Data is the most undervalued asset in most organizations across EMEA. Not because they lack it, but because they don’t manage it with the same strategic intent they apply to finance, operations, or people.

That’s where I come in.
With 25+ years of experience across Europe, the Middle East, and Africa, I help businesses turn fragmented, unreliable, and siloed data into a trusted, strategic asset. My focus is clear: align data initiatives with business goals, deliver measurable impact, and enable sustainable data maturity.
I partner with senior data leaders, CIOs, and transformation teams to:
- Design and implement business-aligned Data Governance frameworks
- Define and operationalize Data Strategy that drives value
- Lead enterprise-scale Master Data Management (MDM) initiatives
- Improve Data Quality at the source with practical, rule-based approaches
- Build internal capability through workshops, keynotes, and advisory support

I’ve delivered projects across industries, from financial services and telcos to public sector and FMCG, and I understand the local and regulatory complexities that shape data work across EMEA.

If your organization is ready to stop firefighting and start building a long-term, business-driven data foundation, let’s connect.

📬 contacts@josealmeidadc.com | 🌐 www.josealmeidadc.com

Misaligned goals. Undefined ownership. Silent assumptions about what “good” looks like. Decisions made without the people who actually feel the impact.

By the time these issues show up in dashboards, pipelines, or AI models, it’s already too late, the damage is baked in.

Data governance isn’t failing because it’s complicated.
It’s failing because organizations try to fix symptoms instead of causes.

If you want governance that works, shift the focus:
-        Start with outcomes, not tables. What decision are we enabling? What risk are we reducing?
-        Define ownership upfront. If everyone touches the data, someone must be accountable for it.
-        Align on meaning. Agree on what “customer,” “revenue,” or “risk” mean before building anything.
-        Bring business to the table early. Governance can’t be effective if the people who use the data aren’t shaping the rules.

When these foundations are in place, everything else, quality, trust, compliance, AI, becomes easier to achieve.

And when they aren’t? All the tools in the world can’t save you.
Start with clarity.
Start with people.
Start with purpose.

Jose Almeida

"Transforming Your Data for Business Success | Your Data Strategy Partner"

With over two decades of expertise, I've honed my craft in Data Management to empower businesses like yours. I specialize in Data Governance, Data Quality, Master Data Management, ETL, Data Migration, and Data Integration, with a track record of success across diverse industries in Europe, the Middle East, and Africa.

I'm not just a Data Consultant and Advisor; I'm your partner in achieving data-driven excellence. My mission is crystal clear: to help you harness the full potential of your data assets, tackle challenges head-on, and propel your business towards success.

Here's how I can make a difference for you:

📊 Data Governance: I build robust frameworks, policies, and compliance measures that supercharge your data operations and drive smart decision-making.

🚀 Data Strategy: I craft tailored data strategies that align with your business objectives, optimizing data management and utilization.

🌐 Master Data Management: I implement MDM programs that provide a single, accurate view of critical data across your entire organization.

🎯 Data Quality: I ensure your data is a trusted asset by rigorously cleansing, validating, and monitoring it.

📚 Data Catalogue and Metadata Management: I enable seamless data discovery and understanding through comprehensive catalogs and metadata management.

🗣 Workshops & Keynotes: I educate and inspire your team with workshops and keynotes, promoting data management best practices.

💡 Pre-Sales Support: I offer technical expertise during pre-sales activities, designing effective data management solutions tailored to your needs.

🌟 Market Offer Creation Support: Together, we create compelling market offerings that unlock the untapped potential of your data.

I'm here for forward-thinking professionals like you who understand the game-changing power of data management. Let's connect, and together, we'll optimize your data landscape for remarkable business outcomes.

Ready to embark on a journey to data-driven excellence? Reach out to me for consultations and advisory services, and let's unlock your data's true potential.

Website: www.josealmeidadc.com
Email: contacts@josealmeidadc.com

Data Strategy & Governance | Driving Business Value Across EMEA | Master Data Management, Data Quality | Advisor | Speaker | ‘Data Foundation’ Newsletter

Daniel Esteban Vesga

You can invest in the best platforms, hire top data scientists, and build beautiful dashboards but if your data isn’t governed, trusted, or owned, none of it matters.

Governance isn’t bureaucracy. It’s how you make sure:
-       The data feeding your decisions is accurate.
-       The reports on your desk tell the same story.
-       The AI models you deploy don’t backfire.
It’s the difference between “data available” and “data usable.”

When governance is missing, chaos doesn’t show up as broken pipelines, it shows up as bad decisions, lost trust, and missed targets.

So before chasing new tools or trends, start with a question:
“Do we actually trust the data driving our business today?”

If the answer is anything less than a confident yes, you don’t need another platform.
You need governance that works.

Data governance isn’t about control. It’s about confidence.

If your organization is still debating whether governance is “worth it,” here are ten reasons why the best-run companies make it a priority:
1.     Better decisions. Governance builds trust in the numbers - no more “which report is right?” debates.
2.     Regulatory compliance. Data laws are tightening everywhere. Governance keeps you ready and defensible.
3.     Risk management. Protects your organization from privacy breaches, reputational damage, and costly errors.
4.     Operational efficiency. Cuts duplication, rework, and time wasted fixing bad data.
5.     Data quality. Defines what “good” looks like and keeps it consistent across the enterprise.
6.     Business-IT alignment. Creates shared accountability, so tech and business finally speak the same language.
7.     AI readiness. No governance, no trustworthy AI - it’s that simple.
8.     Clear ownership. Everyone knows who owns what data, and why it matters.
9.     Scalability. Keeps data manageable as systems, teams, and regions grow.
10. Business value. Turns data from a byproduct into a real strategic asset.
Governance is not paperwork.

It’s the system that ensures your data, and your business, can scale safely, intelligently, and with impact.

New leadership → new priorities.
New tool → new promises.
New project → new excitement.

And then? Twelve months later, everything is rebuilt, renamed, or rebooted again.

Consistent progress beats constant reinvention.
When data strategy becomes a yearly reset, it’s a sign of deeper issues:
-        No clear business outcomes
-        No stable ownership
-        No governance that survives org changes
-        No measurement of value
-        No cultural commitment

A real data strategy has staying power. It outlives reorgs, tools, and personalities because it’s anchored in:
-        Strategic goals
-        Shared definitions
-        Cross-functional accountability
-        Repeatable processes
-        Business ownership, not IT heroics

If your data work feels like déjà vu every January, you don’t need a new project.
You need alignment.
You need clarity.
You need governance that doesn’t evaporate when priorities shift.
Because organizations don’t win with the newest plan, they win with the plan they execute consistently.

And trust issues are expensive. They slow decisions. They multiply meetings. They create parallel spreadsheets and shadow processes. They drain confidence from every conversation.

Trusted data doesn’t need defending. It speaks for itself.
When trust is low, the symptoms are obvious:
-        Leaders ask for “one more cut” of the same report
-        Teams reconcile the same KPIs every month
-        Everyone debates definitions instead of decisions
-        Nobody knows which version is real
-        And AI initiatives stall before they start

When trust is high, something remarkable happens:
The business stops checking, and starts deciding.

-       That shift doesn’t come from a new dashboard.
It comes from:
-       Clear ownership
-       Shared definitions
-       Transparent lineage
-       Consistent quality checks
-       Governance that prevents problems at the source

Data trust isn’t built through presentations. It’s built through discipline, small, consistent signals that say:
“This data is reliable. You can use it.”

If your organisation is stuck in endless validation cycles, don’t add more reports. Build more trust. Because without trust, no data strategy can survive.

And data debt behaves like any other debt: It compounds. It spreads. And eventually, it becomes the reason everything takes longer, costs more, and delivers less.

You see it in the daily grind:
-        Repeated reconciliations
-        Manual tweaks to make dashboards “look right”
-        Shadow spreadsheets appearing everywhere
-        AI pilots that stall because the inputs don’t make sense
-        Teams asking for the same fixes month after month
None of these are isolated issues. They’re all interest payments on ignored data problems.

Data debt isn’t created by bad tools,  it’s created by avoided decisions.
Avoided ownership.
Avoided definitions.
Avoided governance.
Avoided prioritization.
The longer you wait, the more expensive it becomes. Not just in dollars, but in trust, speed, and competitiveness.
The cure isn’t another platform.
It’s discipline:
-        Define ownership
-        Set standards
-        Fix root causes, not symptoms
-        Tie every data effort to a measurable business outcome
Data debt doesn’t magically go away when the next transformation program starts.
It only disappears when the organization decides to stop carrying it forward.
Because in the end, companies don’t fail because of data issues. They fail because they pretend those issues don’t matter until they do.

Bad data doesn’t happen by accident.

It’s the result of unclear ownership, broken processes, and business rules that live in people’s heads instead of systems.

Every hour spent “fixing” data is an hour not spent driving value.

The real fix is simple but not easy:
-       Define who owns the data at the point of creation
-       Build quality checks into business processes, not after them
-       Make data standards practical, visible, and enforced
-       Prioritise the data that powers critical decisions
-       Track improvement in business KPIs not technical dashboards

Data quality isn’t about being perfect. It’s about being reliable enough to run the business with confidence.

If your teams are constantly firefighting, it’s time to stop asking “how do we clean faster?” Start asking “why do we keep producing bad data in the first place?”

So many teams are held together by a handful of brilliant people manually fixing issues, reconciling numbers, chasing down definitions, and stitching processes together at the last minute.

It works… until it doesn’t.
Someone leaves.
A system changes.
A new regulation hits.
Suddenly, the whole operation wobbles.

If your data success depends on individuals, not systems, you’re one resignation away from chaos.
What to do instead:
-        Document the rules. Don’t let tribal knowledge stay tribal.
-        Assign real ownership. Not vague responsibility, actual names.
-        Automate the repeatable. Free people from firefighting to focus on outcomes.
-        Establish quality checkpoints. Not after the fact, but at the source.
-        Make governance a practice, not a project.

Strong data foundations aren’t built by heroes.
They’re built by consistent, repeatable habits that survive turnover, tech changes, and business pressure.
If you want resilience, durability, and trust in your data, stop relying on heroics. Start building habits.

I came across an excellent piece by Ananth P. and one concept hit especially hard.

“This is data obesity: the condition where an organization accumulates data faster than it can derive value from it. It’s not a storage problem. It’s a metabolic one.”

That line says it all. Most organizations don’t suffer from too little data. They suffer from too much data they don’t understand, don’t trust, and don’t use.

Like any form of “obesity,” the symptoms show up everywhere:
-        Slower decisions
-        Heavier processes
-        Bloated pipelines
-        Higher costs
-        Confused teams
-        And AI projects that stall before they start

Because the issue isn’t collection. It’s conversion, turning raw data into usable, trusted, meaningful inputs for the business.
This is where governance, ownership, and strong data strategy matter most. Not to control data but to metabolize it.

Healthy data organizations don’t hoard everything “just in case.”
They curate.
They prioritize.
They assign ownership.
They set standards.
They connect data to outcomes.
They build trust in small, consistent steps.

If your company is drowning in dashboards, struggling with inconsistent definitions, or watching “data debt” grow, you’re not short on data, you’re short on metabolism.
The cure isn’t another tool. It’s discipline. It’s clarity. It’s governance that works in real life, not just on paper.
Data obesity is real. So is the opportunity to build a leaner, stronger, value-driven data culture.

Everyone loves the AI demos. The vision. The promises. The glossy roadmap with arrows pointing up and to the right.

Here’s the part nobody wants to talk about:
If your underlying data is inconsistent, untrusted, duplicated, or misaligned, your AI won’t deliver value, it will deliver surprises. And surprises in business are rarely good.

AI needs:
-        Clean inputs
-        Clear definitions
-        Reliable pipelines
-        Consistent standards
-        Transparent lineage
That’s not “nice to have.” It’s the foundation.

Without it, AI becomes:
-       A proof-of-concept graveyard
-       A never-ending tuning exercise
-       A model that looks smart but performs terribly
-       A risk, not a capability

With it, AI becomes:
-       Useful
-       Practical
-       Scalable
-       Trusted
The organizations winning with AI today aren’t the ones shouting the loudest. They’re the ones quietly doing the unglamorous work:
 Fixing quality. Defining ownership. Aligning the business. Strengthening governance.

You don’t need perfect data. You do need disciplined, trustworthy data.
Because the future of AI won’t be decided by who has the most models, 
 it will be decided by who has the cleanest inputs.

We’ve seen this play out in far too many organizations: The moment reports go wrong, fingers point at the data warehouse, “ETL’s broken.” “Pipeline failed.” “BI tool’s glitching.”, but the truth is usually simpler - no one owns the data.

Marketing owns “its” numbers. Finance has “their” version. Operations keeps a secret spreadsheet no one admits exists, and somehow, we expect the data warehouse to magically reconcile all that chaos.

A data warehouse can only reflect the discipline of the people feeding it.
If upstream data is messy, unowned, and ungoverned, no amount of SQL or Snowflake tuning will fix that.

The real fix starts outside IT:
-        Business leaders must own the quality and definition of their data.
-        Governance must clarify who’s accountable for accuracy and consistency.
-        Data strategy must link directly to business outcomes not just reporting efficiency.
Because:
-        Data strategy is business strategy. You can’t outsource accountability for truth.
-        Prioritize business value. Fix what drives decisions, not what’s easiest technically.
-        Start small, think big. Align one department’s data, prove the value, then scale.
-        Business in the driver’s seat. Data is a business asset, not an IT deliverable.
-        Measure and communicate impact. Track trust, adoption, and time-to-decision, that’s real ROI.

A data warehouse is a mirror. If you don’t like what it shows, fix the reflection, not the mirror.
 
#QuestionForGroup - When reports conflict, where do you see the real root cause, technical issues, or lack of business ownership?

We keep hearing the same thing:  “We have so much data, why aren’t we getting value from it?”

Because value doesn’t come from volume. It comes from focus.
Here’s the pattern:
-        Teams chase too many use cases at once
-        Leaders want dashboards “just in case”
-        AI pilots launch without a clear business owner
-        Requirements grow faster than capacity
-        Suddenly everyone is busy, but nothing moves the needle
 
If everything is a priority, nothing is.
The organisations that succeed with data and AI do one thing differently:
They pick fewer problems, the ones that actually matter, and solve them deeply.
They ask:
-        What’s the business outcome?
-        Who owns it?
-        How will we measure success?
-        What’s the minimum data needed?
-        What decision will change when this is solved?

When priorities are clear, the noise disappears.
People align. Governance becomes easier. Quality improves at the source.
The business starts seeing value, fast.
 
Great data strategy isn’t about doing more. It’s about doing the right things, in the right order, for the right reasons.

If your roadmap feels overwhelming, shrink it. Impact increases when focus sharpens.

Who really owns your data strategy? IT or the business?
This is one of the most common (and costly) mistakes I see: treating data strategy as a technical responsibility instead of a business one.

When IT owns the strategy, the focus becomes tools.
When the business owns it, the focus becomes outcomes.

That single difference determines whether your data effort drives value or just activity.

Every failing dashboard, every broken metric, every AI model that behaves strangely has a root cause that rarely gets discussed:
We assumed things we never checked.
We assumed everyone used the same definition of “customer.”
We assumed sales and finance pulled revenue from the same source.
We assumed product data was complete because “it should be.”
We assumed pipelines were trusted because no one complained.
We assumed AI would work because the demo looked great.
Assumptions are silent, they don’t announce themselves, they just sit underneath everything until they break.
 
Data strategy isn’t just about building systems. It’s about surfacing assumptions before they turn into failures.

Start asking the questions that rarely get asked:
-        What definition are we actually using here?
-        Who owns this number?
-        How do we know this data is complete?
-        What decisions depend on this?
-        When was the last time this was validated?
These questions don’t slow work down, they prevent rework. They don’t create friction, they remove surprises.

Most organizations don’t struggle because they lack data maturity. They struggle because they lack assumption visibility.
Bring the assumptions into the light, and suddenly the whole system becomes clearer, safer, and faster.
Because in data, as in life, the biggest risks are the ones we didn’t realize we were taking.

Most data problems don’t come from bad technology, they come from business denial.

“We trust our data.” (But no one can agree on basic KPIs.)
“Our systems are integrated.” (Except the ones that matter.)
“We already have governance.” (Meaning: an Excel sheet from 2018.)

The hardest step in building a data strategy isn’t designing the framework. It’s admitting the truth about how decisions are actually made. Because every organisation says they want to be “data-driven”, until data challenges how things really work.

Here’s the shift that separates successful data leaders from the rest:
1.     They start with transparency, not perfection - Call out what’s broken, clearly and calmly.
2.     They focus on business pain, not data maturity - Fix what hurts the business most, not what’s easiest to fix technically.
3.     They measure recovery, not activity - “We fixed X, and saved Y” is more powerful than “we implemented Z framework.” 
4.     They communicate wins relentlessly - People don’t support what they don’t understand.
 
Data strategy is less about vision, more about courage.
Courage to confront, simplify, and prove value fast.

You can have the best dashboards, the cleanest pipelines, and the fanciest data stack, but if your business teams don’t understand what the numbers mean, it’s all wasted effort.

We’ve seen this pattern too often across organisations:
-       Reports built by analysts → misunderstood by executives
-       KPIs defined by IT → irrelevant to operations
-       Dashboards filled with data → but no decisions follow

The problem isn’t lack of data. It’s lack of translation.
Data teams talk in tables. Business teams think in outcomes. Someone has to connect the two.

That’s why data storytelling and business alignment matter more than ever:
-       Build metrics around business language, not technical logic
-       Tie every report to a decision or action
-       Communicate insights, not outputs
-       Measure adoption, not just delivery

The goal isn’t to show more data. It’s to drive better decisions.

Here’s a simple question to test your data maturity: When your execs see a report, do they say “so what?” or “what’s next?”

You can buy a new platform. You can implement governance. You can redesign pipelines. But if the organisation still makes decisions on gut, politics, and PowerPoint, your data strategy is just decoration.

Data strategy succeeds when decisions change, not when tools change.
So ask yourself:
-       What decisions are slow today?
-       Which ones are made with incomplete or inconsistent data?
-       Where is value leaking because nobody trusts the numbers?
-       And who owns those decisions?
This is where real transformation starts.

Forget the maturity models.
Forget the perfect architecture.
Forget the “future state” diagrams.
Start with one decision that matters, revenue, risk, cost, or customer experience. 
-       Map the data that feeds it. 
-       Fix what’s broken. 
-       Measure the impact.
-       Tell the story.
-       Repeat.
That’s how data strategy becomes business strategy. Not through complexity, but through clarity.

If your data strategy feels complex, inconsistent, or constantly shifting it’s usually because no one has made the hard trade-offs yet.

Trade-offs are the unspoken backbone of every successful data program, but inside most organizations, they’re avoided, delayed, or buried under technical arguments.

You can’t have unlimited use cases, perfect quality, full decentralization, rapid delivery, and zero governance friction, all at the same time.

Strong data leadership means choosing:
-        Which data domains matter most
-        Which quality issues actually block revenue or decisions
-        Which use cases deserve priority
-        Which teams truly own what
-        Which risks we tolerate and which we don’t

Without those choices, everything feels urgent and nothing moves, teams chase noise instead of outcomes, quality efforts scatter across the organization, AI pilots stall and governance becomes a wishlist instead of a discipline.

If your data strategy feels overwhelming, you don’t need more tools or more people, you need sharper trade-offs, you need to decide what matters now, what matters later, and what doesn’t matter at all.

Clarity isn’t created by expanding the scope, it’s created by the courage to reduce it.

The hard part?
Getting the business to care.

Most data strategies don’t fail because they’re wrong.
They fail because they’re ignored.
-       The business doesn’t see the value.
-       Leaders don’t change how they make decisions.
-       Ownership stays vague - “someone else’s problem.”

The result?
Dashboards gather dust. Governance feels like bureaucracy. Data teams start sounding like they’re begging for attention.

Here’s the reality we don’t talk about enough: A data strategy without business traction is just documentation.

The turning point comes when data teams stop “selling” and start co-creating, with Finance, with Operations, with Marketing, with Risk.

That’s where alignment happens.
That’s where value starts to show up.

Assumptions like:
-        “Everyone knows what ‘customer’ means.”
-        “Our data is clean enough.”
-        “The business will follow the process.”
-        “AI will figure it out.”
These assumptions quietly create misalignment, frustration, rework, and in some cases full-blown project failure.

Data strategy collapses not because teams lack skill, but because leaders assume alignment where none actually exists.
If you want real progress, replace assumptions with clarity:
-        Agree on definitions before you build systems.
-        Validate data quality before you build dashboards.
-        Confirm ownership before you design processes.
-        Check readiness before you launch AI use cases.

Great data work doesn’t begin with tools.
It begins with shared understanding; the discipline of making the implicit explicit.
If you’re feeling friction in your data programs, don’t buy another platform.
 Start by hunting the assumptions.

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