Our solution

Data Processing

If your organization depends on data to make important business decisions, data processing is essential. It easily becomes too complex, especially when looking at tough-to-solve issues like multi-vendor data sources, advanced real-time KPI calculations, or requirements like massive scalability. When data processing becomes complex, Hendrikx ITC is your partner.

Steps of Data Processing

Step 1

DATA Collection

After data validation, the collection of data starts. Data is often extracted directly from the source itself. Data sources can be multi-vendor, which is why we work vendor-agnostic.

Step 2


After the ETL solution extracts and authenticates the data, raw data is processed.  Data transforms into a desired analytical output format that is machine-readable.

Step 3


The last phase of the processing is the data loading part. The chosen ETL solution is moving the transformed data to the target data warehouse.

Common data processing challenges

The three steps of data processing are often surrounded by several challenges. 

Domain Knowledge

A challenge for many organizations is the lack of computational knowledge within the team. This is required to understand the part data processing is responsible for in their organization.

Once issues are detected, insufficient domain knowledge is available to improve the data processing to the desired results.

Adoption Issues

Does your organization dare to operate 100% data-driven? From operational staff to C-level executives, subject experience or ego could stand in the way of relying solely on data to make decisions.

While there are advantages and disadvantages to either 100% data-driven and gut feeling, we trust in the power of data to support the organization in reaching its goals.

Organizational resistance to adopting data as one of the most valued assets could prevent it from reaching desired goals and thus trouble data processing decisions.

    Validation failures

    Due to the massive amounts of data involved during the data processing cycle, a wide range of errors can occur in the analyzed information. It takes only a small mistake to cause huge issues.

    Failure to validate the data leaves decision-makers and end-users prone to mistakes. We do not settle for less than 100% data accuracy.  Data validation is a vital part of data processing.

      Proper data interpretation

      Interpretation is a wide area, but one could start with asking the following questions;

      • Are the desired data goals clearly defined for the organization?
      • Are end-users able to truly understand what is shown?
      • Can they act on the data and make the right decisions?
      • Is the data shown valid?
      • Do anomalies influence decisions?
      • Do the end-users use the correct data from the wide variety of available data to act upon?

      Regularly ask these questions in your organization. It also prevents the dependence upon data to make wrong decisions, though recognizing this issue could be very hard.

      Perhaps training of staff is required to improve interpretation and decision making. Our experts have proven degrees in data-related studies and interpreted many data in a variety of cases.

      Integrating multiple data sources

      Data is collected from many data sources. They could be from the same vendor. But what about the situation where an organization has to choose a multi-vendor strategy?

      Reasons for implementing a multi-vendor data source strategy are; reducing costs by replacing current sources, reducing dependency on a single vendor, or swapping network equipment due to legacy performance.

      There is a risk of vendor lock-in. Vendor lock-in could cost your organization lots of money or prevent it from getting a complete data overview. It is why an organization benefits from an open platform.

      During data processing, we allow multiple vendor data sources to integrate within our data platform. It prevents vendor lock-in and allows your organization to execute the desired strategy.

      Encryption and security

      When organizations fail to encrypt their data the right way, hackers potentially gain access to valuable and often privacy-sensitive information. It could have many unwanted consequences. To combat security challenges, it is of utmost importance to encrypt your data. Not only in your database, but also during your data processing.

      Scalability and performance issues

      Organizations are struggling to keep up with exponentially growing amounts of data being generated. All this data needs to be processed. The incremental amount of data also increase the dependency upon it.

      It means that they have to look towards the future the organization will face with their data. Many existing (legacy) data platforms cannot keep up. Popular apps like Microsoft’s Excel struggle to perform when data gets beyond huge. Many platforms resolve to data clustering to improve speeds. While this could offer performance increases, a concession was made by not processing all the data.

      Having an open ecosystem that does not make concessions and features massive scalability would help being prepared for the future. Failure to invest in the right data platform could lead to preventing future revenue or losing competitive advantage.

      Meet future demands by providing real-time data processing, blazingly fast dashboards, and advanced KPIs calculated on a whim. 

      We have commonly seen these problems in the organizations we have helped. Separate actions can help solve the challenges. To fix most of them at once, we also developed our sophisticated data platform to help organizations.

      Not yet convinced?

      Get a free demo with sample data to see how we can help your organization in the field of data processing, data science, and data platforms. If custom software is required, we got your back as well.

      how we help with data processing

      New to data processing? Need help with your existing network to process data? Our experts know how to help your organization with solving data processing challenges.

      We work with models like CRISP-DM and love to solve your challenge(s)!