Which skills will a successful data analytics team actually need?


Digitization has led to more and more IT systems generating more and more data in companies. For some this is a curse, for others a blessing. As a matter of fact, there is great potential in Big Data. Properly processed, these large amounts of data can be used to gain insights that can impact management decisions in a meaningful way. However, this requires expertise in data analytics. Data analytics is a process for analyzing, evaluating, and visualizing data. It uses statistical methods and algorithms to transform data into meaningful information that can in turn help companies make smarter decisions and improve business processes.

The benefits of data analytics are numerous. Some of the main advantages are:

  1. Improved decision making: Data analytics enables companies to make decisions based on data rather than hunches or gut feelings. This allows them to make smarter and better decisions.
  2. Optimization of business processes: Data analytics can help uncover bottlenecks in business processes and identify scope for optimization. This enables companies to design their processes more efficiently and save costs.
  3. Identification of trends and patterns: Data analytics can help identify patterns and trends in corporate data. Future developments can thus be predicted and opportunities can be identified that might otherwise have been overlooked.
  4. Personalization of offers and services: Data analytics can help companies to better understand the needs and preferences of their customers. This enables them to offer personalized offers and services that are better tailored to the needs of their customers.


In order to exploit the full potential of Big Data analysis, evaluation and visualization, usually several specialists work in a data analytics team. In the following, we will look at the different roles involved.

Roles in a Data Analytics Team

Data Engineer

The Data Engineer is responsible for creating, maintaining, and updating databases and for data security. Technologies and tools such as SQL, Hadoop, Apache Spark, Spark Streaming, Kafka, Delta Lake, HDFS, Databricks or NoSQL are used. Data Engineers are also responsible for integrating data from external sources and ensuring that databases are compatible with the current needs of the business. Finally, the Data Engineer hands off the data to the Analytics Engineer.

Analytics Engineer

The Analytics Engineer is a specialist who focuses on the development and implementation of data analytics systems and tools. The responsibilities of an Analytics Engineer can vary by company and industry, but generally include:

  1. Data Preparation: the Analytics Engineer collects, extracts, transforms, and loads data from various sources into a database or data warehouse to ensure that the data is of high quality and suitable for analysis.
  2. Data Modeling: The Analytics Engineer designs and implements data models to ensure that data can be analyzed effectively and efficiently. This involves identifying relationships between data and transforming them into a suitable schema to meet analysis objectives.
  3. Data Analytics: the Analytics Engineer performs complex analysis to derive insights from data that can improve or support business operations. In doing so, he may apply statistical or mathematical models.
  4. Automation: The Analytics Engineer automates repetitive data processing tasks and processes to save time and resources and ensure that data analysis is always up to date.

At the end of the data preparation process, the Data Analyst comes into play.

Data Analyst

Finally, the Data Analyst is responsible for evaluating and presenting the findings from the analyses. A Data Analyst usually has knowledge of data analysis tools such as Excel, SQL, Tableau or Power BI and is something like the “translator” of data sets. He interprets the data provided by the Data Engineer and the Data Scientist, and his perspective tends to have a business focus and is less technical. Data analysts use techniques such as data mining to extract relevant data and create visualizations thereafter. The purpose of the latter is then to help management understand the results and make better decisions.

By the way, we at ruhr.agency are not only specialists when it comes to implementing lean and scalable ERP systems. If you want to exploit the full potential of Big Data, we have a team of Data Engineers, Data Analysts and Analytics Engineers at your disposal – just contact us!