Analytics applicationsA briefer, more lightweight, analytics applications post today as it’s been a busy week.

I’ve also realised it’s been a while since I last curated some content from the wider Customer Insight blogosphere, so here are some recent posts I thought you might find interesting.

In keeping with our monthly theme, focusing on application of analytics, I’ve pulled together a few posts that caught my eye.

I hope you find this short collection interesting & it helps to reveal how broad a field even just analytics is (let along broader customer insight).

Data Visualisation, beyond the popularity

Like many insight leaders, I’m a real fan of data visualisation done well. We have shared several winning examples in past posts. However, ever since roles dedicated to this skill started to be created (data artist et al), there has been a risk of the normal ‘hype cycle‘ of inflated expectations.

Counter to a lot of fashionable content on this topic, in the post below, Elijah Meeks (from Netflix) offers an interesting perspective on how data visualisers can feel set up for failure & end up leaving such roles. There is much to learn here…

visualisation
If Data Visualization is So Hot, Why Are People Leaving?

From graph databases to graph analysis

In a previous post, reporting back on table discussions with Customer Insight leaders, I mentioned the popularity of graph databases. Prior to that discussion, I was not as familiar with that technology. There are numerous situations where an ability to faster analyse networks of relationships can be very useful. However, most commercial clients have already invested in their customer databases and so can be reluctant to create another data store.

So, I was pleased to see this post on how to use Python to conduct graph analytics over a relational database. In this, more technical post, Konstantinos Xirogiannopoulos shares useful detail about the packages that help & how to code graph analytics in this growingly popular coding language. I hope it, at least, opens your eyes to some of the potential of graph analytics.

District Data Labs – Graph Analytics Over Relational Datasets with Python

The analysis of interconnection structures of entities connected through relationships has proven to be of immense value in understanding the inner-workings of networks in a variety of different data domains including finance, health care, business, computer science, etc.

Deep Learning gets practical – hiding your screen from your boss!

Finally, as it’s felt like a long week, let’s have a bit of fun. I couldn’t resist including this post on how to use deep learning (including facial recognition algorithms) to blank your screen when your boss approaches. Lots of fun detail and amusing graphics from Hironsan.

Of course, I’m not advocating you doing something at work that needs to be hidden! 😉 But, I thought the worked explanation was both fun & gives a better understanding of how some of the hype surrounding deep learning might just get practical in a host of real world situations.

Deep Learning Enables You to Hide Screen when Your Boss is Approaching

Introduction When you are working, you have browsed information that is not relevant to your work, haven’t you? I feel awkward when my boss is creeping behind

Anything to share?

That’s all from me this Saturday morning. Hope you have a great weekend & enjoyable week.

Have you seen any interesting content on the web that you think would help other analytics leaders? Are you an aspiring blogger, who’d like to join our panel of guest bloggers, to share some of your relevant experience?

If so, we’d love to hear from you – please use the comments box below.