The Hark Blog

iot project planning

Why do IoT projects fail and what can be done to ensure success?

The internet of things (IoT) is best known for unlocking new business opportunities by transforming the way people and machines work. In today’s world, data is the key to a business’s success; IoT deployments can provide critical data to any device such as a phone, tablet or computer within seconds to help businesses improve performance.

Hark at EMEX 2022

Energy Management Lessons we learnt at EMEX 2022

Last week, we took a trip to the London ExCel Centre for EMEX, the net zero and energy management expo. The event this year stood out in a way that previous years couldn’t have; the pressure of the energy crisis added a resounding feeling to the event which spurred some important conversations. I wanted to write something to reflect on some of the excellent questions, demos and chats that took place on the Hark wind turbine stand (as seen in the video below).

Splitting data

Splitting data into ‘train’, ‘validation’ and ‘test’ sets

When developing and deploying machine learning models, it’s important that we split the dataset into ‘train’, ‘validation’, and ‘test’ datasets. This protects against an overfitted model, and helps ensure results are generalised. In this blog post we will look in to how to split the data, and why.

project management

8 Project management methodologies for delivering IoT deployments

Internet Of Things (IoT), is no longer a tech buzzword but an advanced everyday reality describing a multitude of technologies that connect assets and objects to the internet. Before you start deploying IoT solutions or deployments, it’s vital to understand what project management methodology to use, as all IoT projects are different and will require slightly different approaches.

puzzle piece missing - missing data

How to deal with missing data

In Data Science, it’s pretty rare to come across a perfect dataset; data will frequently be of poor quality. It could have been recorded incorrectly, or it can be missing completely, which can be caused by poor data collection or storage methodologies.

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