[BlueSense image]

Industry: Facility Management, Retail, Public Transport, Events (soccer, music, trade fairs)
Tech: Python, Java, MongoDB, Kafka, Spark, Hadoop, HDFS, Hive, Storm

BlueSense measures the behaviour of people within buildings, in real-time, using mainly Wi-Fi sensors. It includes state of the art analytics to understand crowd density, crowd movement patterns, dwell times and occupancy levels, both descriptive and predictive. This system was deployed at various clients in retail, public transport, facility management and a football stadium.

This was my main project during my time in the Big Data & Analytics team at KPMG. I started out doing mostly engineering and gradually transitioned into leading the analytics effort as of October 2016. However, in a small start-up like team, scrambling to build a product and find product-market fit before cash runs out, it’s impossible to have neatly separated responsiblities like in a large corporate. This has given me broad experience in:

It was an amazing and sometimes nerve-wrecking experience. BlueSense has been incubating within KPMG NL from early 2014 until June 2017. This eventually culminated into a spin-off, which unfortunately did not gain enough traction.

Since this project pivoted multiple times, featured subprojects are handled separately: