into five parts.

1. Awareness. Are organisations not aware of the importance of good data management practices, and the legal implications of poor ones?
2. Desire. Are organisations aware of the risks of poor data management practices (or, if done right, the rewards of good data practices)?
3. Knowledge. Does the organisation collectively have the knowledge to manage data in a safe and secure way?
4. Ability. If the organisation (and its people) have the necessary knowledge, are they able to translate it into ability?
5. Reinforcement. Having ticked the previous four boxes, is the organisation able to sustain the changes they have made over time? Do they consider good data management practices a one-off paper-based exercise? Or an ongoing effort as the organisation and its environment evolve?

My experience is that different organisations are at different stages of this journey. I would love to hear your thoughts on this. Let me know in the comments where you think the stumbling block for many organisations is.]]></content:encoded>
      <pubDate>Wed, 11 Mar 2026 00:00:00 GMT</pubDate>
      <category>Data Governance</category>
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      <title>Beyond the Shadows: A Data Leader&#39;s Guide to Governing AI</title>
      <link>https://umlautconsulting.co.uk/blog/beyond-the-shadows-a-data-leaders-guide-to-governing-ai</link>
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      <description>For years technology professionals have had to deal with Shadow IT. For data professionals, this has meant governing everything from Excel Hell to cottage...</description>
      <content:encoded><![CDATA[For years technology professionals have had to deal with Shadow IT. For data professionals, this has meant governing everything from Excel Hell to cottage solutions built using low-code technologies. Now, with AI tools and platforms commoditised, 75% of knowledge workers are using these, and of those, 78% are bringing their own tools without waiting for formal approval.¹

When employees use unvetted AI tools without understanding the implications, they create significant organisational risk. How then do Data Leaders and their peers deal with this challenge? An outright ban on these tools never seems to work and also seems counter productive. Data Leaders need to move from reactive security to proactive risk resilience.

**The Cost of Inaction**

Shadow AI breaches cost an average of $4.63 million which is $670,000 more than a typical data breach, due to longer detection times - an average of 247 days.² In 2023, Samsung employees in its semiconductor business uploaded source code to ChatGPT to check for errors. This source code was confidential, and once uploaded, became part of ChatGPT’s training data and could not be removed.

The stakes are now even higher with the introduction of the EU AI Act. “We didn’t know” is no longer a defence, with penalties potentially reaching €35 million or 7% of an organisation’s global revenue, significantly exceeding the GDPR’s maximum penalties of €20 million or 4% of revenue.³

**The Current State**

69% of cybersecurity leaders suspect or have evidence of unauthorised public generative AI use, yet 55% of these organisations lack clearly defined AI vulnerability management or incident response plans^4. Privacy is a clear concern but a massive risk is the lack of transparency in AI algorithms as this complicates data-driven decision making. Add to this that by 2030, 50% of organisations will face delayed software upgrades and rising maintenance costs due to the unmanaged technical debt (such as poorly documented AI integration and incompatible code) caused by the use of generative AI.⁴

Trying to address these challenges with policy only does not work. For example, an organisation that I recently worked with had a policy that only permanent employees would be assigned a Microsoft Copilot licence, driving contractors to use unvetted services such as ChatGPT, potentially creating the same data leakage risks Samsung experienced.

**A Better Approach**

To address the risk of Shadow AI, data leaders should first seek to understand what challenges users are facing and the tools they are using to get their work done. In addition to this, there should be a technical audit to verify what applications and services are actually being used. These findings can then be used to drive the organisation’s AI strategy, inform policy, and support learning and development initiatives.

Shifting governance from restricting tools and platforms to enabling responsible AI usage helps knowledge workers, and therefore the organisation, develop the skills and practices necessary to unlock value from AI.

**The Opportunity**

The goal isn’t for data leaders to kill productivity but to ensure that AI usage is governed and the organisation and its employees are protected. Organisations that can get this right are are predicted to achieve 30% higher customer trust scores and 25% better compliance ratings by 2028 compared to organisations without proper AI governance.⁵

**References**

(https://news.microsoft.com/source/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/)
(https://www.ibm.com/reports/data-breach)
(https://artificialintelligenceact.eu/article/99/#:~:text=Summary,1.)
(https://www.infosecurity-magazine.com/news/gartner-40-firms-hit-shadow-ai/)
(https://www.servicenow.com/uk/blogs/2025/view-from-ai-control-tower#:~:text=Trust%20will%20be%20a%20defining,Controlling%20AI%20sprawl)]]></content:encoded>
      <pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate>
      <category>AI</category>
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      <title>Five Steps To Boost Data Literacy Across Your Organisation</title>
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      <description>“So, how do I export this to Excel?” The question that any person involved in delivering a data solution dreads. We’ve all heard it. The story usually goes...</description>
      <content:encoded><![CDATA[**“So, how do I export this to Excel?”**

The question that any person involved in delivering a data solution dreads. We’ve all heard it. The story usually goes something like this.

An organisation spends six months and several hundred thousand pounds on a modern data platform. Data ingestion, modelling, and stunning dashboards. It is all there. A year later though, usage is minimal, users are creating spreadmarts in Excel, or worse, they are still making decisions based on gut feel.

There are so many reasons why a data platform initiative can fail, but in this article we’ll explore one of the key reasons and what organisations can do to mitigate this.

You see, often the big investment is in technology, and not in people’s ability to understand, question, and use the data presented to them. Data literacy is the bridge between data investment and business value. It is the organisation equivalent of financial literacy - a language that everyone needs to speak to be effective.

In the rest of this article we’ll cover five steps that organisations can take to build that bridge.

**Step 1: Define What Literacy Means for You.**

Data literacy, like a data platform, is not a one-size-fits-all outcome. Providing company-wide, generic training is not going to work. You need to start by defining role-specific personas, for example what does a “data literate” account manager need to be able to achieve versus a supply chain analyst or HR business partner?

Use these personas to support your learning needs analysis. This will provide insight into the outcomes people are trying to achieve, what challenges they currently face, which skills they have, and which skills they need.

This makes any new skill relevant and immediately applicable and, very importantly, answers the question of “what’s in it for me?”

**Step 2: Create a Common Language**

Not having an aligned view on key business terms and metrics is a silent killer when it comes to adoption. Just ask any person in any organisation “how many customers do you have?”.

Organisations need to develop a shared and accessible business glossary. This should be a shared effort across the organisation, but led and owned by business teams. Define important terms like “customer”, “margin”, and “efficiency” and then strictly govern these definitions. Ideally a business glossary would be in place before embarking on any data initiative, but the data initiative can be an opportune vehicle to create a business glossary.

This common language builds consistency and trust and does away with meeting room arguments about whose numbers are right.

**Step 3: Make Learning Practical and Pervasive**

Attending a single training course doesn't create lasting skills. Learning needs to be continuous and integrated into the organisation’s way of working.

Training needs to extend beyond watching a few generic online videos. Training teams, delivery teams, and business teams can run show-and-tells, sharing how they use data to solve problems. Data coaches (usually in the form of analysts) can be embedded within business teams to provide support. Persona-specific workshops can be run as hands-on sessions using the tools and data that people are expected to use every day.

This approach builds a culture of continuous improvement and shared learning and makes the acquisition of data skills a habit rather than a once-off event.

**Step 4: Champion and Celebrate Success**

A successful change in culture needs visible champions which leads to positive reinforcement.

Early on in your data journey the organisation needs to identify and empower data champions. These should be enthusiastic colleagues at all levels in the organisation. This approach can be particularly successful when these individuals are well-respected (note: this has nothing to do with seniority in the business).

When a team achieves a successful outcome, for example, improving a process or spotting a new opportunity, celebrate this publicly. The earlier on in the data journey, the better. Don’t leave this to chance, plan for it up front as part of your change management plan.

Public recognition makes data fluency an aspirational capability. “I want some of what they've got!”

Step 5: Provide the Right Tools and Space to Experiment

People cannot become data literate without getting their hands on both the data (in a business-friendly format) and some user-friendly tools.

Self-service reporting, analytics, and visualisation tools need to support the personas you identified in step 1. Try to step away from vendor hype so that you can choose the tools and technologies that will best support the organisation’s use cases and people.

Most importantly though, provide the space to experiment. Something like a “sandbox” - a safe space with non-critical data where people can explore and experiment without the fear of breaking something.

This will help people get started and encourage curiosity and experimentation, both of which are key to a data-driven culture.

**Conclusion**

Building a data literate organisation is not about turning everyone into a data scientist. It is about helping every person in the organisation move from simply looking at data, to asking critical questions. This then leads to challenging assumptions and making more informed decisions.

How do you know when you are successful? When the conversation moves from “what does this data say?” to “What does this mean for the business?”.]]></content:encoded>
      <pubDate>Sat, 26 Jul 2025 00:00:00 GMT</pubDate>
      <category>Data Literacy</category>
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      <title>From Cost Centre to Value Driver: Quantifying Data ROI</title>
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      <description>Introduction: The Persistent Perception Problem Like many business functions, Data and Analytics capabilities are seen as a cost-centre. A necessary cost,...</description>
      <content:encoded><![CDATA[**Introduction: The Persistent Perception Problem**

Like many business functions, Data and Analytics capabilities are seen as a cost-centre. A necessary cost, rather than a strategic investment. Along with this, many organisations perceive their data as being merely a by-product of their business processes.

In the context of this, many business leaders, especially data leaders, are under increasing pressure to demonstrate tangible business value and return on investment (ROI) from their data assets. Factor in the growing appetite for “doing AI” and organisations really do need to articulate the business value of data and AI initiatives.

In this article, I am going to provide a framework for quantifying the value, and subsequent ROI, of data and data-related initiatives, including how to effectively communicate this value to the business.

**Why Quantifying Data ROI is Crucial (and Challenging)**

Historically, when asked to describe the business value of technology (and data) investments, the answers were always abstract, along the lines of “we can make better and quicker decisions”.

As data landscapes become more complex, building data solutions becomes more expensive and business benefits that are only anecdotally described are not going to make a good business case. This becomes even more challenging when trying to describe the value added by foundational activities such as data engineering and data governance.

It is therefore imperative that the business insights that are served to business stakeholders as well as the foundational data work be mapped to very specific and measurable business outcomes.

The obvious, positive business outcomes that organisations seek are revenue growth and cost reduction, but we must not lose sight of other benefits such as risk reduction/avoidance or measurable efficiency gains. Once these can be clearly identified and explained, executive buy-in and budget allocation becomes easier. More important than this though, the strategic importance of a data function or capability becomes apparent.

The next section lays out a framework that can be used to help articulate this value.

**A Simple Framework for Measuring Data Value**

Whilst a little more time consuming, I recommend taking a top-down approach that involves first understanding exactly what the organisational strategy and goals are. Alongside this, it is key that there is an agreed set of objectives that will support any business case or funding request. As mentioned in an earlier section, a good start is:

- Revenue generation or growth. Any data initiative that achieves this outcome by enabling things like new product development, more targeted marketing, improved sales effectiveness, or new market opportunities
- Cost reduction or efficiency gains. This would be any initiative that results in process improvements, introduction of automation, or reduced operational friction
- Risk mitigation or avoidance. A data initiative would have to improve compliance (avoid fines), enhance security, or support better forecasting
- Strategic enablement. Any data initiative that is core to the organisation’s existence, for example, the ability of a financial services organisation to retain a banking licence or a pharmaceutical company to retain its manufacturer’s licence and wholesale distribution authorisation

Next, consider the organisation’s strategic goals or objectives. To bring this to life, we'll use Lloyds Banking Group as a real-world example. Their 2025/26 strategy contains for core objectives (or pillars):

- Deepen and innovate in Consumer, measured by a three percent increase in depth of relationship and an increase of about 50 percent of active customers served per distribution full time equivalents.
- Create a new Mass Affluent offering, measured by a more than 10 percent increase in Mass Affluent total relationship balances, including assets under administration.
- Digitise and diversify our BCB business, measured by maintaining small business deposit market share and by digitising 50 percent of key servicing interactions
- Develop our Corporate and Institutional business, measured by an approximate 45 percent increase in CIB other operating income and a greater than 5.25 percent increase in income over average risk-weighted assets

At this point in time, it is already evident that Lloyds Banking Group will need the relevant data to highlight how the organisation is progressing against achievement of the stated targets.

Unfortunately, this in itself, does not tell us anything about the value of the data needed. Nor does it help us understand how data will help Lloyds achieve its strategic objectives. To do this, we need to delve a little deeper. Lets consider how Lloyds would “Deepen and innovate in Consumer”. To achieve the stated goal of a three percent increase in Depth of Relationship (defined as product holding for customers retained since 2024) , certain activities need to take place, either projects or business as usual processes, usually a combination of the two. This might look something like:

- Determine what success looks like (i.e. is a three percent increase in depth of relationship reflected by a cross-sell ration uplift in absolute product numbers, a three percent increase in interest income from those customers, a three percent increase in the lending book, or something else)
- Understand who the customer is, so that it is known which products they hold
- Identify customers that present a cross-sell opportunity (i.e. credit card, short-term loan, ISA account, etc.)
- Determine and execute the marketing and sales initiatives to achieve this cross-sell
- Measure the progress and outcome
- Adjust actions where necessary

Lets imagine, to achieve the ambition of successfully delivering the above, a requirement is building a single customer view and segmentation model. Using the success metrics (e.g. a three percent increase in interest income) vs. the cost of building and maintaining a single customer view and segmentation, Lloyds can calculate what a likely return is on their data investment. This should, of course, be done using a robust return on investment model, considering all the cost factors and time value of money.

Whilst this is a simplistic example, because a single customer view would have other benefits (both tangible and intangible) supporting other business initiatives, it illustrates how tangible outcomes and return on investment can be assigned to data projects and investments.

Most importantly, it ensures that data investments are made in support of achieving the organisation's strategy, and not because analysts or peers are saying that every organisation needs a single customer view.

It would be remiss of me not to highlight that simply delivering a data programme, project, or initiative will not ensure success. Organisations need to ensure that the quality of the delivered (data) asset is of a high standard and able to support the intended strategic initiative and that the necessary effort is invested in the organisational change efforts required to drive adoption. We’ll cover this in the next section.

**Practical Steps and Metrics**

Not to be confused with the business metrics discussed in the previous section, you should next seek to define some metrics that can be used to measure how well your data is supporting the organisational objective you have identified.

As with business metrics, you need to ensure that you are selecting the right data metrics, ideally balancing leading and lagging indicators. For example, usage metrics would be lagging indicators in that they describe an event that has already happened. In contrast, something like data quality metrics would be leading indicators inferring that poor data quality would reduce the quality of decisions made or decrease trust in the data.

Some ideas for these types of metrics are:

- Data quality metrics such as data accuracy, completeness, timeliness, consistency, and issue resolution or remediation times
- Data management and operations metrics such as availability, uptime, and performance
- Data governance metrics such as critical data elements curated and governed, data stewardship, and data ownership
- Data cost reduction metrics such as the reduction in spend on legacy applications and infrastructure

It is important that each of these metrics are baselined so that progress can be tracked. The metrics should then be presented in data value scorecards or dashboards and shared with relevant stakeholders.

**Communicating Value Effectively to Stakeholders**

Instead of just publishing the data value scorecards or dashboards, stakeholders should be actively engaged and encouraged to understand the significance of the value added by the organisation’s data. This messaging should be tailored to different audiences such as Finance, the C-suite, and business unit leaders. Stay away from technical jargon and use this approach to build a narrative around data as a strategic asset and value driver.

Don’t wait until your data programme is complete (not that they ever are!) but identify a quick win that you will focus on before even starting your programme. Once this goal has been achieved, celebrate it and demonstrate it to stakeholders.

**Conclusion: Making the Shift**

Being able to demonstrate ROI is key to elevating the organisation’s data capability from a cost centre to a strategic investment. Remind stakeholders that linking data efforts to business outcomes needs to remain deliberate and concerted.

I would love to hear about your own challenges or successes in quantifying the value of your data so feel free to leave a comment or message me directly.]]></content:encoded>
      <pubDate>Tue, 17 Jun 2025 00:00:00 GMT</pubDate>
      <category>Data Strategy</category>
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      <title>The Business Analyst&#39;s Dilemma</title>
      <link>https://umlautconsulting.co.uk/blog/the-business-analysts-dilemma</link>
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      <description>Bridging the Gap Between Business Needs and Data Products “That means absolutely nothing to me! It is just a throwaway comment!” the General Counsel for a...</description>
      <content:encoded><![CDATA[*Bridging the Gap Between Business Needs and Data Products*

“That means absolutely nothing to me! It is just a throwaway comment!” the General Counsel for a large telecommunications organisation, let's call him Gary, boomed across his desk at a colleague and me. We were working on a process optimisation project, and Gary had been identified as one of our key stakeholders. Gary had mentioned an important element of his job, we duly documented this, and my colleague replied, “Noted”. This set Gary off on a tirade about how the term “noted” had no legal significance, nor did it commit us to taking on board his requirement. Clearly a miscommunication, when we were all actually aligned.

Business Analysts are the important bridge between technical teams and business stakeholders in data projects. They translate complex business needs into technical specifications while making technical constraints understandable to non-technical colleagues or clients. A skilled BA doesn't just collect requirements – they uncover underlying business problems and ensure the final products deliver genuine value. In my experience, the success of data and AI projects often hinges on having someone who speaks both languages fluently and keeps everyone focused on outcomes rather than features.

In the example above, whilst functionally doing things right, we had failed on the communications aspect.

My career as a Business Analyst, working with Business Analysts, building teams and practices, and training, coaching, and mentoring Business Analysts, has taught me so many things.

If I were to retain the skills from only a single lesson, the lesson would be this: The ability to effectively translate between technical and business languages is a core skill that drives successful outcomes and sets not only Business Analysts but anyone up for a long and successful career.


**The Communication Challenge**

The biggest disconnect I see between the technical and business domains can be summarised as the difference between features and benefits. Technologists tend to talk about features, and business stakeholders understand benefits. The quote from the 1967 film, Cool Hand Luke, says it all: “What we have here is failure to communicate”.

A real-world example of this was the Apple iPod. The iPod was not the first or only MP3 player on the market, but whilst competitors, such as the Creative Zen Mozaic, led with messages like “16GB, 1.8” TFT Display, 8EQ settings”, Apple simply stated “1,000 songs in your pocket”. Features versus benefits.

Sometimes the divide is even simpler than this. I remember having a robust discussion with a report developer who refused to include the client’s logo in the initial version of a report because “It was not important and could be easily added later”. The client didn’t think it was unimportant.

These are all examples of poor translation between the technical and business domains. Factor in enough of these seemingly “unimportant” elements and you are jeopardising the successful outcome of a data project. Missed benefits, unused solutions, and white elephants.

**Practical Techniques**

So, how do we ensure that as Business Analysis, we help our business, our clients, and our teams through these challenges? Here are three key techniques that, as a Business Analyst, need to be mastered.

***Develop a Business-first Mindset***

It is so easy to be distracted and directed by technology, and this remains a challenge for so many data projects and programmes. Yes, the shiny new things are fun to play with, prototype, and test. However, one needs to recognise that technology should always remain an enabler and not a driver of change.

Every data project or initiative that is undertaken should have a clear business purpose, desired outcome, and measurable benefits.

Are you thinking of migrating your data lake from Microsoft Azure and AWS? Or vice-versa? Why? Because one technology has cooler features than the other? Wrong decision! Instead, ask questions like:

- Once migrated, will cloud consumption costs be reduced?
- What will the migration cost be?
- What will the internal upskilling cost be?
- How long will we need to run the ‘legacy’ solution?
- All things considered, how long will it take before the business recognises a net positive return?

***Be Curious***

Curiosity killed that cat, so the saying goes. The full saying, though, is “Curiosity killed the cat, but satisfaction brought it back”. Some people are naturally curious, and this is a powerful trait for Business Analysts.

As with any skill, this can be honed and the simplest way of doing this is by asking questions, specifically ‘Why?’. Don’t stop after asking it once. Keep asking it, to the point that people might begin to think you are stupid.

Bringing this level of curiosity to your business analysis role will help you unpick the difference between the business’s wants and the business’s needs. And importantly, clearly articulate this to your business stakeholders, which also supports the next point.

***Build a Shared Understanding***

Business is, as life is, constantly managing unlimited needs and limited resources. Building a shared understanding and consensus within the business is key to achieving success. This is where a Business Analyst needs to leverage their communication skills. Spoken, written, and visual.

**Building Your Skills**

Progressing your career as a Business Analyst will involve growing a set of functional skills. The key ones to focus on are:

- The ability to use workshops, interviews, and observation to elicit, understand, and prioritise business requirements
- The ability to analyse and interpret (often disparate sets of) data
- Process modelling skills to document current processes and to define improved or new business processes
- Excellent writing skills to produce high-quality, non-ambiguous documentation
- Visualisation skills that can represent complex concepts in an easy-to-understand manner, using both structured and unstructured data

In addition to your functional skills, you will also need to grow your soft skills. The important ones to consider are:

- Active listening being the ability to fully concentrate, understand, respond, and remember what stakeholders are saying
- Communication and translation by explaining complex concepts simply and adapting your communication style to different audiences
- Critical thinking to objectively evaluate information, question assumptions, and identify the real problems behind stated issues
- Stakeholder management for building relationships, managing expectations, and navigating organisational politics (yes, everyone has to!)
- Adaptability and resilience to adjust to changing requirements and to persist in the face of challenges or resistance

Leveraging functional skills, soft skills, and domain knowledge will allow you to develop credibility with not only your business stakeholders but also your technical counterparts.

**Conclusion**

Being an effective translator between the business and data worlds is an invaluable skill to both yourself and the organisations that you can help. It really is a role to embrace and one that is very sought after.

Where to start? Begin by performing a skills assessment on yourself and looking for gaps. There are myriad resources out there to help you address these gaps and develop your functional and soft skills, but the number one step you can take is to find a project or a role where you can be exposed to the business end of data. For me, it was learning and implementing ERP software in the 1990s. Given the vastness of the data and technology world today, it could be anything for you.


]]></content:encoded>
      <pubDate>Sat, 29 Mar 2025 00:00:00 GMT</pubDate>
      <category>Data Strategy</category>
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      <title>Protecting Trust: Why Data Governance is Non-Negotiable</title>
      <link>https://umlautconsulting.co.uk/blog/protecting-trust-why-data-governance-is-non-negotiable</link>
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      <description>Yesterday I received an email from a professional event photography business that my son&#39;s previous school and sports club has used for event photographs. As...</description>
      <content:encoded><![CDATA[Yesterday I received an email from a professional event photography business that my son's previous school and sports club has used for event photographs. As soon as I saw it was from the organisation's Data Protection Officer, I knew what it was going to contain. News of yet another data breach, involving my personal data. Name, address, email, and phone number. And of course a warning, "there is a potential risk of identity theft or other fraudulent activities. We strongly recommend that you remain vigilant and monitor your personal accounts for any suspicious activity". There you have it, due to a failing in their processes and systems, the onus is now on me to mitigate the risks that result from this breach. Of course, this is something that I already do but it poses the question "how seriously can customers take an organisation that cannot get the basics of data protection right?".

Consumers are becoming a lot more informed and prescriptive when it comes to the use of their data. Aside from the potential fines from regulators, organisations really need to get their acts together when it comes to the management of personal data, and data as a whole. It is therefore imperative that these organisations invest the necessary effort to ensure that they have a robust data governance capability in place, and this needs to be foundational to the organisation's data strategy. Get this wrong and organisations will expose themselves to a host of vulnerabilities and reputational damage as well as eroding customer trust.

Data governance has historically been a bit of a bland topic but has fast become a core capability that organisations need to develop. This is even more pressing with the growing adoption of AI and the multitude of use cases of the technology unlocks.

In addition to being one of the least sexy aspects data, data governance can also be a little nebulous. So what do I mean when I say that organisations need to build this capability? In summary, they need to ensure that a few key principles are in place by:

Ensuring clear ownership and accountability for data
Ensuring compliance with regulations like GDPR, CCPA, and of course the upcoming EU AI Act
Performing regular audits and risk assessments to identify weaknesses and improvement opportunities
Empowering their staff improve their data literacy and helping them understand the importance of good data governance
Given the importance of strong data governance, why do so many organisations find themselves in such a difficult place? I attribute this to three key factors:

The pace of business and technological innovation has outstripped the pace of data management and data governance efforts and organisations have now built up a serious level of "data governance debt"
Because data governance can be rather nebulous, organisations just don't know where to start
Organisations ignore the significant organisational change management element needed to successfully achieve a good data governance standard
None of these issues are insurmountable and organisations should begin with the basics. Simply put, get started by expanding the key principles set out earlier in this article. This sets out your intention for data governance and the scope that you wish to include, such as data quality, data security, compliance, etc. It is also an excellent prompt to begin the organisational change management component of your data governance initiative.

The principles that you have defined should then drive the policies that you need to create and adopt. Start with a single, overarching data governance policy. This should cover, at minimum, the following content:

- The policy purpose and scope
- The data governance principles that have previously been identified
- The broad roles and responsibilities (in the organisation) for data governance and good data management
- Data access and usage rules (especially considering AI use cases)
- Data quality and standards
- Data security and privacy
- Compliance and regulatory requirements
- High-level data management procedures
- Performance metrics to help track the progress of the data governance initiative
- Specification on how the data governance policy will be reviewed and updated on a regular basis

Whilst there is rather a lot here, don't overthink things. Not all the sections of the data governance policy need to be fully fleshed out for your first version. Some sections may even be placeholders. The idea is to use the policy to begin building consensus and getting the organisation onboard.

The initial policy can then be evolved and elements can be broken out into supporting policies. For example, you may want to create a data quality policy that sets out more detail on how data quality will be managed and improved. The policy can take a similar form to the overarching data governance policy but of course will be specific to data quality and significantly expand on sections such as data quality standards and metrics.

Once your data governance policy has set up the guardrails, you can begin to focus on the next level of detail such as processes and procedures that will enable your data governance policy, along with the roles and responsibilities that will be needed.

Of course, do not forgot the required investment in communications and learning and development as part of your organisational change management effort.

There is of course a lot of supporting detail that will progress your data governance initiative and I will cover these topics in other articles. In the meantime, please feel free to use this guide to take the first step on your data governance journey]]></content:encoded>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
      <category>Data Governance</category>
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      <title>Setting Up a Data Literacy Programme</title>
      <link>https://umlautconsulting.co.uk/blog/setting-up-a-data-literacy-programme</link>
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      <description>Establishing a successful Data Literacy Program requires careful planning and execution. The first step is to clearly define your objectives. Are you aiming...</description>
      <content:encoded><![CDATA[Establishing a successful Data Literacy Program requires careful planning and execution. The first step is to clearly define your objectives. Are you aiming for a basic level of data literacy across the organisation, or do you have more focused goals like building an internal data team? Having a clear vision will guide your program's direction and intended outcomes.

Next, conduct a thorough stakeholder analysis to identify groups and individuals who will be impacted by your data literacy initiative. This analysis will help you understand the scope of your program and its potential effects on various parts of the organisation.

With your stakeholders identified, determine your 'to-be' state. This involves defining what success looks like for your program. Consider organisational capabilities, individual skills, processes, and even technical capabilities. Ideally, your vision should encompass people, processes, and technology.

Once you've outlined your desired future state, assess your current situation. Build on your stakeholder analysis to understand the existing data literacy capabilities within your organisation, focusing primarily on people and processes, with technology as a secondary consideration.

By comparing your current state to your desired future state, you'll identify the gap that your Data Literacy Program needs to bridge. This gap analysis will inform your impact assessment, helping you understand how the proposed changes will affect your organisation and its people.

Developing a robust communication strategy is crucial for keeping people engaged and educated throughout the program. Identify effective communication channels within your organisation, such as intranets, emails, posters, or town halls. Importantly, ensure that communication is two-way, allowing for feedback and dialogue.

Based on your impact analysis, determine the learning and development requirements for your program. This could include online courses, bespoke in-person training, or other activities designed to upskill your workforce and support their data literacy journey.

Identify champions and sponsors within your organisation who will lead the change. These individuals need to be visible advocates for data literacy, demonstrating senior leadership support. Provide them with the necessary coaching and support to effectively lead the change.

Finally, develop a resistance management approach. People often resist change due to lack of awareness, lack of skills, or simple unwillingness. Address these issues through your communication strategy, learning and development plan, and targeted engagement activities like coaching sessions, town halls, lunch and learns, or hackathons.

With these elements in place, you'll have a solid foundation for a robust Data Literacy Program. This comprehensive approach will allow you to plan and deliver a program that brings significant benefits to both your organisation and its people, fostering a data-driven culture and enhancing decision-making capabilities across the board.]]></content:encoded>
      <pubDate>Wed, 11 Sep 2024 00:00:00 GMT</pubDate>
      <category>Data Literacy </category>
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      <title>Why Data Matters</title>
      <link>https://umlautconsulting.co.uk/blog/why-data-matters</link>
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      <description>In my last article, &#39;The Evolution of Workplace Skills,&#39; I talked a lot about bringing people along on the journey of developing data literacy in the...</description>
      <content:encoded><![CDATA[In my last article, 'The Evolution of Workplace Skills,' I talked a lot about bringing people along on the journey of developing data literacy in the organisation. But that discussion was based on the assumption that everyone is eager to join in on this journey. As many of us have experienced, this isn't always the case. When it comes to data and data initiatives, people often question the value of our efforts. They ask, 'So what? Why is this important?' Some even admit they don't understand what we mean by data or how they use it in their daily work. These are common challenges you'll encounter when trying to emphasise the importance of data in organisational decision-making.

In this article, I'm going to discuss an approach to explain why data is vital and what it means in the broader context of the organisation. Let's start at the highest level: strategy. Whether you call it organisational or corporate strategy, we all understand it's the direction and end goal that we set for our organisations. To implement strategy, it needs to be broken down into specific, strategic business goals. Some may refer to these as OKRs (Objectives and Key Results), but I'm a bit old school and prefer the Kaplan and Norton balanced scorecard approach to implementing and measuring strategy.

Breaking down strategy into business goals, the first set of goals is generally financial goals. Let's be honest, if you're in business, making money is crucial for staying in business, even for non-profits. Achieving these financial goals requires a focus on customer-related activities because that’s where you generate revenue and provide value to customers. Once you understand your goals around your customer, you'll look at internal processes - which internal capabilities and processes are needed to deliver your promises to the customer.

Lastly, to deliver on these internal processes, you'll need to establish learning and growth objectives. This involves skilling up your people and defining roles and responsibilities to support those internal processes.

This approach takes you from high-level strategy to implementable strategic business goals, leading to various initiatives or projects generally categorised into process, people, and technology. It's at this stage you might encounter your first resistance to change, necessitating business change enablement, which I'll cover in a subsequent write-up.

Once your people, processes, and technology enablers are in place, they dictate how your business operates and delivers its value chain. This could be transforming raw materials into sellable products or leveraging skilled personnel to deliver consulting services. Regardless of the business model, it will be delivered through your operational processes. And yes, you'll face pushback here too, requiring further change enablement.

As your operations run, data is produced from systems like ERP and CRM. Contrary to the belief that data is merely a byproduct, it's an asset. This data includes invoices processed to customers, invoices received from suppliers, general ledger entries, CRM interactions, HR data, and more. These data points become your actuals when it comes to reporting and analysis.

Throughout this process, you'll also engage in planning, setting targets, and forecasting. You’ll then measure actual data against these targets, which can include financial goals, customer metrics, internal process benchmarks, and learning and growth targets. This helps you understand your performance and whether you're on track to deliver your strategy. If not, adjustments can be made, and this cycle continues, feeding back into your strategic planning.

So, in summary, data is crucial because it forms the feedback loop necessary for tracking and evaluating whether your strategic goals are being met. Recognising this is the first step in understanding and communicating why data is important for organisations. In future articles, I'll dive deeper into how to bring this data to life, enact change enablement strategies, and other essential aspects of data management.]]></content:encoded>
      <pubDate>Sun, 16 Jun 2024 00:00:00 GMT</pubDate>
      <category>Data Strategy</category>
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      <title>The Evolution of Workplace Skills</title>
      <link>https://umlautconsulting.co.uk/blog/evolution-of-workplace-skills</link>
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      <description>As our workplaces and organisations have evolved over the years, so too have the skills required to thrive in these environments. If we rewind to the...</description>
      <content:encoded><![CDATA[As our workplaces and organisations have evolved over the years, so too have the skills required to thrive in these environments. If we rewind to the Industrial Revolution, we witness a monumental shift as Industry 1.0 introduced automation to manufacturing. Workers had to transition from their existing roles and learn to operate new machinery. Fast forward to the late 80s and early 90s, and another tech revolution occurred with the introduction of personal computers.

Personal computers required a whole new set of skills that workers needed to master. Besides their core professional skills - whether they were accountants, statisticians, or procurement officers - employees had to become computer literate. Learning to operate a computer, transitioning from using keyboards to navigating with a mouse, and adapting to graphical user interfaces were all essential skills. They also had to get proficient in applications like Lotus 1-2-3, early versions of Microsoft Excel, word processors, and presentation software like PowerPoint. Job adverts once explicitly asked for Microsoft Office proficiency; today, such skills are assumed.

Now, we're on the brink of another major transformation with the rise of Artificial Intelligence, but let’s park that topic for now. Instead, let's focus on a vital skillset that's essential today: data literacy. We're at a stage where progressing in many careers will be difficult without a reasonable level of data literacy. In this series of articles, I'll explore how organisations can help their staff develop data skills and how individuals can enhance their own capabilities, thereby improving the organisation itself.

Understanding the importance of data literacy is crucial for organisations. Some businesses still haven't realised how transformative data can be. The journey begins with acknowledging the significance of data and how it can enhance business operations. Organisations need to build data capabilities and bring their people along this journey, to help their organisations meet their strategic objectives.

This series will delve into various aspects of data literacy and how to develop it in the organisation. Future posts will cover topics such as building a data-driven culture within organisations, developing data skills for non-data professionals, ethical considerations in data usage, and, of course, data governance. We'll also discuss data storytelling, visualisations, and ultimately how AI relies on robust data capabilities, to add value to an organisation.

]]></content:encoded>
      <pubDate>Sat, 08 Jun 2024 00:00:00 GMT</pubDate>
      <category>Data Literacy</category>
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