Big data in companies: challenge and opportunity at the same time
Digitalization has led to more and more IT systems generating more and more data for companies. For some it is a curse, for others a blessing. The fact is that big data has great potential. When properly processed, these large amounts of data can be used to gain insights that can positively influence management decisions. However, this requires expertise in the area of data analytics.
What is data analytics?
Data analytics is a process for investigating, interpreting, and visualizing data. It uses statistical methods and algorithms to transform data into meaningful information, which in turn can help companies make well-founded decisions and improve business processes.
Benefits of data analytics for companies
Improved decision making:
Data analytics enables companies to make decisions based on data and not based on guesswork or gut feeling. This enables them to make more informed and better decisions.
Optimizing business processes:
Data analytics can help uncover weaknesses in business processes and identify optimization opportunities. As a result, companies can make their processes more efficient and save costs.
Identification of trends and patterns:
Data analytics can help identify patterns and trends in corporate data. In this way, future developments can be predicted and opportunities identified that might otherwise have been overlooked.
Personalization of offers and services:
Data analytics can help companies better understand the needs and preferences of their customers. This allows them to offer personalized offers and services that are better tailored to the needs of their customers.
Roles in a data analytics team
In order to exploit the full potential of analyzing, interpreting and visualizing big data, several specialists usually work in a data analytics team. In the following, we would like to shed light on the different roles there are.
Data Engineer
The data engineer is responsible for creating, maintaining and updating the databases as well as for the security of the data. It uses technologies and tools such as SQL, Hadoop, Apache Spark, Spark Streaming, Kafka, Delta Lake, HDFS, Databricks or NoSQL. Data engineers are also responsible for integrating data from external sources and ensuring that the databases are compatible with the company's current requirements. Finally, the data engineer transfers the data to the analytics engineer.
Analytics Engineer
The analytics engineer is a professional who specializes in the development and implementation of data analysis systems and tools. The duties of an analytics engineer may vary by company and industry, but in general, they include:
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 high-quality and suitable for analysis.
Data modeling:
The analytics engineer designs and implements data models to ensure that the data can be analyzed effectively and efficiently. This involves identifying relationships between data and transforming them into a suitable scheme in order to achieve the analysis goals.
Data analysis:
The analytics engineer performs complex analyses to gain insights from the data that can improve or support business operations. In doing so, he can use statistical or mathematical models.
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.
Data Analyst
The data analyst is ultimately responsible for interpreting 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, with his perspective being more of a business focus and less technical. The data analysts use techniques such as data mining to extract relevant data and then create visualizations. The latter should then help management understand the results and make better decisions.
Our approach at ruhrdot.
By the way, we at ruhrdot. are not just specialists when it comes to implementing lean and scalable ERP systems. If you want to exploit the full potential of big data, we will also provide you with professional support with a team of data engineers, data analysts and analytics engineers: just contact us!
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