This article was originally intended for publication late in the year it reviews, but, as they ^[1] say, the best-laid schemes o’ mice an’ men gang aft agley…

In 2017 I wrote more articles ^[2] than in any year since 2009, which was the first full year of this site’s existence. Some were viewed by thousands of people, others received less attention. Here I am going to ignore the metric of popular acclaim and instead highlight a few of the articles that I enjoyed writing most, or sometimes re-reading a few months later ^[3]. Given the breadth of subject matter that appears on peterjamesthomas.com, I have split this retrospective into six areas, which are presented in decreasing order of the number of 2017 articles I wrote in each. These are as follows:

1. General Data Articles
2. Data Visualisation
3. Statistics & Data Science
4. CDO perspectives
5. Programme Advice
6. Analytics & Big Data

In each category, I will pick out two or three of pieces which I feel are both representative of my overall content and worth a read. I would be more than happy to receive any feedback on my selections, or suggestions for different choices.

General Data Articles
	August The Data and Analytics Dictionary
My attempt to navigate the maze of data and analytics terminology. Everything from Algorithm to Web Analytics.
	November & December The Anatomy of a Data Function: Part I, Part II and Part III
Three articles focussed on the structure and components of a modern Data Function and how its components interact with both each other and the wider organisation in order to support business goals.
Data Visualisation
	January Nucleosynthesis and Data Visualisation
How one of the most famous scientific data visualisations, the Periodic Table, has been repurposed to explain where the atoms we are all made of come from via the processes of nucleosynthesis.
	September & October Hurricanes and Data Visualisation: Part I – Rainbow’s Gravity and Part II – Map Reading
Two articles on how Data Visualisation is used in Meteorology. Part I provides a worked example illustrating some of the problems that can arise when adopting a rainbow colour palette in data visualisation. Part II grapples with hurricane prediction and covers some issues with data visualisations that are intended to convey safety information to the public.
Statistics & Data Science
	February Toast
What links Climate Change, the Manhattan Project, Brexit and Toast? How do these relate to the public’s trust in Science? What does this mean for Data Scientists? Answers provided by Nature, The University of Cambridge and the author.
	February How to be Surprisingly Popular
The wisdom of the crowd relies upon essentially democratic polling of a large number of respondents; an approach that has several shortcomings, not least the lack of weight attached to people with specialist knowledge. The Surprisingly Popular algorithm addresses these shortcomings and so far has out-performed existing techniques in a range of studies.
	October A Nobel Laureate’s views on creating Meaning from Data
The 2017 Nobel Prize for Chemistry was awarded to Structural Biologist Richard Henderson and two other co-recipients. What can Machine Learning practitioners learn from Richard’s observations about how to generate images from Cryo-Electron Microscopy data?
CDO Perspectives
	January Alphabet Soup
Musings on the overlapping roles of Chief Analytics Officer and Chief Data Officer and thoughts on whether there should be just one Top Data Job in an organisation.
	February A Sweeter Spot for the CDO?
An extension of my concept of the Chief Data Officer sweet spot, inspired by Bruno Aziza of AtScale.
	September A truth universally acknowledged…
Many Chief Data Officer job descriptions have a list of requirements that resemble Swiss Army Knives. This article argues that the CDO must be the conductor of an orchestra, not someone who is a virtuoso in every single instrument.
Programme Advice
	January Bumps in the Road
What the aftermath of repeated roadworks can tell us about the potentially deleterious impact of Change Programmes on Data Landscapes.
	February 20 Risks that Beset Data Programmes
A review of 20 risks that can plague data programmes. How effectively these are managed / mitigated can make or break your programme.
	March Ideas for avoiding Big Data failures and for dealing with them if they happen
Paul Barsch (EY & Teradata) provides some insight into why Big Data projects fail, what you can do about this and how best to treat any such projects that head off the rails. With additional contributions from Big Data gurus Albert Einstein, Thomas Edison and Samuel Beckett.
Analytics & Big Data
	February Bigger and Better (Data)?
Some examples of where bigger data is not necessarily better data. Provided by Bill Vorhies and Larry Greenemeier .
	March Elephants’ Graveyard?
Thoughts on trends in interest in Hadoop and Spark, featuring George Hill, James Kobielus, Kashif Saiyed and Martyn Richard Jones, together with the author’s perspective on the importance of technology in data-centric work.
and Finally…

I would like to close this review of 2017 with a final article, one that somehow defies classification:

	April 25 Indispensable Business Terms
An illustrated Buffyverse take on Business gobbledygook – What would Buffy do about thinking outside the box? To celebrate 20 years of Buffy the Vampire Slayer and 1st April 2017.

 
Notes

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

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