The best things I’ve read in the BI space in 2018

The best things I've read in the BI space in 2018

Over the past few weeks, there are five things that I’ve read in the tech and BI space that I’ve found really interesting.

1. Jen Underwood’s analysis of the 2018 Gartner BI Magic Quadrant – Parts 1 and 2


These two articles provide a very different perspective on Gartner’s 2018 Magic Quadrant because they’re written by someone who isn’t from Gartner – Jen Underwood. Reading Jen’s piece in conjunction with Gartner’s Magic Quadrant adds a lot of flavour and color to the research so you can view the Magic Quadrant from a new perspective.

It’s also a very valuable piece of work if you’re in the process of evaluating multiple tools. It can help you think about all of the vendors rather than just the top right-hand quadrant. For example, her thoughts on where Tableau and Yellowfin are both positioned is interesting.


2. HBR discussion on the what AI really means


There are two articles that I read recently in the HBR that should be read together – Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use and Is “Murder by Machine Learning” the New “Death by Powerpoint”?

The first article talks about the hype of AI and what it actually means for an organization that is going down the path of implementing it. The second examines at AI as the new PowerPoint and talks about the importance of not overdoing it.

When you read these articles together you’re reminded that AI projects can quickly get out of hand. There’s actually a lot of algorithms out there that are simple to use and can be implemented quickly and efficiently so your organization can start getting value rapidly. So, if you’re thinking about using AI it’s important to understand the reasons why. Have a really solid business case and don’t let people implement something just to build out their CV – the AI needs to meet the needs of the organization.


3. Hooked – How to Build Habit-Forming Products by Nir Eyal


While this is a book, it may as well be an article because it’s quite short. The core concept in Hooked is interesting – it gives you an understanding of what brings recurring users back to a product. I found this valuable when thinking about how BI tools are adopted within organizations and why they aren’t used as much as buyers would like them to be.

In a way, this book is a roadmap to improve the adoption of BI. For example, I hadn’t deeply thought about data cadence before. When you’re building a dashboard with monthly data, a user will only look at the dashboard once a month. That’s not enough to be habit-forming – to create a habit people need to use the tool at least once a day. As a vendor, we have to help organizations create a cadence that will bring people continuously back into their analytic tools and dashboards. This means the data has to be interesting and change often.

If you don’t want to read the book you can also watch this video that provides almost as much value.


4. How an Entire Nation Became Russia’s Test Lab For Cyberwar


I actually read this article before the Facebook Cambridge Analytica scandal and the Russian intervention into the US election came to light. At that time, it was largely a dystopian view of technology but it blew me away in terms of the scale of what a potential cyber-war could look like. It talks about the mechanisms for conflict that are being tested today on a real life country – the Ukraine – which is quite astounding.


The article highlights just how fragile our industrial ecosystem is and how complacent we are about the technology that underpins our day to day lives. Everything is connected which means it wouldn’t take much to disrupt it. It’s like the film The Italian Job where they disrupted an entire city just by attacking the traffic lights. In the same way, our technology infrastructure is also very fragile, which means it’s also incredibly easy to disrupt.


5. The Algorithm That Catches Serial Killers


This is a broad consumer piece about an individual who manually collected data on murders across the United States to try and identify serial killers. This article shows the power of aggregated data and what that means for a community.

He literally did all the data collection and analysis by hand – chipping away at it, putting it together and looking for patterns. He’s now proven a hypothesis that can productionized. You could take all the work he’s done, put an algorithm on top of it and add incredible value to law enforcement in an instant. This highlights the low hanging fruit that’s available in many areas of analytics. A really simple analysis can add so much value to organizations and society as a whole.