Data is all around us. Every click, every purchase, every digital interaction generates an astronomical amount of information, forming what is known as Big Data. Without analysis, though, this data tells us nothing. That’s where the Data Analyst comes in. A true master of figures, he or she identifies trends, deciphers behaviour and informs strategic decision-making. .
In today’s fast-paced world, where companies need to anticipate change rather than suffer the consequences of it, this profession has become indispensable. Behind every optimised marketing campaign, every personalised recommendation or every controlled financial risk, there’s a data analysis expert. Their role goes far beyond simply reading statistics: they transform the flow of information into concrete performance levers.
If you’re passionate about analysis, curious about what lies behind the numbers, and ready to play a key role in decision-making, the profession of Data Analyst could well be your calling. Find out how this post shapes corporate strategies and how to kickstart a career in this promising field.
What is a Data Analyst?
A specialist at interpreting data, the Data Analyst collects, processes and analyses vast amounts of information to extract exploitable trends. Their aim: to help companies make strategic decisions based on hard facts rather than mere hunches.
Unlike the Data Scientist, who develops advanced predictive models, the Data Analyst concentrates on the study of historical data. They transform this information into clear, visual reports, often in the form of interactive dashboards, to facilitate decision-making.
The explosion in data is truly unprecedented: according to IDC forecasts, the global volume of data out there is set to reach 175 zettabytes by 2025, up from the 33 zettabytes generated in 2018. Faced with this avalanche of data, companies’ need for experts who can make sense of these information flows is greater than ever.
What is the Data Analyst’s role in the company?
The Data Analyst is the intermediary between data and decision-makers. Their role is essential in sectors as varied as finance, marketing, e-commerce, logistics and healthcare. They help companies to:
- Identify market trends and adjust their strategies accordingly.
- Optimise internal process performance through data analysis.
- Facilitate decision-making by making data comprehensible and usable.
- Improve the user and customer experience by studying the behaviours and preferences of these groups.
Leveraging data has become a real driver of growth: according to a McKinsey study conducted in 2022, B2B companies using data-driven growth engines record above-market growth and EBITDA increases of 15 to 25%.
What are the main tasks of a data analyst?
To extract relevant information, the Data Analyst follows a rigorous process. From data collection to the production of clear, visual reports, each step is essential in order to guarantee reliable analyses that can be used by decision-makers.
Data collection and cleansing
Prior to any analysis, the Data Analyst must ensure that the data is reliable. He or she extracts it from various sources (SQL databases, CRM, Excel files, etc.), then cleans it by removing inconsistencies and duplicates.
Analyse data to generate insights
Data analysts use statistical and analytical tools to:
- Identify trends and correlations.
- Identify anomalies or opportunities for improvement.
- Optimise the performance of marketing campaigns or internal processes.
Create dashboards and reports
The results must be clear and exploitable by decision-makers. The Data Analyst designs interactive dashboards to monitor key KPIs in real time.
These key performance indicators help teams to measure their objectives, identify trends and adjust their strategies, thanks to dynamic, intuitive visualisations.
Collaboration with decision-making teams
He works closely with marketing, finance, logistics and management to align analyses with the company’s strategic objectives.
How do I get to be a data analyst?
With the rise of data-driven strategy, the job of Data Analyst is attracting an increasing number of professionals. There are several routes that lead to this position, whether through a traditional academic career or retraining.
What is data-driven strategy?
Data-driven strategy is an approach that relies on data analysis to guide a company’s decisions and strategies, thereby optimising its performance and competitiveness.
Training and qualifications
The profession of Data Analyst can be accessed via several career paths:
- 3 to 5 years of higher education in statistics, IT or management.
- A qualification from an engineering school specialising in data science or business intelligence.
- Masters degree specialising in big data or data analysis.
Certifications and further training
To stand out in the job market, recognised certifications are a plus:
- Google Data Analytics Professional Certificate
- Microsoft Certified: Data Analyst Associate
- IBM Data Analyst Professional Certificate
Platforms such as DataCamp, Coursera and OpenClassrooms also offer tailored training courses.
What qualities does a Data Analyst need?
The profession of Data Analyst relies on a combination of technical skills and personal qualities. A good analyst must know how to manipulate figures, interpret trends and communicate results to decision-makers effectively. These skills enable them to make the most of data and guide corporate strategies.
Hard skills
- Database skills: know how to query and structure the kind of data stored in relational databases.
- Statistics and data analysis: understand concepts such as correlation, regression and statistical distributions, so that you can identify reliable trends.
- Data visualisation: transform numbers into clear, comprehensible graphs, to facilitate decision-making.
- Programming: know how to code in such a way as to automate analyses and process large volumes of data efficiently.
Soft skills
- Analytical and logical thinking [Saut de retour à la ligne]A Data Analyst must be able to interpret numbers, spot patterns and identify cause-and-effect relationships .
- Rigorousness and attention to detail [Saut de retour à la ligne]Decisions taken on the basis of analyses must be reliable. Close attention to detail helps to avoid errors and guarantee high-quality results.
- Ability to explain things clearly and teach others [Saut de retour à la ligne]A good analyst knows how to adapt his or her discourse to make information accessible to non-specialist colleagues and partners, be they managers, marketers or engineers.
- Curiosity and a keen interest in technological developments [Saut de retour à la ligne]The world of data is constantly evolving. Data Analysts need to keep abreast of new trends, tools and methodologies to optimise their work.
How much does a Data Analyst earn?
In Switzerland, the profession of Data Analyst is a well-paid one, thanks to the high demand for analytical expertise in many sectors. Salaries vary depending on experience, technical skills and field of activity.
Data Analyst and Data Scientist: what are the differences?
Although people often confuse these two professions, there are some fundamental differences between Data Analysts and Data Scientists.
The Data Analyst specialises in exploiting existing data to understand past and present phenomena. They provide reports and dashboards to decision-makers.
Data Scientists go further, developing predictive models and exploiting artificial intelligence capabilities (machine learning, deep learning) to anticipate trends and automate the analysis of complex data.
If a Data Analyst wishes to progress to the role of Data Scientist, he or she will need to acquire advanced skills in programming (Python, R), machine learning algorithms and applied mathematics.
To find out more about the job of Data Scientist, take a look at our job description: data scientist.
What is machine learning?
Machine learning is a branch of artificial intelligence that enables algorithms to learn from data in order to make predictions or automate tasks without being explicitly programmed to do them.
What is deep learning?
Deep learning is a sub-field of machine learning that uses multi-layer artificial neural networks to analyse large amounts of data and recognise complex patterns, such as in computer vision or natural language processing.
What tools do data analysts use?
Data Analysts need to master several categories of tools so that they can analyse and visualise data effectively.
Data analysis software: Excel, R, Python
- Excel: indispensable for rapid analysis, data management and the creation of pivot tables.
- R and Python: these languages are increasingly used for more advanced analyses, particularly for handling large volumes of data and automating analytical tasks.
Visualisation tools: Tableau, Power BI
Data visualisation tools enable raw data to be transformed into clear, interactive graphics, to facilitate decision-making.
- Tableau: acclaimed for its ease of use and its power in terms of graphics.
- Power BI: widely used in business, especially in Microsoft environments.
Databases: SQL, BigQuery
- SQL (Structured Query Language): the essential language for querying and manipulating relational databases.
- BigQuery: used to analyse large volumes of data in the cloud; Google is a notable user.
Mastery of SQL is often a fundamental requirement for a data analyst, as most companies store their data in relational databases.
Which industries recruit the most Data Analysts in Switzerland?
In Switzerland, demand for Data Analysts is growing rapidly. Companies in many sectors are looking to exploit their data to optimise their performance and competitiveness.
- Banking and finance
Banks and insurance companies use data analysis to:
– Assess financial risks and improve investment management.
– Detect fraud by analysing suspicious behavior.
– Personalise customer offers with tailor-made recommendations.
- Health and the pharmaceutical industry
The medical field is increasingly harnessing the power of data science to analyse millions of patient data sets, optimise treatments and accelerate clinical research.
- E-commerce and retail
Online retailers use data analysis to:
– Understand consumer behaviour and improve the user experience
– Optimise inventory management by anticipating demand
– Personalise product recommendations to boost sales
- Industry and logistics
Industrial companies use data to improve production line management, plan equipment maintenance and reduce operating costs.
- Technology and start-ups
Companies in the digital sector are at the forefront of the adoption of a data-driven strategy. Data analysts play a key role in optimising algorithms and improving digital services.
What are the challenges and issues facing the profession of Data Analyst today?
The job of Data Analyst is constantly evolving, and entails several major challenges.
- Data quality management
The data collected is often incomplete, erroneous or poorly structured. Data cleansing and validation are an important part of the Data Analyst’s job.
- Data protection and GDPR compliance
With the rise of regulations such as the GDPR (General Data Protection Regulation ), companies need to ensure that information is used ethically and in a secure way. The Data Analyst must be aware of these issues and ensure compliance with the regulations in force.
- Rapidly evolving technologies
New tools and techniques are constantly emerging. A good Data Analyst needs continuous training to keep up to date with the latest innovations in Big Data and artificial intelligence.
- Automation and artificial intelligence
The rise of machine learning andautomation tools is gradually changing the role of the data analyst. If they are to remain relevant, they must learn to use advanced algorithms and understand how these tools can optimise their work.
- Turning data into concrete action
The Data Analyst’s greatest challenge is not just to analyse numbers, but to extract actionable insights that will have areal impact on decision-making.
What career options are available?
The profession of Data Analyst offers a wide range of career prospects. After a few years’ experience, there are several options open to you:
- Lead Data Analyst
After acquiring experience, a Data Analyst can rise to the position of Lead Data Analyst, where he or she will supervise a team of analysts and help define analytical strategies.
- Data Scientist
By developing skills related to machine learning and artificial intelligence, a Data Analyst can progress to the next level and become a Data Scientist, focusing on the design and training of advanced predictive models.
- Chief Data Officer (CDO)
This position involves managing a company’s overall data strategy and supervising teams of analysts and Data Scientists.
- Data Analytics Consultant
Some Data Analysts choose to become consultants, helping different companies implement data-driven strategies.
- Data Engineer
By specialising in data engineering, a Data Analyst can progress to the role of Data Engineer, responsible for the Design and Maintenance of Data Infrastructures.
The role of Data Analyst is one of the most highly sought-after positions in the digital world, and continues to grow at a rapid pace. Thanks to the key role such experts play in decision-making and performance optimisation, this job represents an excellent gateway into the field of data science.
If you’re passionate about numbers, curious about exploring large amounts of data and enjoy solving problems, the job of Data Analyst is for you!
Would you like to develop your career in data analysis? Discover the opportunities we have on offer and join Qim info !
