In the data-driven business world of 2025, data analysts are crucial for making informed decisions based on data from various sources.
They use a combination of programming languages such as Python and R, databases and data warehouses, business intelligence tools such as Power BI or Tableau as well as cloud platforms and big data technologies to effectively implement data analysis, data mining and machine learning.
This article shows the most important data analysis tools that every data analyst should know in order to gain valuable insights for business analytics and digital transformation.
Why the right tools are crucial for data analysts
Data is at the heart of modern companies. Whether for marketing, product development or strategic decisions - well-founded insights from data are crucial to success.
Data analysts process information from relational databases, data warehouses and various cloud platforms in order to derive recommendations for action.
Without the right tools, the analysis of Big Data quickly become unmanageable. From classic spreadsheets to highly complex BI solutions: Choosing the right data analysis tools determines how efficiently an analyst can extract, process and interpret data from various sources.
Programming languages: Python & R
Python
Python is still the standard programming language for data analysts and data scientists. Libraries such as Pandas, NumPy and Matplotlib facilitate data manipulation and visualization. For machine learning Scikit-learn, TensorFlow or Keras are suitable for creating predictions and recognizing patterns in large data sets.
R
R scores particularly well in statistics and data mining. With packages such as ggplot2, dplyr and caret, explorative data analyses and statistical models can be professionally implemented. R is ideal for complex analyses that go beyond standard business intelligence.
Databases & Data Warehouses
| Platform | Real time | Suitable for |
|---|---|---|
| Snowflake | Yes | Large companies |
| BigQuery | Yes | Big data & real time |
| Redshift | Yes | AWS users |
SQL
Structured Query Language (SQL) remains indispensable for working with relational databases. SQL enables efficient querying, filtering and aggregating of data - essential for making well-founded decisions.
Data warehouses
Cloud-based data warehouses such as Snowflake, Google BigQuery or AWS Redshift allow large volumes of data from various sources to be analyzed. They are scalable, performant and form the basis for modern BI and analytics processes.
Business Intelligence Tools
| Tool | Visualization | Cloud | Target audience |
|---|---|---|---|
| Power BI | High | Yes | Beginner |
| Tableau | Very high | Yes | Analysts |
| Qlik Sense | High | Yes | Advanced |
| Looker | Medium | Yes | Cloud focus |
Power BI
Microsoft Power BI is a leading tool for business analytics. Analysts can create interactive dashboards, integrate data from different sources and visualize the results. Power BI supports companies in making data-based decisions.
Tableau
Tableau offers extensive visualization functions and interactive dashboards. It facilitates the interpretation of complex data sets and makes patterns, trends and deviations immediately visible.
Qlik Sense
Qlik Sense is characterized by its associative data modelling, which allows analysts to explore data flexibly.
With interactive dashboards, self-service analyses and powerful visualizations, users can identify correlations in data that remain hidden in traditional BI tools.
Qlik Sense supports companies in quickly analyzing large amounts of data from various sources and improves decision-making.
Looker
Looker, part of the Google Cloud, offers a modern business intelligence platform that is geared towards data-driven workflows.
Analysts can create customized reports, combine real-time data from different sources and embed business insights directly into operational processes.
Looker is particularly suitable for companies that require cloud-based analyses and scalable data models.
Data analysis tools & big data technologies
Apache Spark
Apache Spark is an important platform for processing big data in real time. It enables both batch and stream analyses and is particularly suitable for companies that analyze large volumes of data.
dbt (Data Build Tool)
dbt supports data analysts in the modeling and transformation of data. It facilitates the creation of pipelines that merge and process data from different sources - a key prerequisite for well-founded decisions.
Cloud platforms
Cloud platforms such as AWS, Google Cloud or Azure offer tools for storage, analysis and machine learning. They enable scalable analyses of data from different sources and improve flexibility in data processing.
Classic tools: Microsoft Excel & Co.
| Tool | Data volume | Typical use |
|---|---|---|
| Excel | Small-medium | Ad-hoc analyses |
| Power BI | Medium-large | Dashboards & Reporting |
| Tableau | Medium-large | Interactive visualization |
Despite all modern solutions, Microsoft Excel remains a standard in data analysis.
Pivot tables, Power Query and Power Pivot enable quick analyses and the creation of simple dashboards.
Excel is particularly useful for combining data from different sources and gaining initial insights.
Advanced tools for data science
Machine learning & data science
Analysts who are Data Science and machine learning require additional tools.
Models can be created, tested and implemented using Python, R or specialized ML frameworks. Machine learning makes it possible to predict trends, detect anomalies and automate decision-making processes.
Integration of different data sources
One of the biggest challenges in modern data analysis is the integration of different data sources.
Whether internal databases, cloud services or external APIs - analysts need to ensure that all data is consistent and valid. Tools such as Power BI, Tableau, dbt and cloud platforms make this process much easier.
Business analytics & well-founded decisions
The right combination of tools enables data analysts to generate valuable insights from big data.
By combining BI tools, data warehouses and data analysis tools, patterns can be recognized, forecasts created and well-founded decisions made.
Example: A Digital Transformation Manager can use Power BI to create dashboards that combine data from CRM, ERP and web analytics to optimize marketing campaigns.
Conclusion: the must-have tools for 2025
The selection of the right tools depends heavily on the individual requirements. For beginners, a mix of Excel, SQL and Power BI is recommended. Advanced analysts benefit from Python, R, cloud platforms, big data technologies and BI tools such as Tableau.
Those who specialize in data science should also have knowledge of machine learning and data modeling.
Your next step: further training in data and AI
To be successful in the modern, data-driven world of work, continuous training is crucial. We offer practical training in AI, Data Analytics, Data Science, Business Intelligence and Digital transformation.
With the right tools and sound know-how, you are ideally equipped to turn data into valuable decisions.
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