Data Tech & AI

‘Our data engineers and data scientists work side-by-side. It makes us super-efficient and agile at the same time.’

‘In the world of data-tech companies, In2Intel is quite an odd duck. Often, data businesses focus on solely one particular discipline, like data integration, data science or application integration. But we’re different. We master all three skills, including the overlap between the three components.

 

'We gather data, clean them, organize and quantify them. These ordered data sets provide analyses and insights we visualize into manageable dashboards. When that’s all set, your dashboards will show you clear points of required actions. It’s the exact information you need to set up efficient toolsets for your day-to-day business.’

 

‘At In2Intel, these three data disciplines practically merge seamlessly. We manage the entire spectrum. Our data engineers and data scientist work side-by-side. It makes us super-efficient and agile at the same time. That all-round dedication, from business intelligence to productionalizing data and providing end-to-end solutions, makes us agile, efficient, proactive and in close contact with our clients.’

- Noud, Data Engineer

1

Domain

Data Integration

In this section, we take a closer look at the field of data engineering, creating traditional ETL (Extract Transform Load) and modern ELT (Extract Load Transform) processes. An important aspect here is the data modeling, where we strive for data normalization and optimization for the back-end of all dashboards. The main goal of data integration is to unlock and link small and large (big data) data sources, with the ultimate result of combining this data in a data warehouse that serves as the basis for your reports.

2

Domain

Data-Analysis/Science

In the data analysis and science phase, we use the work done in the data integration phase. In this section, dashboards are developed for various departments, including management (Business intelligence). In addition, advanced analyzes are performed, for example to evaluate the results of a marketing campaign and to make forecasts for future campaigns. Finally, in this section we come to the 'science' aspect, in which machine learning models are developed for predictions and advanced detection.

3

Domain

Application Integration

While the data integration phase is mainly about collecting data, this section focuses on bringing the data out. This means that the data must be transferred from system A to system B, for example from the data warehouse to a financial, CRM or marketing system (end-to-end solutions). In addition, the models developed in the 'data science' section must be brought to production, so that they can be displayed in an app or on a website (Productionize data), for example.

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