How to Build Confident Data Analysts and Upskill Your Team | Dub Dub

by Sarah Burnett & Fiona Crocker | Co-Founders, Dub Dub Data

You know that analyst on your team. Technically brilliant. Quietly exceptional. Builds the most useful Tableau dashboard in the organisation - and then says absolutely nothing about it in the meeting.

They are not quiet because they lack capability. They are quiet because nobody ever designed an environment that made it safe to speak up. And nobody coached them on what speaking up actually looks like.

Here is the hard truth for data leaders: the biggest gap in most data analyst roles is not technical skill. It is confidence - and confidence is something that environments produce or suppress. It is not a personality trait. It is a design problem. And design problems have solutions.

Whether you are trying to develop a strong data analyst role within your organisation, upskill an existing team, or build a career in data yourself, this guide covers the frameworks and best practices that the world's most effective data coaches have spent years refining. These are not bootcamp theories. They are real-world lessons from the field of data analysis - the kind that actually change how teams perform.

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We’ve dedicated an entire episode of our unDUBBED podcast to D34 Lifting the Floor: How Andy Kriebel Builds Unstoppable Tableau Analysts. Skip the scroll and view here.

Why Building Confident Data Analysts Is a Leadership Problem, Not a Hiring Problem

Most organisations treat analyst confidence as a recruitment challenge. Hire the right people, and the problem solves itself. But this framing misses something critical about how data analyst skills actually develop.

Confidence is not a fixed trait you either find in a candidate or you do not. It is a behavioural output - something that emerges, or fails to emerge, depending entirely on the environment a person works in. Psychological safety - the belief that you will not be penalised for speaking up, asking a question, or admitting uncertainty - is one of the strongest predictors of both individual performance and team effectiveness.

If your data analysts are not speaking up, not asking clarifying questions, not owning their dashboards in front of decision makers - the question is not "what is wrong with them?" It is "what is wrong with the environment we have built around them?"

The good news: environments are designable. And Tableau consulting and training that focuses only on the tool, without attending to the environment, will always underdeliver on its potential to provide opportunities to learn and grow.

The Three Conditions That Produce Confident Data Analysts

Before investing in Tableau training or team development, it is worth understanding what actually generates analyst confidence. Three conditions consistently make the difference in the world of data analysis.

1. Permission to Not Know

In most data teams, not knowing something is treated as a failure. Analysts who ask questions they feel they "should" know risk being judged as underprepared. So they stay quiet, figure it out alone, and sometimes build the wrong thing entirely.

In a high-confidence environment, asking a question is treated as a signal of engagement, not ignorance. The implicit contract is: if you do not understand something, you ask - because someone else almost certainly has the same question and nobody is voicing it.

This shift does not happen by accident. It has to be explicitly modelled, consistently rewarded, and occasionally enforced by calling on people who are clearly confused but holding back.

For data leaders: make the first question in every team session a safe one. Ask it yourself if you have to. Model the behaviour you want to see. Reward the person who asks the thing nobody else will. This is how you develop a strong foundation of communication skills across your entire team.

2. Accountability That Feels Supportive, Not Punitive

There is a difference between accountability that drives performance and pressure that drives avoidance. The first creates ownership. The second creates hiding.

High-performing data teams create accountability structures that feel supportive rather than punitive. This might look like visible commitments in front of peers, lightweight check-ins on progress rather than high-stakes reviews, and the normalisation of "I tried this and it did not work" as useful information rather than evidence of failure.

Gamification works surprisingly well here - not because adults need gold stars, but because visible recognition creates social proof. When someone is celebrated for posting their data visualisation publicly, asking a question in a training session, or completing a challenging real-world dataset, it shifts the group's perception of what is normal. And what the group considers normal shapes individual behaviour far more than any individual motivation ever will.

3. Coached Discomfort

Growth does not happen inside the comfort zone. But most people will not voluntarily step outside it without a specific, supported push from someone they trust.

The most effective coaching intervention is often deceptively simple: a direct, personalised challenge paired with immediate accountability. Not "you should think about speaking at a user group." But: "You have plenty to say. Come back next week and tell me you have booked a speaking spot."

The difference is specificity, timeline, and relationship. A good coach does not create pressure through hierarchy. They create it through trust - the sense that someone who genuinely believes in you is watching, and that stepping back would feel worse than the discomfort of stepping up.

For data leaders: know your people well enough to know which discomfort is productive for each analyst. The one who freezes in presentations needs a different push from the one who disappears into deep technical work and never surfaces with findings. Both have the soft skill gaps that coaching can address - but they are different gaps.

How to Build a Data Analytics Learning Culture That Actually Sticks

One of the most persistent frustrations in data analytics team development is investment in training that does not translate back to the job. A workshop happens. Energy is high. Analysts return to their desks and revert to exactly what they were doing before.

This is almost never a training design problem. It is a culture problem. Learning sticks when it is embedded in the day-to-day environment - when there are regular opportunities to apply, share, and get feedback on what was learned, and when analysts can practice on real-world problems rather than synthetic exercises.

Why Live Data Analyst Training Beats Recorded Content

There is a reason people sign up for online courses to learn data skills with genuine enthusiasm and quietly stop watching after the third module. Recorded content is passive, and passive learning has a very low transfer rate - particularly when it comes to developing the soft skill of actually using and communicating data analysis under pressure.

Live training - where you have to show up, engage, answer questions, and be seen - has a fundamentally different psychological contract. You are visible. Your participation is noticed. The discomfort of being called on when you are unsure forces active processing that watching a video simply does not.

The practical implication for data leaders: a regular live session - even a short one - where someone is expected to demonstrate a technique or analyse data in front of peers, creates more durable skill development than a library of excellent resources that nobody is completing. This applies whether your team is learning Tableau, SQL, or statistical analysis.

How Data Analyst Portfolios Build Accountability and Visibility at the Same Time

Homework in a professional development context can sound patronising. In practice, it is one of the most effective mechanisms for building data analyst skills and building career visibility simultaneously.

Completing an assignment, publishing it to a portfolio, and receiving feedback closes the learning loop in a way that passive consumption never does. It generates evidence of capability. It forces the consolidation of knowledge into something concrete that potential employers and stakeholders can actually evaluate.

More importantly, it creates accountability. When the expectation is that training is applied - not just absorbed - analysts engage differently from the start. Career changers who invest in building a strong Tableau Public or data visualisation portfolio consistently outperform those who rely on credentials alone when entering the job market.

For data leaders: stop celebrating attendance and start celebrating application. Ask your team not "did you do the course?" but "what did you build with what you learned, and how did it perform against real-world data?"

Peer Learning and Statistical Analysis Practice: The Fastest Path to Data Fluency

One of the fastest ways to develop data analyst skills is to watch someone slightly more experienced work through a real problem in real time - including how they approach statistical analysis, handle inconsistency in raw data, and make decisions about visualisation tools. Not a polished tutorial. An actual person, thinking out loud, making decisions, occasionally backtracking.

This kind of peer observation is extremely high-value and almost never formally structured in data teams. Creating regular pairing opportunities - where one person works through a data analysis problem while another watches and asks questions - is low-cost and remarkably high-return. The watcher often learns more than the person doing the work.

This practice is particularly powerful for developing the ability to analyse data and uncover hidden patterns - a skill that is difficult to teach in the abstract but absorbs quickly through observation of real-world practice.

What Strong Data Analyst Skills Actually Look Like in Practice

There is a wide range of technical skills that matter in a data analyst role - from SQL and spreadsheet proficiency through to statistical analysis, data visualisation, and the ability to work with big data. But the analysts who truly stand out in the field of data analysis are rarely those with the deepest technical knowledge alone.

The Technical Foundation: SQL, Statistical Analysis, and Visualization Tools

A strong data analyst needs to be able to query and analyze data efficiently. SQL remains one of the most in-demand data analyst skills across industries - the ability to pull, filter, and transform raw data is foundational to almost every data analyst role. Statistical analysis skills allow analysts to move beyond describing what happened and start explaining why, identifying trends, and building models that support business decisions.

Visualization tools like Tableau sit on top of this foundation, translating data analysis into formats that decision makers can actually act on. The best data analysts develop a strong instinct for which visualization approach will best serve a given audience - not just which chart type is technically correct, but which will land in the room.

Tools like Power BI are also increasingly relevant in organisations that run mixed analytics environments, and a strong data analyst in today's job market benefits from understanding the broader analytics ecosystem rather than being fluent in only one tool.

The Soft Skills That Separate Good from Great in a Data Analyst Role

Technical skills get a data analyst into the room. Soft skills determine what happens once they are there.

The key soft skills for a successful career in data are more specific than most job descriptions suggest. Communication skills - the ability to explain complex data analysis to non-technical stakeholders without dumbing it down - are consistently cited as one of the hardest things to find in the field of data analysis. The ability to present findings clearly, tell a story with data, and connect data insights to business decisions is what elevates a data analyst from useful to indispensable.

Equally important is the ability to listen before building - to understand what a stakeholder is actually asking before opening a spreadsheet or writing a query. Data analysts who develop this habit iterate less, produce work that better serves real-world problems, and build the kind of trust with stakeholders that accelerates their career in data.

How to Think Like a Data Analyst, Not Just Work Like One

There is a moment in the development of any complex skill where you stop consciously applying techniques and start thinking natively. In data analysis, this is the shift from knowing how to analyze data - to automatically seeing business problems through the lens of what the data can reveal.

This is what it means to think like a data analyst. And it is one of the most important transitions in a data analyst role, at any level of seniority.

This shift cannot be forced, but it can be accelerated. The fastest path is a combination of deliberate volume of practice on real-world datasets, exposure to how skilled practitioners approach data analysis problems out loud, and consistent reflection on why certain analytical decisions were made - not just what was done.

For those earlier in their career in data, gaining practical experience with real-world data - including dealing with inconsistency, missing values, and the messy reality of raw data in business environments - is more valuable than any amount of theoretical training. Bootcamps and structured programs can provide a foundation, but the field of data analysis rewards those who stay updated on industry trends and continuously apply their skills to new contexts.

The Career in Data Changer Advantage: What Data Leaders Are Missing

Data teams consistently underestimate career changers. The assumption is that people transitioning from other industries lack the foundational data analyst skills, need more onboarding time, and carry higher hiring risk.

The evidence points the other way.

Career changers who make a deliberate decision to move into a data analyst role tend to be among the highest-performing learners in structured development programs. They are investing in themselves, often at personal financial cost. They know exactly what is at stake. They are building portfolios with urgency, not complacency - and they are actively developing their data analyst skills in ways that signal commitment to potential employers.

They also bring something that career-lifers often lack: domain perspective from outside the world of data analysis. A former nurse transitioning into healthcare analytics understands context that a pure technical hire never will. A former teacher moving into data storytelling has communication skills that most technically trained analysts are still developing years into their careers in data.

When hiring or developing analysts, look for the investment pattern - the portfolio being built, the real-world problems being tackled, the LinkedIn presence being developed - rather than years of experience with specific visualization tools. That pattern is a stronger signal of long-term success than an analytics degree alone.

The Listening Test: The Soft Skill That Predicts Data Analyst Success

Here is a question worth sitting with if you lead a data team: if you asked someone to name the most important characteristic of a great data analyst or consultant, and they did not say listening - what would that tell you?

Technical data analysis skills can be trained. Domain knowledge can be acquired. Visualization skills build with practice. But the ability to genuinely listen - to understand what a stakeholder is actually asking before you analyze data, to hear the concern beneath the stated requirement, to know when the question being asked is not the question that needs answering - that is the foundation everything else rests on.

Data analysts who listen well produce work that solves real-world problems. They gain insights that actually drive business decisions rather than sitting in reports that nobody reads. They iterate less because their first pass is already aligned with what was actually needed.

This is the key soft skill most often underdeveloped in highly analytical people - because the analytical mind is already formulating the answer while the other person is still talking. Building deliberate listening habits is some of the highest-return development work available to any data analyst, regardless of where they are in their career in data.

Building Your Network to Stay Updated and Accelerate Your Data Career

For analysts navigating a career change or trying to level up, one of the most high-leverage moves is also one of the least common: finding the people who are already doing what you want to be doing in five to ten years, and getting in front of them.

Offer to pay for an hour of their time if you have to. Ask what they would do differently if they were starting over. Ask how they stay updated on industry trends and the evolving field of data analysis. Use that to shortcut years of trial and error they have already done.

The people who are genuinely excellent in the world of data analysis are rarely threatened by someone new showing interest. The market is wide enough. They may even have referrals to send your way - but only if you have put yourself out there first.

This is the spirit that has always made the DataFam community one of the most generous professional communities in the analytics world. Not everyone racing to get ahead of each other, but people pulling each other up - sharing data analyst skills, providing opportunities to learn, and helping each other develop a strong foundation that leads to successful careers.

Conclusion

Building confident, high-performing data analysts is not primarily a hiring challenge or a training challenge. It is a design challenge.

The environment you create - the safety to ask questions, the accountability structures that feel supportive, the feedback habits that remove surprises, the learning culture that rewards application over attendance - determines what your analysts become, far more than the data analyst skills they arrived with.

Whether someone is early in their career in data or a seasoned practitioner trying to think like a data analyst at a deeper level, the same principle applies: confidence is repetition, questions, feedback, and curiosity. It is built deliberately, over time, by leaders who understand that lifting the floor is their most important job.

If you are ready to invest in Tableau consulting and team enablement that actually changes how your organisation uses data, we would love to talk.

FAQs

1. What are the most important data analyst skills to develop?

   The most in-demand data analyst skills combine technical capability - SQL, statistical analysis, data visualization, and the ability to work with raw data and spreadsheets - with key soft skills including communication, listening, and the ability to present findings to decision makers. The analysts who build a successful career in data are those who develop both in parallel, not just the technical side.

2. How do you build confidence in a data analyst role?

   Confidence in a data analyst role is built through environment, not personality. Psychological safety, regular feedback, real-world practice on meaningful datasets, and a culture that rewards questions over silence are the most reliable drivers. Passive training alone does not build the confidence to present data analysis and influence business decisions.

3. What is the fastest way to develop data analyst skills?

   The fastest path combines live training where participation is expected, deliberate practice building a portfolio of real-world data analysis work, and regular exposure to more experienced analysts working through problems out loud. Bootcamps can provide a foundation, but applied experience on real-world data is what accelerates fluency fastest.

4. How do soft skills affect a data analyst career?

   Soft skills are what determine whether a data analyst's technical work actually drives business decisions or sits unread in a report. Communication skills, the ability to listen before building, and strong presentation skills are consistently the hardest to find and the most valuable in the field of data analytics. Data analysts who develop these key soft skills gain insights that land with stakeholders and build the kind of trust that accelerates career progression.

5. How should data leaders develop their team's analytics skills?

   Stop celebrating attendance at training and start celebrating application. Ask not "did you complete the course?" but "what did you build, and how did it perform on real-world data?" Create regular live sessions, pairing opportunities, and portfolio-building expectations. The analysts who upskill fastest are those in environments that provide opportunities to learn through doing, not just watching.

6. What does it mean to think like a data analyst?

   Thinking like a data analyst means automatically seeing business problems through the lens of what the data can reveal - rather than consciously applying techniques one at a time. This transition happens through deliberate practice, exposure to how skilled practitioners analyze data out loud, and consistent reflection on why certain analytical decisions were made. It is the point at which data analyst skills become genuine data fluency.

Ready to Build a Data Analytics Team That Performs and Communicates?

The gap between data teams that are technically capable and data teams that are genuinely influential is almost never about the tools. It is about how analysts are developed, coached, and supported to bring their best thinking into the room.

At Dub Dub Data, we work with organisations across Australia and New Zealand to close that gap. Whether you need Tableau consulting, fractional data leadership, hands-on team enablement, or a strategic approach to building a data culture that actually sticks - we are here to help.

Book a 30-minute discovery call today and let's talk about where your data analytics team is, where you want them to be, and how to get there faster than you think.

Book your discovery call here

Watch Lifting the Floor: How Andy Kriebel Builds Unstoppable Tableau Analysts Podcast

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🎙️ Unscripted. Uncensored. Undeniably data.

🎙️ D34: Lifting the Floor: How Andy Kriebel Builds Unstoppable Tableau Analysts

In this episode of unDUBBED, hosts Sarah Burnett and Fiona Crocker sit down with Andy Kriebel, Tableau Visionary, Hall of Famer, and founder of Next Level Tableau, to unpack the coaching philosophy behind one of the most respected Tableau training programmes in the global data community.

Andy has spent nearly two decades turning analysts into exceptional talent, and 10 of his trainees have gone on to become Tableau Visionaries themselves - out of only 72 people worldwide who hold that distinction. His stated goal? To make you so good at Tableau that you don't need him anymore.

The conversation dives deep into how confidence is built through environment, not personality, why soft skills are just as critical as technical Tableau skills, and what separates analysts who improve slowly from those who accelerate fast. Andy also shares the one question he asks that predicts who will become a great consultant, his walking one-on-one coaching philosophy from his days at the Data School in London, and how Next Level Tableau is rebuilding the sense of community that the #DataFam has been missing.

Takeaways

• Confidence is an environment you design, not a personality trait you're born with.
• Active participation and accountability accelerate Tableau skill development faster than any self-paced course.
• Soft skills like listening, asking questions, and giving feedback are the number one predictor of a great consultant.
• Career changers who invest in their own learning consistently outperform those whose companies foot the bill.
• Building your network and connecting with influencers in your field is the fastest shortcut to career growth.
• The Tableau community thrives when people focus on lifting the floor, not racing to the top.
• Homework, portfolio building, and LinkedIn visibility compound quickly for career switchers breaking into data.
• Learning Tableau is a continuous process - even seasoned practitioners pick up new efficiency gains in every class.
• The difference between learning Tableau and thinking Tableau is built through deliberate repetition and observation.
• Great managers remove obstacles, deliver no surprises at review time, and show up consistently for their people. 

Chapters

00:00 Introduction to Confidence in Data Analysis

02:55 Building a Community of Confident Analysts

05:45 The Role of Teaching and Engagement

08:42 Andy Kriebel's Journey to Coaching

11:28 The Importance of Soft Skills in Data Analysis

14:00 Creating a Supportive Learning Environment

16:23 Nurturing Confidence Through Environment

19:10 Career Transitions and Building Skills

22:36 Navigating Career Changes

27:28 The Importance of Active Participation

33:19 Learning Through Observation

36:24 Thinking Like Tableau

39:35 Key Traits of a Great Consultant

Links

Next Level Tableau

Andy Kriebel on LinkedIn 

Andy Kriebel on Tableau Public 

DataIQ Top 100 Most Influential People in Data

Keywords

andy kriebel, next level tableau, tableau training, tableau visionary, building confident analysts, data analyst skills, tableau community, datafam, soft skills for data professionals, tableau coaching, career change into data, data visualisation training, analyst confidence, tableau certification

TL;DR

Andy Kriebel has spent two decades proving that exceptional Tableau analysts aren't born - they're built. This episode unpacks his coaching philosophy, the power of community and accountability, and why lifting the floor is the only strategy that works long term.