Big data and machine learning can be used for identifying patterns and forecasting developments that people are not capable of doing. With our systematic approach to data analysing, we create valuable knowledge based on our customers’ data in order for them to make the right decisions in critical situations.

big data

Systematic develops and delivers software and system solutions for customers within the health sector, the defence, the energy sector, and the Danish, public IT infrastructure. The different solutions support our customers by making the right decisions in critical situations. What is common to our solutions is that they are centred on vast amount of data - defined as big data.

How we work with big data 

Big data is the term for the vast amount of data collected by IT systems every day around the world. The vast amount of data may represent important information and interesting connections in relation to your customers, products, or employees, but access to data alone does not entail value to your company. A clear purpose is necessary as well as a systematic and analysing approach in order to understand the connections between the many types of data. This is why we are working with big data across our business units – of great value to our customers.

Through analysis, big data enables identifying hidden patterns and hereby forecasting what is most likely to happen in a concrete sequence of events. For instance, we are able to forecast the optimal course of treatment of patients within the health sector, which leads to better planning, less overcrowding, and hereby higher patient security at the hospitals.

At Systematic, we work very committed with big data. In fact, we have established several projects with the purpose of identifying different ways of analysing our customers’ data. For example, In the Northern Denmark Region, we have made advanced data models that are able to forecast the overcrowding at the hospitals several days ahead and with less errors compared to the personnel when using analogue methods. With our systematic approach to data analysing, we support the different departments around the hospital with forecasting the load of patients, which secures the necessary staffing at the hospitals – of great benefit to both personnel and patients.

Machine learning helps us forecasting future events

With the use of machine learning for analysing previous collected data (historical data), the system is automatically able to find an algorithm, which later can identify patterns in the historical data. On the basis of the patterns, it is possible to set up a model that manipulates the new data followed by the ability to forecast future events more precisely and further ahead in time compared to what is possible for humans. Afterwards, we can measure and assess how accurate the forecasting was and adjust the algorithm in order to ensure that the accuracy continues to improve. In this way, the collected data paves the way for new knowledge, which can be used for critical decision making in the future.

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As an example of Systematic’s committed effort within big data and machine learning, we are taking part in a big collaboration with a range of universities and private as well as public organisations in Denmark. The project’s name is DABAI, and the goal is to leverage big data in order to become better at solving business and societal challenges.

Read more about the DABAI project and collaboration here.