‘Analytical skills’ are a permanent resident in nearly every finance job description, but what does it really mean? It’s not simply about learning another tool or technique; it’s about sharpening how you think – how you frame questions, evaluate evidence and turn complexity into insight that decision-makers can trust.
‘Employers rarely mean just “working with data”,’ says Rina Lakhman ACCA, director of digital at The University of Manchester in the UK. ‘They’re looking for people who can make sense of complexity, challenge assumptions and turn information into sound business decisions.’
As Phil Boden, market director at Robert Half, explains, the emphasis has moved decisively towards interpretation and judgment. ‘For accountants, “analytical skills” means the ability to interpret data, spot trends, question anomalies and turn numbers into insights that support decisions,’ he says, adding that employers increasingly value people who can judge whether an insight is ‘valid, relevant, ethical and commercially sound’.
Analytical skills require professionals to understand the big picture
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For Pamela Curley FCCA, senior delivery lead at Elevate Management Consulting in Australia, analytical strength starts with curiosity and critical thinking, not dashboards or models.
‘A good analyst is someone who can validate a hypothesis, demonstrate critical thinking, identify patterns, trends, relationships and draw logical conclusions,’ she says. Crucially, they ‘simplify complexity, diagnose issues and recommend solutions, tailoring communication to diverse stakeholder audiences’.
Beyond data
One of the biggest misconceptions is that analytical ability equates to data analysis alone. Both Curley and Lakhman challenge that assumption.
Curley warns that technical proficiency without understanding can actively mislead. ‘You could have exceptional technical skills in many analytical tools but not understand the underlying data, and so create dazzling dashboards that do not tell the correct story,’ she says.
Lakhman emphasises that effective analysis depends on context. Analytical skills, she notes, require professionals to understand ‘the big picture and the context of the situation, the business, industry, legislation, key drivers and world events’ before interpreting the data.
This broader analytical lens includes assessing risk and controls, evaluating regulatory impacts, interpreting behavioural signals and exercising ethical judgment in ambiguous situations.
Analysts must adapt how they present findings depending on the audience
Keeping accountable
Technology has transformed how analysis is performed, but not who is accountable for it – and faster analysis raises the stakes. Engaging with stakeholders and understanding how data is generated is as important as the analysis itself, Lakhman says.
‘Technology does not make decisions; it accelerates analysis,’ she says. ‘Accountants still remain responsible for judgment, assurance and ethical oversight, ensuring insights are not blindly accepted without critical evaluation.’
Boden reinforces this point from an employer perspective. ‘Technology can automate reporting, but accountants provide context, assurance and decision-ready interpretation,’ he says.
Behaviours
Strong analysts share common behaviours: they think in a structured way, breaking complex problems into parts and documenting assumptions clearly. They are sceptical of results that appear too neat, and they test insights before presenting them.
Curley highlights the importance of structure. Analysts must be able to ‘break complex information into parts, identify patterns, trends, relationships, evaluate evidence, document assumptions and draw conclusions’. Without that discipline, insight rarely translates into action.
Lakhman adds that communication is inseparable from analysis. Insight only has value if it is understood. Analysts must adapt how they present findings depending on the audience, ensuring recommendations are relevant, clear and commercially grounded.
Key skills
In order to analyse data effectively, the following technical skills are essential:
- data literacy: understanding structures, quality issues and limitations
- tool proficiency: using spreadsheets, business intelligence platforms and automated analytics systems
- numerical and statistical reasoning: recognising patterns, assessing significance and identifying outliers
- data visualisation: telling a clear story using charts and dashboards.
No shortcuts
AI has intensified the need for strong analytical judgment rather than replacing it. While algorithms can process data at scale and identify patterns quickly, they cannot assess appropriateness, ethics or relevance.
‘AI elevates the role of the accountant: from data producer to insight interpreter, ethical guardian and strategy adviser,’ says Lakhman, who splits AI’s impact into ‘positive’ and ‘new responsibilities and risks’. (See ‘AI’s impact on analysis’.)
Curley is explicit about the risks of over-reliance. ‘There have been several scenarios where individuals relied on AI information that has been grossly incorrect,’ she says. Her advice is to frame questions, scenarios and benchmarks independently before turning to AI tools, using them to support – not replace – professional judgment.
Analytical skill is now as much about oversight as output
Boden notes that employers increasingly expect professionals to understand AI limitations and governance issues, not simply use the tools. Analytical skill is now as much about oversight as output.
Being a better analyst
Becoming a better analyst is a continuous process rather than a single skill upgrade. It involves deepening business understanding, refining judgment and being willing to challenge both data and technology.
Strong analysis blends structure with scepticism, numbers with narrative and insight with accountability. In a profession increasingly defined by complexity, those capabilities are not optional extras, but what distinguish trusted advisers from technicians.
Being a better analyst ultimately means thinking better – and ensuring your thinking stands up when it matters most.
AI’s impact on analysis
Rina Lakhman ACCA, director of digital at The University of Manchester, splits AI’s impact into two key areas.
Positive impact:
- greater speed and scale
- pattern detection
- enhanced forecasting
- automation of repetitive tasks.
New responsibilities and risks:
- Professionals must understand how AI tools reach conclusions.
- Poor data leads to poor AI output.
- AI requires careful governance to avoid unfair or misleading results.
- AI cannot replace ethical judgement or contextual interpretation.
