Debunking six common myths about what data analysts do 

The world of data is evolving at a pace that’s hard to keep up with, and so are the misconceptions surrounding it. There is a lack of clarity around what data analysts do and the value they bring to organisations. And this is impacting hiring decisions, technology spend, and strategic priorities in ways that can quietly hold businesses back. 

At the same time, AI-generated code is automating many of the technical tasks analysts once handled. This can create uncertainty about the future of these roles and their relevance.  

In reality, the evolution of data tools is not diminishing the importance of analysts — it is redefining it. As the volume, complexity, and strategic importance of data continue to grow, so does the need for professionals who can interpret information in context, connect insights to real-world decisions, and help organisations navigate an increasingly complex digital environment. 

To separate fact from fiction, we’re unpacking the six most common myths surrounding data analysts:

• Myth 1: AI will replace data analyst experts
• Myth 2: Low-code, no-code tools will replace data analysts
• Myth 3: It is faster to outsource data capabilities
• Myth 4: Data analytics is just about creating dashboards
• Myth 5: You must come from a tech background to be a data analyst
• Myth 6: Data engineering is the same as data analytics

Myth 1: AI will replace data analyst experts

It’s the question everyone’s asking: will artificial intelligence make data analysts redundant? The short answer is no. 

AI is good at pattern recognition, automation, and processing large volumes of data at speed. But what it cannot replicate is human judgment. Understanding what data analysts do goes far beyond running queries; it encompasses contextual understanding, critical thinking, and the ability to ask the right questions. Knowing which problem to solve, how to frame it, and what the findings mean for a specific business requires expertise that AI in data analytics simply cannot replicate. 

Tools like machine learning and generative AI are handling repetitive data preparation tasks, freeing analysts to focus on higher-value work: storytelling with data and stakeholder communication. Rather than a threat, machine learning for analysts is increasingly becoming a powerful tool.  

Data scientist employment in the US is projected to grow 34% between 2024 and 2034, making it the 4th fastest-growing occupation in the US. This is a clear sign that data analytics careers are not only safe but thriving. 

FDM Consultant, Norbert Csecs, who went through our graduate programme and is currently working as a  Technical Business Analyst for a financial institution, believes, “AI can automate some routine analysis, but its outputs still need human judgment and verification. It won’t replace data analysts, experts or juniors, because framing the right questions and interpreting results in context remain human responsibilities. In practice, AI changes how analysts work more than whether they’re needed.”

Myth 2: Low-code, no-code tools will replace data analysts

Low-code and no-code platforms are making waves in the software and data space. The low-code development market is projected to reach $58.2 USD billion by 2030. 

 Without the need to write complex lines of code, organisations can save weeks, if not months, of development time, with low-code platforms reported to reduce app build times by up to 90%. 

The rise of low-code and no-code platforms is not about replacing traditional developers but redefining their role. Data Analysts are transitioning from being solely code-centric to becoming strategic enablers of innovation.  

While low-code and no-code platforms offer pre-built components, many applications require custom functionality. Professionals with strong data engineering skills in languages like Python, JavaScript, or Java remain essential for extending platform capabilities, writing custom APIs, or building bespoke features.‍

Myth 3: It is faster to outsource data capabilities

Businesses worldwide are feeling the pressure to maintain their leading position through digital transformation. But, in an increasingly competitive market, many believe the only cost-effective route is outsourcing.  

In reality, building data capabilities in-house (or with the right embedded talent) almost always outperforms outsourcing in the medium to long term. 

Outsourced teams lack the deep organisational context that makes data truly valuable. They don’t know the quirks of your data sources, the nuances of your business model, or the unspoken questions your leadership team are trying to answer. The result is often generic solutions that require significant rework and lengthy knowledge-transfer cycles.

Myth 4: Data analytics is just about creating dashboards

Dashboards may be one of the most visible outputs of data analytics, but they’re only one part of the process. The real value lies in interpreting the data’s meaning for businesses and using it to guide smarter decisions. 

Analysts provide context, highlight what matters, and help organisations understand what actions to take next. In short, data analytics isn’t just about building dashboards; it’s about turning information into informed action.

Myth 5: You must come from a tech background to be a data analyst

Reality: Technical skills are important, but you don’t need to come from a tech-heavy background to be a data analyst. 

Data analysis is built on curiosity, problem-solving, and the ability to interpret information. While technical tools can be learned, the real value often comes from understanding context and turning data into meaningful insights. Many successful data analysts start by building practical skills over time. 

A tech background can be helpful, but it’s not a requirement. In fact, at FDM, our consultants come from a wide range of academic backgrounds. We equip professionals with the skills they need to thrive in the rapidly evolving data landscape. Our programmes provide the foundation for success. What’s important is your willingness to learn and adapt. 

Myth 6: Data engineering is the same as data analytics

While they are related, data engineering is about preparing and structuring data, while data analytics involves analysing the data to uncover insights. Data engineering sets the stage for effective analysis by ensuring that data is clean, organised, and accessible. Without a solid engineering foundation, even the best data analytics tools won’t produce reliable or meaningful results. The smooth flow of data from engineering to analytics ensures that the organisation has access to real-time, high-quality data for decision-making and insights generation.

Summary

As data complexity continues to grow and the stakes of getting it wrong continue to rise, the need for skilled, experienced, and commercially-minded data professionals has never been greater. AI and automation will change how these roles are performed — but they will not replace the human expertise, judgement, and strategic thinking that sit at the heart of a truly successful data-driven organisation.

How FDM can support

At FDM Group, we recognise the challenges businesses face in sourcing skilled professionals and the growing need for hands-on, industry-relevant training. Our Data & Analytics Practice is designed to bridge the skills gap by equipping individuals with the knowledge and experience they need to excel. Whether you’re a graduate looking to enter the field, a career returner, or an experienced professional aiming to upskill, our careers programmes provide the essential coaching and support to help you succeed.    

Read more about our other Practices to find the right career path for you.