First 100 days of Trump tweets

Screen Shot 2017-05-03 at 11.36.21.pngPurely out of curiosity and because I hadn’t seen it anywhere else, I took Donald Trump’s tweets from his first 100 days in office (source) and made this Wordle out of them.

The bigger the word, the more it was used.

I wanted to do a comparison to Obama, look at sentiment, tweets and engagement over time but unfortunately, I have no time and there’s been a lot of other analysis out there, two listed below:




A Kodak moment is coming for organisations that don’t get Big Data

You’d be forgiven for thinking that the data hype is over.  In 2011, McKinsey & Company published a report called Big data: The next frontier for innovation, competition and productivity which forewarned of Big data taking over the world.

Six years later and does it feel like data has taken over the world? Well, I think it depends on where in the world you are.

Those companies and organisations that were early adopters probably think so. Everywhere you look, data is being exploited by smart startups to disrupt traditional businesses. Except that I’m not sure many people outside of the early adopters think that it’s the exploitation of data that’s doing the disruption.

And this leads to big issues. When CEOs and leaders think that Netflix, Uber, Airbnb are just Apps and not data, it’s easy for those people to think that they need an app to compete. They’re wrong.

Most organisations are sitting on mountains of data (or have the ability to get it) that they’re simply not exploiting because they don’t realise that this is what the sharks are feeding on. They will be eaten up in short time if they don’t start collecting/using it soon.

I was recently at a conference where over one hundred of the finest minds in data, in business and organisations, gathered together to discuss and share their experiences within their own organisations.


Corinium CAO Europe conference


The people at the conference were at various stages in their organisation adopting a data-centric business model. The huge theme that came out of it as a blocker to anyone talking about why things weren’t happening or why things did happen was senior stakeholder support.

Unless support for data-centricity (not just data projects) comes from the very top, organisations will struggle to do anything but pay lip service. They are ripe for being gobbled up by younger ‘apps’ who get it. Maybe not today, maybe not tomorrow, but soon.

The big danger here isn’t individual businesses and organisations failing. Plenty of companies have failed to grasp the significance of emerging technologies in the past (Blockbuster, Kodak being the famous ones) and the world hasn’t collapsed.

What’s different here is that when multiple organisations fail in the new world, there’s usually just one or two huge winners who snap up the consumers due to the relative ease and speed they can spread without traditional advertising, building and staff.

This is bad for business, competition and ultimately the consumer, no matter how friendly and do-no-evil the newcomers seem.

The impression that I got from the conference is that everyone, even in large organisations who ‘get it’, are struggling. Linkedin failed to realise the significance of its own data until someone fought for a small ‘people you may know’ section on the homepage that made return visits go through the roof. And Linkedin is an example where they already have the hard work of the data done.

Linkedin failed to realise the significance of its own data until someone fought for a small ‘people you may know’ section on the homepage that made return visits go through the roof. And Linkedin is an example where they already have the hard work of the data done.

And Linkedin is an example where they already have the hard work of the data done. The governance, the skills, the technology were all in place, they just hadn’t productised it. A vast number of incumbents haven’t sorted out the hard bits because they don’t even believe that they need to.

So it was five years since McKinsey wrote that report. Perhaps it was a little early but actually, I don’t think so. We can already see the impact around us if we think of disruptors as being data centric rather than just apps, and incumbents as not.

Just as Kodak thought that the digital camera was about technology rather than photographs.

Kodak is back! (well worth watching the clip below)



Data in five senses

Following from yesterday’s post about creating Real World Data Objects I started to think about the other ways that data can be consumed.

I was at a talk at the BBC last year where Fank Swain’s project was discussed. He was registered blind and was adapting his hearing aids in order to be able to ‘hear’ wireless signals.

This got me thinking about the other senses and how they could be used to consume data in different ways with a view to exploring how we bridge the data literacy gap.


Although I couldn’t see anything specifically about turning data into smells, I did come across the below (I suppose the first one is kind of doing it in an I/0 kind of way).

I was more interested though in nuanced project, for example, is anyone experimenting with making different smells to indicate different states in a live data set i.e I smell bananas if there’s heavy traffic on my route to work?

This device gives off a metallic smell when your personal data is being compromised

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The Smelly maps aims to map the smells within a city 

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NASA scientists have been listening to data for a while now 

And here’s what that Wifi sounds like

FedEx make a custom soundtrack to your delivery based on mode of transport, dimensions and destination here


Although quite firmly being of interest to artists at present. There is a potential for Real World Data Objects to bring ‘touchers’ into contact with data

Below is Nathalie Meibach’s incredible sculpture made of meteorological data

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This article reminds us that there have been data sculptures around for thousands of years and contains this image of the Tohuko Japanese earthquake

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PhD students, Adam Eydelnant and JarlathByrne Rodgers opened a lab to get people to taste data from a “data drink machine”

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Taste of data is an ongoing project where statistics and cuisine are combined together.

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Well, this is the obvious one isn’t it. I’ll just leave the Information Is Beautiful awards page here

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Making data come alive – Real World Data Objects

I’m currently working on my first 3D, touchable, pass-aroundable, real world data object. (I’m calling these things RWDOs as I haven’t seen anyone else naming this yet but there’s probably another word out there).


The idea of creating a real object made from data came to me whilst out on a run and, like all good ideas, it’s been done before. It was easy enough to find some examples through a quick Google search but I haven’t yet seen anyone writing these up as ‘a thing’ or who has tested their ability to engage users/organisations who would otherwise be uninterested in data.

Here is an artwork by Andrej Boleslavský which shows visits to a web page over the course of a year.

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And here’s one from Volker Schweisfurth who is looking at doing very similar things with his data. You can read more about his work here.

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Why am I doing it?

Well I’m into data. But more specifically I’m interested in finding ways to make people as engaged in data as a storytelling medium as I am.

At the moment the data we consume mostly comes in spreadsheets, charts, written articles and graphics. Whilst this serves the function of informing those people who are already data literate, I think that we have a long way to go to reach those who aren’t.

People who aren’t data literate are isolated from a good deal of facts, making it harder for them to make informed decisions.

I don’t think this is about how intelligent/high achieving someone is. The CEO of a company is as much in danger of skipping over great data insights and leading her company to disaster as is the voter who would no longer be hoodwinked by the unscrupulous tabloid.

Alternative representations of data will, I believe, help in some part to bridge the gap between those who can interpret data and those who can’t.

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What am I doing?

Well I’m hoping to make something out of the tweets that Donald Trump has sent out and compare those to an interesting other source (such as Obama’s over the same time period.

I’m going to show the results on this blog and hopefully, it’ll all be done within the next couple of months.


The rise of Analytics in organisations

Next week, I’ll be attending the Chief Analytics Officer conference in London

As organisations and businesses slowly wake up to the increased importance of their data in their future survival, it’s inevitable that the role of the person who takes on the strategic responsibility for that data will rise in importance too.

The conference organisers say “it is predicted that within 5 years the Chief Analytics Officer will be one of the most strategic roles within any organisation.”

Analytics roles have grown steadily over the past two years as a proportion of the overall UK IT jobs market, from 3.96% to 5.08% with salaries increasing by around 7% overall but with certain roles managing to jump 30 – 60% (source – IT Jobs Watch search).

With over 150 executives from all over Europe attending, it’s going to be a good place to get the pulse of the challenges facing organisations today. The key themes for the conference are:

  • THE DATA DRIVEN C-SUITE – The Role. Evaluating data analytics investment, prominence and acceptance in the boardroom.
  • CULTURE and LEADERSHIP – The Human Element. Defining, fostering and embedding a culture of data analytics and insight based decision making. Democratising analytics to foster insight and co-operation throughout the organisation.
  • THE INTELLIGENT ENTERPRISE – The Defining Factor. Driving innovation, agility and business value. Monetising data to deliver strategic worth to the business.

I’ll be blogging from each session attended, the keynotes and panel discussions and updating live from the event.




Three right ways and one wrong way to use your data

In my experience, most organisations don’t have a clear plan or structure in place for using the data that they have to its full (or even some) of its potential.

If you find the wrong way of using data below familiar to you, don’t worry, you’ve plenty of company. Acknowledging that you’re making mistakes is the first step on the road to being brilliant, and starting to think about the right ways to use data will put you above 95% of your colleagues.

The wrong way

Picking stats that make you look good/ignoring stats that don’t

Or attribution bias.

I once sent out a chart showing visitors to a website, post-redesign. It looked impressive. A straight arrow indicated that visitors to the site had doubled in the week post-launch. In fact, it looked too impressive and I made it clear in my email to a small group of people to treat it with caution, don’t forward on, and that I’d need to look into it.

I quickly found out that visits to the site were being double counted and the actual rise was more like what I’d modeled. A moderate increase which would grow over the next 3-6 months. I sent out chart number 2.

Here’s the lesson. The first chart was distributed to the whole business even after I’d informed the recipients of the correction. People loved it. The second chart, well, it was awkward when I brought it up in future meetings, was seen by all I thought needed to know and never forwarded on.

Successful data travels far and wide whereas data showing uncomfortable truths doesn’t. Due to the wide variety of data available, it’s possible to make any project look like a success. Visitor numbers low? Show how engaged they were! Awkwardly low retweets? Stick in a single tweet from someone saying how much they loved it! Senior management is rarely data literate enough to question it and so lessons are not learned.

Of course, collecting the data is one thing; accepting what the data tells us is another. We have both worked with all too many organizations where “data-driven decision making” is code for contorting the facts until they reveal whatever senior management expects to see.

-Harvard Business Review, Why organizations don’t learn. Nov 2015

Senior management is rarely data literate enough to question it and so lessons are not learned and the cycle continues. No one likes to see their flaws, it’s uncomfortable but in the areas that we’re forced to learn from mistakes revealed in data in a clear and transparent way, it produces brilliance – think airline pilots, surgeons, the military etc.

Although the above disciplines have been forced into facing up to their data at every level, there’s no reason you shouldn’t do the same even if you may not be dealing with life or death situations.

The right ways to use your data

1. Solve an existing problem

The challenges that an organisation faces are often informed by data and yet people don’t rely on data to solve it (or throw around that one big stat to back up what their biased gut instinct is telling them to do).

A data-literate approach enables better decisions to be made that go against an organisations instincts.

2. Finding a problem/opportunity

Having a team of people within the organisation who can explore data, hiring people who have data skills or training up existing teams at every level can reveal problems and opportunities at all levels. Not all data needs to be big or to result in big decisions being made. Data is probably being used in your organisation badly right now.

Even without knowing that the answers to common insights gleaned from data can be the start of something profound happening within an organisation. Insights such as:

Lots of people search for x

The number of people visiting x is dropping/rising

People in X region do this

People who do X then do Y

3. Education

There is a huge disconnect between the traditional marketing personas that we have in our organisations and actual real behaviour online. Educating people around online behaviours can give a much deeper insight into how people behave and not just how ten people say broadly how they behaved during an interview.

Having employees be able to explore the actual real-life behaviour of the data of an organisation is a powerfully reflective tool and is being used by all kinds of organisations including those that famously rely on gut instinct such as News outlets

Of course, it’s not just giving people access to data but encouraging a data-centric approach to working, ensuring people are supported in using that data and encouraging open discussions about the quality and relevance of the data and any insights or assumptions drawn from it. Opening the data up is a good first step though and trusting people to be smart enough to use it wisely is a decision you’ll have to make.

The lessons

1. Face facts – make unbiased data analysis part of your daily routine

2. Solve the problems that data has revealed through exploring that data further

3. Use data to find issues and opportunities

4. Putting data into the hands of people at all levels can be a powerful tool. Trust people to figure it out.

5. Just use it! (but not for the wrong way)

Eat all the pies: A digital growth strategy

Below, I divide growth strategies (strategies to grow an organisations online presence) into four types.

1. Bigger slice  


Satisfying existing visitors.

You have 100 visitors to your online offerings. One of these visitors is a customer, doing the thing(s) that give value to the business (assuming that your business isn’t just to get visitors).

It’s reasonable when thinking about how to grow, to start with what you have and try to get two customers. It feels reasonable anyway. Just as reasonable as Kodak making better/cheaper film to compete with the digital camera revolution.

Pro – You now have two customers for really little investment.

Con – You’ve reached your ceiling and are stagnating

2. Bigger pie  


Creating new customers.

Now we’re talking. What’s everyone else doing now? How can we get a piece of that (through takeovers, partnerships, entering new markets)? You take your 100 visitors (and one customer) and try to increase that by, say 100. So now you have 200 visitors and two customers. Cool.

It’s surprising how many organisations don’t get to this stage. Often the barrier is that they’re doing too well. Or feel that they’re unique in some way, shielded from the digital disruption that’s happening to them.

To those that do, content strategy and platform innovation come are the tools. Let’s shift our content focus, capture these personas, give them what they want.

Pro – You have two customers (for a little more investment)

Cons – You have reached your ceiling and are stagnating

3. Future pie 


Shaping what customers will want in the future.

So you have your 100 visitors (and one customer) and you completely ignore them. You disregard everything they’re doing on your site/your competitor’s sites and rip up the rule book of how to go about meeting their needs.

In five years time, you have 1,000,000 visitors and they’re all customers because, well, they are now in some way.

Or maybe not. Because Your CEO looked at the innovation in year two and didn’t like it or, did like it but didn’t think her customers would. Well, of course, they wouldn’t. The customers don’t exist yet. Still, the project got binned and the lead walked from the organisation in frustration and founded a start-up and took all your customers in ten years.

Pro – You have 1,000,000 customers

Con – You don’t. Because the project got canceled and you lost all your customers in the meantime.

4. All the pies 


The only way to grow/exist in the long term is to look at being a bit of all three of the above. Serve your existing visitors better whilst looking for new ones with an eye (and the boldness) to look at the future.

This requires the most effort and the biggest culture change within an organisation. The majority within are probably using the ostrich pie strategy – sticking their head in the sand and saying their customers won’t want to change. And there’s plenty of evidence every day to confirm that bias. They haven’t changed yet. But then eventually the they become smaller and smaller. And it’s too late. And you’re dead.









Colombia v Columbia: How many people get it wrong?

tldr; I reckon about a quarter of people get it wrong but I’m open to being corrected…

I was recently in Colombia (not Columbia) and whilst I was out there I noticed wristbands and T-Shirts etc for sale with the “It’s Colombia not Columbia” branding similar to below.

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Credit: Miami Herald 

Colombians were understandably annoyed by how their country was consistently miss-spelt and so this campaign was an attempt to highlight and change this. When I saw the campaign, I was curious to know the numbers behind the miss-spelling.

The tragic news of the Colombian plane crash gave me a chance to look try to do a quick and dirty analysis of the scale of the problem.

The plane crash being such a big, worldwide news event meant that the search data for that time around people searching for Colombia V Columbia would be likely to be overwhelmingly about Colombia, the country rather than Columbia the university, clothing company or the state.

To see quantify the scale of the problem and look at the worst culprits I compared Colombia and Columbia in Google trends for a timespan of one day.

Screen Shot 2016-11-29 at 13.49.26.pngscreen-shot-2016-11-29-at-13-50-15screen-shot-2016-11-29-at-13-49-36

If we assume that most searches around this day were about the plane crash and therefore should have been ‘Colombia’ then you could see that most people were getting it right when they searched with a hefty chunk getting it wrong. But that’s a big assumption, that most people were searching for the plane crash.

So I then looked at just those News searches in Google trends by narrowing down the category. That’s much less of an assumption.

Below shows searches for Colombia (blue) and Columbia (red) over the period of one day (the spikes are when news broke of the crash). On breaking news, the red peaks at around 35 relative to the blue’s 100 so 35 as a percentage of 35+100 = 26% = that feels like a fairly good finger in the air estimate of the volume of people who miss-spell Colombia. 

Screen Shot 2016-11-29 at 13.54.53.png

A quick Google search showed that even respected news organisations get it wrong over the spelling of Colombia. Interestingly, when I clicked on the links on Google’s In the news results, below, it looked like the Columbia spelling had been corrected on the Business Insider site (but not in its page title), on the Mirror (which presumably had initially published with Columbia unless Google autocorrected) and was still miss-spelt on the Manchester Evening News.

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Google autocorrecting or news editors correcting?



10 amazing things your internet search data is used for

In the days before the internet, libraries were a much more important source of free information for many people. What they lent, when and where from was, until recently, recorded by the Public Lending Right (PLR) body in the UK.

I (vaguely) remember the data that the PLR used to collect being used by the media as a gauge of public interest in a particular topic, book or genre however, as it was by its nature out of date by the time it was published, it was of limited use.

Google is our librarian now and the data that it gathers not only tells us what people were interested in, but what in particular (a bit like being able to tell which specific paragraph of a book people were interested in, rather than just the fact they were interested in that book).

The data also tells us when specifically they were interested in that topic, where they were, if they were satisfied with the information they received and even what demographic they were likely to belong to.

Coupled with all of this extra information, we also know what they wanted last week, rather than waiting for six months and, as the internet provides information on virtually any topic, we have a full on data set that details virtually anything that anyone has ever been interested in knowing.

So what I’m trying to say is that the data collected by Search engines and ISPs when you search for something online is a bit of a treasure chest. That little query string at the end of your URL may not mean much taken on its own (apart from to those closest to you), but when fed into many, and aggregated, and sliced, analysed and interpreted, it becomes one of the most powerful sources of potential knowledge and insight into human behaviour that we have today.

In my opinion, that knowledge is largely untapped due to lack of knowledge of its existence, the expense of getting more than a limited view of the data through third party tools and partly, perhaps, due to the lack of high profile use cases.

There are some researchers and organisations however that are making use of this data, mostly in Health, Finance and Marketing, paving the way for others.

I’ve made a start in listing them here. If you know of any other examples, feel free to comment and I’ll add them into the list.

1. Predicting unemployment

In March 2013, four academics from Beijing’s Renmin and Tsinghua universities published a paper detailing how using search engine data had outperformed traditional methods of predicting unemployment .

Similar results were achieved by German researchers, from Bonn university in May 2009

2. Knowing when people are abusing drugs

In November 2012, a paper was published by the Clinical toxicology (Philadelphia) journal detailing how internet search data could be used to detect outbreaks of people abusing drugs known as “bath salts”.

3. Measuring public awareness of Erectile Dysfunction

The Journal of the British Association of Urological Surgeons, BJU international published paper in December 2012 looking at public awareness of erectile dysfunction in Ireland, following a series of public awareness campaigns

4. Predicting outbreaks of Dengue fever

In August, 2011 a paper from PLOS Neglected tropical diseases concluded that “Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems.”

5. Predicting outbreaks of the flu have long been predicting flu outbreaks and have a sleek website that really brings the data to life

6. Making loads of money from the stock market

Okay, so there’s a little bit of supposition in that but there have been studies linking search data to stock market activity and if anyone knows how to use data to make money, it’s got to be stockbrokers, right?

7. Helping computers understand humans

Microsoft looked at using search data to help machines understand human speech in this paper

8. Predicting house prices

A study by researchers from MIT said “We found evidence that queries submitted to Google’s Search Engine are correlated with both the volume of housing sales as well as a house price index”

9. Knowing when we’re more likely to spend

The Bank of England were reportedly using search data to help them understand consumer confidence in the UK

10. Selling you things online

Google and other search engines have long made their search data available for advertisers to research what their website visitors are most likely to search for and so shape their Ads, content and even website architecture accordingly