Become a Data Expert by 2026: How AI Has Transformed the Field & What Still Matters!

By 2026, data expertise no longer simply involves analyzing numerical tables and generating recommendations. In an era where generative AI is reshaping practices and businesses now demand mastery of data engineering and governance, the profession is rapidly growing more complex. What technical and human skills are truly necessary? Is a technical background vital for success in data? How can one effectively train to stand out in this market? Antoine Krajnc, founder and CEO of Jedha, shares his perspective for BDM on a sector undergoing rapid change and the keys to building a solid career within it.

Training in Data

What characterizes the role of a data expert in 2026? What does this role actually involve today, in your view?

In 2026, in the age of generative AI, the tasks of data experts include a more “industrial” dimension. Whereas a few years ago, these experts were asked to produce recommendations from analyses of provided data, today they are also expected to source data independently and industrialize data flows to enhance these analyses. Moreover, the nature of data has evolved considerably, as natural language processing has become much more accessible.

While data analysis previously focused on tabular data (and occasionally some images), the scope has now expanded thanks to LLMs, as textual data (and sound) can now be handled more efficiently.

Furthermore, the “technical” aspect of skills required has also evolved. Experts are now expected to be well-versed in best practices for data governance, to implement effective and secure data management while complying with regulations, including GDPR and the AI Act.

However, the fundamental goal of the expert role remains unchanged: to understand an organization’s data to make increasingly relevant decisions. The difference lies in the level of complexity, which has escalated as data becomes more diverse and intricate.

What are the indispensable technical skills for a career in data in 2026?

Although AI has made significant inroads into technical fields, the core skills that make a difference have remained similar.

Training to Acquire Data Skills

What soft skills are most sought after in this sector?

Regarding soft skills, I see three as being paramount:

1. Ability to challenge and question: this skill has become more than necessary because AI is very adept at generating results that “appear” excellent. Without the ability to question and challenge the findings, one might trust analysis results that are entirely incorrect.
2. Communication: whether it’s about understanding different roles, simplifying concepts, or effective storytelling, communication remains a crucial skill, even for a data expert.
3. Rigor, business thinking, and a willingness to learn: these are classic soft skills but remain ever relevant as technology evolves quickly, necessitating discernment between what is useful to learn and implement and what is not.

What profiles succeed best in this profession? Is a technical background essential?

Honestly, a minimum technical foundation is now indispensable, especially for those starting in the field. This isn’t necessarily a setback. Particularly if the position mainly involves strategic management of data governance, where more managerial skills might be emphasized. Outside of this, the data field is becoming increasingly technical.

However, this doesn’t mean that the field of data is only for engineering school alumni.

The alumni of our school are proof that one can have a non-technical background, get trained, and then succeed in the field. The key is indeed to undergo this training.

What tools, languages, and methodologies must a data expert absolutely master?

There isn’t much more to add to what has already been listed under technical skills, except that Agile methodologies have also made their mark in data. After some adaptations, companies have preferred this type of methodology. It’s better to be comfortable with this topic.

What training path would you recommend for someone aiming to become a data expert in 2026?

Firstly, it’s crucial to start getting hands-on experience as soon as possible. If you haven’t already done so, there are many free mini-courses available (we offer them on our platform Julie, where anyone can register and access over 50 hours of free content, including on Python and other data technologies). This will help to solidify your project.

Next, it depends on your background, aspirations, and preferences. For someone starting from scratch, I recommend seeking out training programs that take this into account. At our school, for example, learners start with the “Essentials” program, which provides all the basic skills needed when starting from zero.

Advice from Antoine

Regardless of your level of ambition, ensure that the chosen program makes you autonomous in a coherent set of technologies. Too many technologies and you only scratch the surface; too few and you won’t be relevant in the market.

Antoine Krajnc

Founder and CEO, Jedha

This is why we created our full-stack data programs, which equip learners to be autonomous across all market-demanded technologies. And our Lead level, which brings learners to the state-of-the-art level to be highly differentiated in the market.

What are the possible career opportunities for a data expert?

For the more technical professions, which make up the bulk of the market, there are the classic roles: data scientist, data analyst, and data engineer. The demand for the latter is growing significantly in companies.

The number of positions for data scientists and data analysts remains fairly stable, with a nuance for the data analyst role, where a new level is emerging at the crossroads with the data engineer: the analytics engineer.

There are also more managerial positions, like the data manager, which focuses more on governance and whose needs are also high.

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