Jason Mackinlay

One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could

One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could

I agree Tim - the first generation phase in IOT should be a gimme for most applications. And not particularly interesting or informative. I think the challenge we face is that some of the things we want to use IOT data streams for are actually emergent properties. And we often need to be collecting the gimme data products from several elements in a system before we can start to interpret the bigger picture. For tyres, I can see feedback mechanisms for fuel efficiency and suspension wear based on what an instrumented tyre is reporting. And if you apply this to the transport industry,

@Andrew at MEA you've made a great point here and it's easy to see how your approach differs from that of other companies. Most of my work relates to using data rather than generating it and I place a value on good data (specifically data with integrity, currency, completeness, correctness, quality). One common classification of the data associated with a task is to consider the data products in three ways: the raw data collected or generated, the analysis and deliverables given to the client, and the knowledge generated from doing the work. It is good practice to describe in the contract how

Big data is often confused with lots of data. This topic raises the issue of how to handle the growing volumes of data, and the 'Flood of data' knowledge note shows just how large this can be, but it there is further discussion that can be had on the differences between handling high volume data as a technical issue, and conducting data analytics of a big data set. The link can also be drawn to IOT design whereby the purpose for sensing and observing a particular data event should be a deliberate outcome of the design process to inform a decision. I see reports of data stores being overwh