Here's a scenario you might recognise: Your organisation invests in an analytics platform -Tableau, Power BI, Alteryx, you name it - for a specific department or enterprise-wide. You run training sessions, everyone's excited, the initial feedback is great. Fast forward twelve months, and only a fraction of people are using it effectively. The rest have quietly drifted back to Excel (or other traditional tools).
Leadership starts questioning the Return On Investment (ROI). IT wonders if they picked the wrong tool. Training/Enablement teams ask if they need more sessions. But here's what I've observed working with organisations over the years: the problem is rarely the technology itself.
According to McKinsey, 70% of technology transformations fail, with common causes including lack of user buy-in and failure to sustain adoption. But here's the opportunity - the major blockers that I’ll highlight in this blog post can be addressed even before you buy (or renew) the licences.
The Real Problem
Analytics rollouts fail when you treat them as technology implementations rather than organisational change initiatives. Training teaches people how to use tools, but it doesn't address the culture, priorities, structural barriers, and cross-team dependencies that determine whether they actually use them day-to-day.
Here's a critical insight I've observed in many organisations: often, a process or structure problem gets framed as a technology problem. Organisations think "if we just get the right tool, people will work differently." But a new tool won't change ingrained ways of working without intentional organisational design around it.
Technology enablement should not be treated as a project with a start date and end date. It’s an ongoing change management programme. Yes, your initial enablement initiative, run by a Centre of Excellence or central data team, will have defined milestones and deliverables before moving over to BAU. But the change itself doesn't stop there. Successful organisations build ongoing support into BAU: office hours, community forums, quarterly updates. The enablement project ends; the change doesn't.
So what are the main things that get in the way? Based on my experience firsthand at a major automotive manufacturer with 40,000+ employees, conversations with clients since joining The Information Lab, and insights from colleagues who've worked on enablement programmes at major organisations in various industries - Financial Services, Retail, and Defence just to name a few, I've identified four major blockers. When these blockers are addressed systematically, I’ve seen organisations move from 20-25% active adoption to 60%+ within 14 months.
Blocker 1: Leadership Doesn't Walk the Talk
This isn't about a CEO standing up at a town hall saying, "data is important" and everyone nodding along. We've all sat through those. Sure, it helps but it’s far from being enough.
It's about what happens after the enablement programme. Your team gets trained, they're excited, they start exploring data to answer real questions. Then their manager asks them to drop everything for an urgent slide deck. Within a few weeks, using the new platform starts to feel like a luxury they can't afford, and they return to old habits.
I've worked with organisations that dedicate "self-development time" in people's calendars, but that time is the first thing that gets sacrificed when deadlines hit. It's not just about executives saying the right things. It’s about creating a culture where learning new tools and working differently is genuinely valued and expected.
What success actually looks like
At the automotive organisation I worked at, senior leaders didn't just endorse Tableau in presentations. They actively used it themselves. For the quarterly internal earnings calls for example, which were ‘mandatory’ calls for all employees, they used to present the numbers using Tableau dashboards instead of PowerPoint slides. They also regularly asked for data-driven insights in decision forums. This was sending the right message to the business and reinforcing a culture where decisions are made using data.
There was a clear pattern: the departments with the highest adoption were invariably led by executives who were vocal advocates for data-driven decision-making. You could almost predict which teams would embrace the platform just by watching their leadership.
When leaders consistently model the behaviour they want to see, teams follow.
Blocker 2: No Time or Permission to Work Differently
Organisations invest in powerful analytics platforms but don’t always create the space for people to actually use them differently. People are already at 100%+ capacity, and now they are expected to “go explore data” on top of everything else they are doing.
Here's a conversation I've heard variations of more times than I can count:
Analyst: "I'd love to explore that question, but I need to finish the weekly Excel reports first."
Manager: "Well, can't you do both?"
But here’s the deeper problem: there’s often a lack of clarity on why they should use the new platform in the first place. What would the new analytics platform bring that they don’t already have with traditional tools like Excel? There’s nothing inherently wrong with doing analysis in Excel, people use it because it works for them. But the value of platforms like Tableau isn't about building prettier versions of the same reports. It's about shifting from report production to data exploration and insight generation.
One of the first interactions I had with a business user at the automotive company was them asking: "I have this monthly report in Excel. How do I replicate this in Tableau?" They were focused on replicating the output: the charts, the layout, the format. But nobody had helped them reframe the question: "What am I actually trying to understand with this monthly report? What questions am I trying to answer?"
When you start with the question rather than the output, platforms like Tableau or Power BI become fundamentally different from Excel. Instead of "I need to produce this specific chart," it becomes "I need to understand why revenue dropped, let me explore the data and see what patterns emerge." The charts are just a means to that end, not the end itself.
But people won't make that mental shift if they're not given permission to work differently. And they certainly won't invest time learning a new approach if nobody's explained that the goal isn't replicating reports faster, it's answering business questions more effectively.
Without explicit permission to invest time exploring rather than just producing, and without understanding this fundamental shift in approach, people rationally stick with what they know.
What success actually looks like
Successful rollouts start by demonstrating what "working differently" actually means. Instead of "Here's how to rebuild your Excel report in Tableau," show them: "You're spending hours each week producing this report. What question is it actually answering? Let's explore that question together in Tableau and see what insights we can surface that the static report was hiding."
Then, give explicit permission to work this way: "Your role is evolving. You're moving from 'produce these five reports' to 'help us understand why this metric is moving.' Yes, you'll be less productive initially whilst you're learning to work this way, and that's expected."
This means accepting that productivity will temporarily dip during the transition. Build this into expectations from the start. At The Information Lab, we can support this transition by embedding consultants within teams to maintain delivery whilst demonstrating this exploratory approach and building that capability in your team.
Practically: identify one recurring report and ask "What question is this actually answering? What would we want to know that this report can't tell us?" Then use that as the starting point for learning to work exploratively rather than just replicating outputs.
When people understand they're learning a different way of working and not just a different tool for doing the same work, adoption becomes natural.
Blocker 3: Training Without Purpose (and Without Understanding Your Users)
I've seen organisations run comprehensive two-day training sessions with excellent attendance, high satisfaction scores, and people leaving feeling confident. Then a few weeks later, those same people are struggling when they try to apply what they learned to their actual work.
The issue wasn't the quality of training. It's that people learned mechanics with sample data but couldn't translate that to their complex, real-world business problems.
Here's what typically happens: A finance person learns to build worksheets using the sample Tableau Superstore dataset. They return to their desk with a real question: "Why did revenue in the West region drop last quarter?" and they don't see the connection between what they learned and how to investigate their actual problem. So they go back to Excel, because at least they know how to get started there.
There's another fundamental problem with most training: it's one-size-fits-all. But data consumers who need to use dashboards for self-service analysis need completely different training from data analysts building content, who in turn need different support from business analysts doing complex investigations. Without understanding WHO your users are - their data personas - you can't create appropriate training for them.
What success actually looks like
Start by identifying your data personas. Who are your consumers, data analysts, and business analysts? What does each group actually need to succeed?
Then, effective training begins with "What questions keep you up at night?" and uses participants' actual data where possible. When that's not feasible, at least create clear parallels between sample data and their real-world problems.
Here's the mental shift that needs to happen: Modern platforms are designed for exploration and discovery, not just prettier report production. Training should reflect this.
Instead of teaching "here's how to build a bar chart," teach "you want to understand why sales dropped, let's explore the data together and see what patterns emerge." Start with the business question, not the tool feature.
The most effective programmes I've seen define use cases upfront, quantify their potential impact, prioritise them, then run multiple cohorts through structured programmes focused on solving those specific problems. This approach kickstarts visible value and helps people learn how to adapt what they've learned to their own data and processes.
And critically: learning doesn't end when training does. You need ongoing support mechanisms tailored to different personas - office hours, champions programmes, peer learning communities. When people see the platform as a way to answer their burning questions rather than just another tool they're supposed to learn, they stick with it.
Which brings us to a critical point: what happens when those support mechanisms don't actually materialise?
Blocker 4: No Data Community or Support Network
Training ends, everyone goes back to their desks, and then... silence.
A few weeks later, someone gets stuck on a problem. They don't know where to turn for help. They spend 30 minutes googling, get frustrated, and go back to Excel because at least they know how that works.
Meanwhile, someone in a different department has already solved that exact problem, but there's no way for these two people to connect.
This is what happens when organisations treat enablement as the finish line rather than the starting line. Without an ongoing community and support network, people are left to figure things out on their own. Best practices stay siloed. New features go unnoticed. And gradually, the initial enthusiasm fades as people drift back to their old tools.
What success actually looks like
Organisations that understand this build communities into their enablement programmes from the start.
This looks like an active online community, for example a Teams or Slack channel where people can ask questions and get help from peers, not just from a central IT team. It's a place where someone can share "I just figured out how to do X" and five other people benefit immediately.
Regular meet-ups matter too. Lunch and learn where people show what they've built. Monthly challenges that get people exploring features they haven't used. Show and tell where teams present their work and inspire others.
The enablement team plays a crucial role here: maintaining an intranet page with resources, FAQs, and training materials. Sending regular newsletters highlighting new features, success stories, and practical tips. Running office hours where people can get help with specific problems.
Recognise your community contributors. Celebrate the people who answer questions, share their work, and help others succeed. Make it visible that being an active community member is valued.
I've seen this make the difference between platforms that fade after a year or so and ones that become genuinely embedded in how people work. The community creates momentum that outlasts any training programme.
The Shift in Thinking Required
The question shouldn't be "How do we get people trained on this tool?"
The question should be "How do we build an organisation where people naturally use data to make better decisions, with this platform enabling it?"
That requires assessing organisational readiness first:
Leadership & Culture:
Do executives model data-driven decision-making in their own work?
Is there genuine support for the time needed to transition to new ways of working?
Do you have data champions in various pockets of your organisation who can support and inspire others?
Roles & Priorities:
Are there dedicated time and capacity for people to shift from report production to insight exploration?
Are there clear use cases, specific business questions to explore, identified and prioritised?
Users & Training:
Have you identified your data personas (Consumers, Data analysts, Business analysts etc) and their different needs?
Can people actually access the data they need to do their work?
Is there space in workflows for analytical work, or are people already drowning?
Community & Support:
Is there a data community in place - online channels, regular meet-ups, somewhere people can learn from each other?
Are there ongoing support mechanisms (office hours, champions network, resources)?
Do you have a plan for how updates and new features will be communicated to users?
If the honest answers to most of these questions are no, a rollout will be expensive, frustrating, and ultimately unsuccessful.
Practical Next Steps
So what does this mean practically? Here's what I'd recommend:
1. Assess organisational readiness before buying licences. Not just "do we need this?" but "are we actually ready for this?" Include cross-team impacts and system dependencies in your assessment.
2. Define success upfront. What does success look like in concrete terms? Clear KPIs, specific use cases with quantified impact, prioritised cohorts to roll out in phases. If you can't define what success looks like, you definitely won't achieve it.
3. Identify your data personas and create targeted enablement for each. Consumers, Data analysts, and Business analysts need different things. Stop trying to train everyone the same way.
4. Identify and empower data champions across different teams. Find the people who naturally gravitate to data work and give them the support and recognition to help others.
5. Start with high-impact wins. Run cohorts through structured programmes focused on specific business questions. Quantify the value generated. Make it visible.
6. Build transition support into your plan. Accept that there will be temporary productivity dips. Consider embedding consultants to increase velocity whilst building capability, rather than expecting instant transformation.
7. Create continuous support mechanisms. Office hours, champions networks, recorded training materials, peer learning communities. Understand that different personas need different types of ongoing support.
8. Make success visible. Recognise and celebrate people who've used data to make better decisions, especially when it leads to career progression or business impact. Nothing motivates adoption like seeing your peers succeed and advance.
Final Thoughts
Platforms like Tableau, Power BI, and Alteryx are genuinely powerful tools when implemented thoughtfully. But they need organisational foundations to succeed.
The conversation isn't just about which tool to buy. It's about whether your organisation is ready to use it - the culture, the cross-team coordination, the time for genuine transition, and the ongoing support mechanisms that turn initial enthusiasm into sustained value.
If you're considering an analytics platform rollout, or if you're frustrated with the adoption of a platform you've already invested in, the answer probably isn't better technology or more training. It's addressing these organisational blockers first.
That's where real, lasting change happens.
Recognise these blockers in your organisation? I'd be happy to discuss how The Information Lab can help you address them. Drop me a message on LinkedIn or by email at thierry.driver@theinformationlab.co.uk.
Thierry Driver is a Data Success Lead at The Information Lab, where he helps organisations build the capabilities and culture needed to get genuine value from their analytics investments. He previously led data culture and Tableau enablement at a major automotive manufacturer with 40,000+ employees.