Design thinking is a methodology that supports solution-oriented and creative thinking. We employ design thinking in our data concept workshops and aim at finding new opportunities for your company or organisation. We offer a number of workshops, with a focus that can be tailored to your likes.
Awareness creation. What is Big Data? What is Artificial Intelligence? What is Internet of Things? And what's in it for me?
Use case discovery. Together with you, we will find some use cases to get you started with smart data in your business.
Acceleration projects. Quickly turn your data into value by doing focussed, small projects. Test your data use cases realistically before starting big and expensive projects.
Today's analytics goes beyond the traditional BI data warehouse based analytics and reporting.
Next to traditional analytics, we offer consultancy in time-series analysis, essential for IoT and process mining, and graph analytics, essential for connected data.
Being a data-driven company requires setting up a data architecture that brings together many different data sources, and allows the usage of those data via multiple APIs and user interfaces.
Setting up such an architecture can be a challenge, especially when considering existing data solutions with which to integrate.
Going from a traditional data architecture, often built around relational databases, data warehouses, and OLAP and reporting tools, to a modern data architecture, which adds non-structured and text data, sensor data, event logs, and network data, requires a deep knowledge of what is possible with today's technologies.
Getting relevant information out of raw data requires interpretation of data. Automated algorithms are like smart engines operating on the data.
When applying smart engines to a wide variety of enterprise data, it is often required to customize the smart engines, or to combine them with existing third-party algorithms so they can become even smarter engines.
Getting data in a useful format is the realm of data engineering. From mining and cleaning to stream processing. We prefer to automate and integrate as much as possible.
This is the field of the technology geeks. Big data technologies, such as Hadoop, Neo4J, InfluxDB, Kafka, etc., and languages like Python, R, Scala, Spark, Go, etc.
Data engineers use their technology skills to build the right applications within the bounded business context and architecture.
Visualising data is all about communicating a message. But this can sometimes be quite challenging. It's no longer bar graphs or pie charts; it's about bringing together complex time-related and connected data.