Introduction
SQL, Python, Excel, statistics and business intelligence tools still matter. But in 2026, knowing them is increasingly the starting point, not the final reason an employer selects a candidate.
Artificial intelligence can now assist with querying, coding, visualisation and routine analysis. As execution becomes faster, employers are placing greater value on identifying the right problem, questioning assumptions, understanding business context and communicating findings.
PwC's Global AI Jobs Barometer 2026, based on more than one billion job advertisements, found that highly AI-exposed occupations are adding tasks requiring judgement, creativity and empathy at 2.5 times the rate of less AI-exposed occupations. Technical knowledge remains essential, but it becomes more valuable when combined with human judgement.
Why Data Hiring Expectations Are Changing in 2026
Entry-level data work once gave candidates time to develop through repetitive tasks such as cleaning files, producing standard reports or updating dashboards. AI is beginning to absorb parts of that work.
The June 2026 AI Workforce Pulse study by Pearson and Cognizant surveyed 750 senior HR professionals across the United States, United Kingdom and India. It found that AI already completes roughly one-third of entry-level tasks. It also reported that 96% of respondents expect entry-level positions to evolve within five years into roles where employees supervise or manage AI systems.
This does not mean entry-level roles are disappearing. In the same study, 85% of HR leaders still considered them essential, while 94% expected AI to create new entry-level roles. What is changing is the expected contribution. A junior analyst may be asked to review AI-generated analysis, investigate unusual results or identify missing context.
Employers increasingly want to know whether candidates can recognise which task is worth performing, assess the result and connect it to a real decision.
Technical Skills Still Matter, but They Are Becoming the Baseline
A data analyst still needs an appropriate technical foundation, which may include SQL, spreadsheets, Python or R, statistics, visualisation, data cleaning and basic AI literacy.
However, two candidates can know the same tools and produce very different business value. One may create a correct dashboard filled with metrics. The other may identify the three measures that explain a commercial problem and show what to do next.
| Technical baseline | What differentiates a candidate |
| Writing a SQL query | Translating a business concern into the right query |
| Building a dashboard | Selecting metrics that support a real decision |
| Training a model | Assessing whether the model is suitable and responsible to use |
| Creating a visualisation | Explaining the insight clearly to a non-technical audience |
| Using an AI assistant | Validating its reasoning, calculations and assumptions |
The right-hand column reveals how a candidate thinks when the answer is not already defined.
Seven Capabilities Employers Want Beyond Technical Knowledge
1. Problem-finding before problem-solving
Analysts are often trained to solve clearly stated questions. In the workplace, the initial question may be incomplete or even misleading.
For example, a manager may ask why traffic declined when the real concern is a fall in qualified leads. A capable analyst first clarifies the intended outcome and checks whether traffic is the appropriate measure.
The Pearson and Cognizant study found that 64% of surveyed organisations valued identifying new problems and developing new solutions over solving known problems with established methods. Candidates who can frame ambiguity are becoming more valuable than those who simply wait for a perfect brief.
2. Business understanding
Technical findings become useful when connected to how an organisation operates. Data professionals need to understand customers, revenue, costs, risk and the objectives behind key performance indicators.
Suppose revenue falls while website traffic remains stable. A tool-focused response may be to build another dashboard. A business-focused analyst investigates conversion rate, order value, customer retention, pricing, channel quality and product mix before deciding what the business should examine next.
3. Analytical judgement
Real data can be incomplete, delayed, duplicated or shaped by how it was collected. Good judgement includes challenging assumptions, recognising bias, distinguishing correlation from causation and being honest about uncertainty. A trustworthy analyst explains what is known, what remains uncertain and what evidence is still needed.
4. Data communication and storytelling
An analysis has limited value if the intended audience cannot understand it. Data storytelling means organising evidence around a decision: what happened, why it matters, what may be driving it and what action should be considered.
Candidates can demonstrate this skill by presenting one project in two ways, first as a technical explanation of methods and then as a concise recommendation for a business stakeholder.
5. AI fluency with human oversight
AI fluency in 2026 means more than knowing how to write prompts. Data professionals must decide when AI is appropriate, provide enough context, verify its outputs and recognise when human review is necessary.
An AI assistant may draft code or summarise patterns, but it can still use the wrong field, misunderstand a metric or produce an unsupported explanation. Employers need analysts who treat AI output as work to evaluate. The professional remains responsible for the recommendation.
6. Adaptability and interdisciplinary thinking
Adaptability is not simply learning the latest tool. It includes transferring knowledge to a new problem, learning an unfamiliar domain and combining evidence from different functions.
In the Pearson and Cognizant research, 69% of HR leaders preferred broad or interdisciplinary backgrounds over deep specialisation when considering early-career talent. This does not reduce the importance of STEM knowledge. It shows why behavioural insight, communication, finance, marketing or operational understanding can make technical analysis more useful.
7. Collaboration and accountability
Data work rarely happens in isolation. Analysts must clarify requirements, question stakeholders constructively, document decisions and accept feedback. Employers also notice whether candidates can explain their choices, what went wrong and how they would improve the work.
How Employers May Evaluate These Skills During Hiring
These capabilities may be assessed through:
• Scenario-based questions with incomplete information
• Portfolio discussions that focus on decisions rather than screenshots
• Case-study presentations for non-technical audiences
• Questions about assumptions, trade-offs and data limitations
• Exercises that include incorrect or questionable AI-generated outputs
• Requests to discuss an analysis that failed or changed direction
• Follow-up questions that test whether the candidate can defend or revise a conclusion
Candidates should practise explaining their reasoning aloud. Interviewers may care as much about the response to uncertainty as the final answer.
Certificates Are Stronger When They Are Supported by Evidence
A course certificate confirms completion. It does not necessarily show how someone performs when facing an ambiguous problem.
A stronger portfolio or professional profile explains:
• The business problem addressed
• The approach selected and why
• The assumptions and limitations considered
• The evidence uncovered
• The recommendation made
• The result, when an outcome is available
• What the candidate would change in a second attempt
This turns a project from a list of tools into evidence of judgement, communication and practical application.
Building Evidence Through the PangaeaX Ecosystem
PangaeaX provides connected ways for data professionals to practise and demonstrate these capabilities. Through CompeteX, participants can work on data challenges and scenario-based problems that assess reasoning alongside technical execution. AuthenX uses portfolio screening and conversational AI-led interviews to evaluate demonstrated experience and skill alignment. ConnectX supports continued learning through professional discussions, resources and community interaction.
Together, the ecosystem supports a practical progression: practise applied skills, authenticate what you can demonstrate and continue learning with other data professionals.
A 2026 Readiness Checklist for Data Candidates
Before applying for your next role, ask:
• Can I turn an unclear request into a well-defined business question?
• Can I explain why a metric matters, not only how it is calculated?
• Can I identify limitations that could change my conclusion?
• Can I verify an AI-generated analysis before using it?
• Can I present the same insight to technical and business audiences?
• Can I support my claimed skills with a project, challenge result or assessment?
• Can I explain what action should follow from my analysis?
Data skills hiring trends in 2026 point towards a broader definition of professional capability. Employers still need people who can query data, build models and create reports. But the candidates who stand out will be those who can decide what matters, test what can be trusted, communicate what should happen next and provide credible evidence that they can do it.