iStock photoAfter spending time with insurance marketers this week, it was interesting to get back to reading some techie insight guides.

With so much media time often given over to hype & over-inflated expectations, it was encouraging to find good examples of practical & realistic online content.

Across the world’s of data, analytics & research, there are so many online publications sharing well curated content. Here are a few insight guides that caught my eye this week and why I think they are a useful antidote to all the ‘fluff’ spouted by those trying to sell you new software.

To start with, let’s consider the least romantic of these insight disciplines, data. It’s fashionable to talk about predictive analytics, modelling or attractive visualisations, even when the topic is actually ‘big data’. But the reality for most analysts & data managers is lots of ‘bad data’. Often the majority of the time taken to complete new analysis is work on cleaning data. So, it;s encouraging to see a guide published on just that topic. Here is a handy reference guide for analysts looking to tackle a variety of quality problems with their data sources:

Quartz/bad-data-guide

bad-data-guide – An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.

Most of the other technical areas of customer insight seem sexy by comparison, but still rely on that foundation. Let’s turn next to the other end of the spectrum. Data Scientists, when accurately identified, are now expected to have skills in machine learning as well as coding & statistics. However, outside of IT departments, less is shared to define this domain. I recall working on Neural Networks, Case Based Reasoning, Genetic Algorithms & Fuzzy Logic systems in the early 90s, but that apparent hey day of Artificial Intelligence (AI) never realised its potential (at least in the world of Financial Services). Today’s new wave of AI applications are more technology than algorithm focussed and digital integration makes their potential applications more obvious. But there is still a dizzying array of software and in-app functionality to choose from. For that reason, even here at the ‘sexy’ end of Insight land, a guide would help. For that reason I was pleased to see this article from Shivon Zilis, with an infographic to help position the players in this market:

The current state of machine intelligence 2.0

A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence.

Finally, I would be remiss in my mission (to encourage holistic Customer Insight), if research was not considered as well. Some commentators would have you believe that all the new thinking & developments are happening in the worlds of data & analytics. But that is not the case. Innovations in enabling technologies, as well as learning from psychology are continually refining this field as well. So, how should a research leader stay up to date in their field. Like many disciplines, trends in best practice often follow developments in the US market. One of the leading barometers of what is happening there, Greenbook’s Research Industry Trends (GRIT) quarterly report, is well worth reading. In this post they usefully summarise the key themes and share an infographic (which you could use when briefing your team), as well as a link to the full 70 page detail:

GRIT Report for Q3 – Q4 2015 Is Here: Download It Now!

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Hope at least one of those three guides was helpful for your insight challenges. What about you? Do you have a guide that you’ve found useful for keeping up to date as an insight leader? If so, please share with us, so we can all develop professionally in this key field.