Data Engineering & Analytics
Collect, manage and analyse IoT or IIoT data to enable data-driven decision making and artificial intelligence. Data engineering made easy with ithinx.
Data engineering as the foundation for big data and AI analyses
Most companies are also data producers. Over the course of time, however, independent and confusing data islands form in many companies. These data islands can become a problem if the data accumulates there in an unsorted manner. Without a standardised data management process, no added value can be obtained from this data. In order to make successful business decisions on the basis of this data, the data must be cleanly organised in a process.
Our services
We support you on the way to your robust data engineering process, which will optimise your data flow and provide the foundation for more successful analyses and valuable insights.
Consulting
Betreuung Our team advises you on data engineering and data analyses. We evaluate your current data structure and help you to define a data maturity model tailored to your company.Data Engineering as a service
ausstattung Data engineering through standardised data pipelines for IoT and IIoT use cases. Our experts create the structured data architecture that suits your needs and implement data lakes and data warehouses.Data Analysis
graph Our experts work with you to develop analysis requirements so that you can improve your business decisions. We provide you with a customised analysis portal for this purpose.Maintenance
Maintenance We maintain your data pipelines as part of an SLA contract: from logging and monitoring to any necessary bug fixes.
Increase your data quality and data usability
If a company wants to deal with BI and AI, it should always be considered that approx. 70-80 per cent of the time must be invested in data acquisition and preparation of raw data (data engineering) alone. If you want to save time and avoid errors, benefit from our experience in this area and let us advise you on our data engineering services.
Your benefits with data engineering at a glance
Improved data quality
qualitaetssicherung Usually, companies collect data and want to use it, but often use a variety of sources, data collection centres or formats. Data engineering can transform the data into a standardised and usable format.Data scaling
skalierbarkeit Data validation and data cleaning can no longer be scaled manually once a certain amount of data is reached. Good scaling can only be achieved through automated cleaning and formatting of the data.Data-based decision-making
Kooperation Icon Automated and standardised data processing enables your company to make data-driven business decisions.
Your journey to greater data maturity
If you want your company data to achieve a high level of quality and standardisation, a proper data maturity model should be tailored to your company, as this is an essential step in becoming data-driven
Find out why data maturity is important and how you can evaluate it in your company in our expert article.
More exciting insights into data maturity and processing
Data Maturity and Engineering What is data maturity and why is assessing it important?
data Automated data pipelines for processing IoT data
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