data analyst
Snapshot
Unlock valuable insights and drive data-informed decisions as a data analyst. This role blends technical skills with business acumen, making it a rewarding career for those who enjoy problem-solving and uncovering patterns.
As a data analyst, you'll be at the heart of understanding what data means for your organization. Your days will involve importing data from various sources, ensuring its accuracy and consistency, and then transforming it into meaningful information. You’ll use analytical tools and algorithms to model data, identify trends, and interpret results, ultimately supporting strategic business goals. Expect to create clear and compelling visualizations, such as graphs, charts, and dashboards, to communicate your findings to stakeholders.
- • Import, inspect, clean, and transform data from diverse sources.
- • Develop and apply data models to analyze trends and patterns.
- • Validate data integrity and ensure data sources are reliable.
Unlock valuable insights and drive data-informed decisions as a data analyst. This role blends technical skills with business acumen, making it a rewarding career for those who enjoy problem-solving and uncovering patterns.
Could data analyst fit you?
Answer three quick questions. This is not a full assessment — it is a teaser to help you decide whether to compare your profile.
Do you enjoy tasks that require Analytical Thinking?
Do you enjoy tasks that require Attention to Detail?
Do you enjoy tasks that require Initiative?
Future Outlook for data analyst
The outlook for data analyst is exceptionally stable. While AI tools will assist with daily tasks, the core of this role relies on human judgment, resulting in a high resilience score of 81.4%.
How are these scores calculated?
The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.
How could data analyst change as AI adoption grows?
Human judgement, trust, and context remain strong protectors for this role.
How could data analyst change as AI adoption grows?
Human judgement, trust, and context remain strong protectors for this role.
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
This role remains strongly human-led where define data quality criteria depends on trust, nuance, and real-world judgement.
Where AI may become a co-pilot
AI is more likely to assist supporting tasks such as establish data processes, documentation, search, and workflow coordination.
Tasks most exposed to automation
Automation pressure appears selective rather than broad, with the strongest signal currently coming from AI / machine learning.
Detailed Analysis Vital Signs, AI Vectors & Megatrends
Show more Close
Vital Signs, AI Vectors & Megatrends
Vital Signs
AI Exposure Vectors
0-100%Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks
Exposure to content generation, creative augmentation, and large language model tools
Exposure to workflow automation, decision-support software, and process digitisation
Exposure to physical automation, robotics, and sensor-driven task displacement
Megatrend Signals
0-100%Model-derived scores. Indicates structural exposure to megatrends, not direct demand.
Technical Details
NexFuture™ v2.0 combines O*NET ability and activity profiles with ESCO skill group distributions and six global megatrend signals. Scores are probabilistic estimates, not guarantees. See the NexFuture™ Methodology White Paper for full details.
What people in this role usually do
Digital Technology
A typical day as a data analyst
09 09:00 · Morning define data quality criteria
10 10:30 · Mid-morning establish data processes
12 12:00 · Midday integrate ICT data
14 14:00 · Afternoon manage data
15 15:30 · Late afternoon normalise data
17 17:00 · Wrap-up perform data mining
Task order is illustrative. Individual days vary.
-
business analytics
The disciplines and technologies for solving business problems through employing quantitative methods such as data analysis and statistical models.
-
data mining
The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
-
data models
The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.
-
data quality assessment
The process of revealing data issues using quality indicators, measures and metrics in order to plan data cleansing and data enrichment strategies according to data quality criteria.
-
documentation types
The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.
-
information categorisation
The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
-
normalise data
Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.
-
use data processing techniques
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
-
establish data processes
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
-
perform data mining
Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.
-
use databases
Use software tools for managing and organising data in a structured environment which consists of attributes, tables and relationships in order to query and modify the stored data.
-
integrate ICT data
Combine data from sources to provide unified view of the set of these data.
-
apply statistical analysis techniques
Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.
-
analyse big data
Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
-
handle data samples
Collect and select a set of data from a population by a statistical or other defined procedure.
-
collect ICT data
Gather data by designing and applying search and sampling methods.
-
interpret current data
Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.
-
execute analytical mathematical calculations
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
-
define data quality criteria
Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.
-
manage data
Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardisation, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialised ICT tools to fulfil the data quality criteria.
Skill DNA
Work personality traits and values that define this role
See whether this role fits your Career DNA
Take the free Career DNA assessment to see how data analyst aligns with your interests, work style, and future path. In less than 10 minutes, you will get a personalized fit signal and a roadmap for what to do next.
Scan to continue on mobileGrowth Pathways & Similar Roles
Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.
Where does data analyst fit?
Similarity scores based on skill overlap from ESCO data.
Frequently asked questions
- What kind of technical skills are essential for a data analyst?
- While specific tools vary, a strong foundation in data manipulation (e.g., SQL), spreadsheet software (e.g., Excel), and data visualization tools (e.g., Tableau, Power BI) is generally expected. Familiarity with statistical analysis and programming languages like Python or R is also beneficial.
- Is a formal degree always required to become a data analyst?
- A degree in a quantitative field (e.g., statistics, mathematics, computer science, economics) is often preferred, but not always mandatory. Strong analytical skills, demonstrable experience with data analysis tools, and a portfolio of projects can also be valuable, especially for career changers.
- How does the role of a data analyst differ from that of a data scientist?
- Data analysts typically focus on describing and interpreting existing data to answer specific business questions. Data scientists often build predictive models and develop new algorithms. While there's overlap, data analysts are more focused on reporting and actionable insights from current data, whereas data scientists are more involved in creating new analytical approaches.