The first half of my planned thoughts on Hurricanes and Data Visualisation is called Rainbow’s Gravity and was published earlier this week. Part two, Map Reading, has now also been published. Here is an unplanned post slotting into the gap between the two.
The image above is iconic enough to require no introduction. In response to my article about the use of a rainbow palette Quora user Hyunjun Ji decided to illustrate the point using this famous painting. Here is the Mona Lisa rendered using a rainbow colour map:
Here is the same image using the viridis colormap :
The difference in detail conveyed between these two images is vast. I’ll let Hyunjun explain in his own words :
In these images, the rainbow color map might look colorful, but for example, if you take a look at the neck and forehead, you observe a very rapid red to green color change.
Another thing about the rainbow colormap is that it is not uniform, especially in terms of brightness. When you go from small to large data, its brightness does not monotonically increase or decrease. Instead, it goes up and down, confusing human perception.
To emphasise his point, Hyunjun then converted the rainbow Mona Lisa back to greyscale, this final image really brings home how much information is lost by adopting a rainbow palette.
Hyunjun’s points were striking enough for me to want to share them with a wider audience and I thank him for providing this pithy insight.
According to its creators, viridis is designed to be:
Colorful, spanning as wide a palette as possible so as to make differences easy to see,
Perceptually uniform, meaning that values close to each other have similar-appearing colors and values far away from each other have more different-appearing colors, consistently across the range of values,
Robust to colorblindness, so that the above properties hold true for people with common forms of colorblindness, as well as in grey scale printing, and
This first article is not a critique of Thomas Pynchon‘s celebrated work, instead it refers to a grave malady that can afflict otherwise health data visualisations; the use and abuse of rainbow colours. This is an area that some data visualisation professionals can get somewhat hot under the collar about; there is even a Twitter hashtag devoted to opposing this colour choice, #endtherainbow.
The [mal-] practice has come under additional scrutiny in recent weeks due to the major meteorological events causing so much damage and even loss of life in the Caribbean and southern US; hurricanes Harvey and Irma. Of course the most salient point about these two megastorms is their destructive capability. However the observations that data visualisers make about how information about hurricanes is conveyed do carry some weight in two areas; how the public perceives these phenomena and how they perceive scientific findings in general . The issues at stake are ones of both clarity and inclusiveness. Some of these people felt that salt was rubbed in the wound when the US National Weather Service, avid users of rainbows , had to add another colour to their normal palette for Harvey:
In 2015, five scientists collectively wrote a letter to Nature entitled “Scrap rainbow colour scales” . In this they state:
It is time to clamp down on the use of misleading rainbow colour scales that are increasingly pervading the literature and the media. Accurate graphics are key to clear communication of scientific results to other researchers and the public — an issue that is becoming ever more important.
At this point I have to admit to using rainbow colour schemes myself professionally and personally ; it is often the path of least resistance. I do however think that the #endtherainbow advocates have a point, one that I will try to illustrate below.
Many Marvellous Maps
Let’s start by introducing the idyllic coastal county of Thomasshire, a map of which appears below:
Of course this is a cartoon map, it might be more typical to start with an actual map from Google Maps or some other provider , but this doesn’t matter to the argument we will construct here. Let’s suppose that – rather than anything as potentially catastrophic as a hurricane – the challenge is simply to record the rainfall due to a nasty storm that passed through this shire . Based on readings from various weather stations (augmented perhaps by information drawn from radar), rainfall data would be captured and used to build up a rain contour map, much like the elevation contour maps that many people will recall from Geography lessons at school .
If we were to adopt a rainbow colour scheme, then such a map might look something like the one shown below:
Here all areas coloured purple will have received between 0 and 10 cm of rain, blue between 10 and 20 cm of rain and so on.
At this point I apologise to any readers who suffer from migraine. An obvious drawback of this approach is how garish it is. Also the solid colours block out details of the underlying map. Well something can be done about both of these issues by making the contour colours transparent. This both tones them down and allows map details to remain at least semi-visible. This gets us a new map:
Here we get into the core of the argument about the suitability of a rainbow palette. Again quoting from the Nature letter:
[…] spectral-type colour palettes can introduce false perceptual thresholds in the data (or hide genuine ones); they may also mask fine detail in the data. These palettes have no unique perceptual ordering, so they can de-emphasize data extremes by placing the most prominent colour near the middle of the scale.
Journals should not tolerate poor visual communication, particularly because better alternatives to rainbow scales are readily available (see NASA Earth Observatory).
In our map, what we are looking to do is to show increasing severity of the deluge as we pass from purple (indigo / violet) up to red. But the ROYGBIV  colours of the spectrum are ill-suited to this. Our eyes react differently to different colours and will not immediately infer the gradient in rainfall that the image is aiming to convey. The NASA article the authors cite above uses a picture to paint a thousand words:
Another salient point is that a relatively high proportion of people suffer from one or other of the various forms of colour blindness . Even the most tastefully pastel rainbow chart will disadvantage such people seeking to derive meaning from it.
Getting Over the Rainbow
So what could be another approach? Well one idea is to show gradients of whatever the diagram is tracking using gradients of colour; this is the essence of the NASA recommendation. I have attempted to do just this in the next map.
I chose a bluey-green tone both as it was to hand in the Visio palette I was using and also to avoid confusion with the blue sea (more on this later). Rather than different colours, the idea is to map intensity of rainfall to intensity of colour. This should address both colour-blindness issues and the problems mentioned above with discriminating between ROYGBIV colours. I hope that readers will agree that it is easier to grasp what is happening at a glance when looking at this chart than in the ones that preceded it.
However, from a design point of view, there is still one issue here; the sea. There are too many bluey colours here for my taste, so let’s remove the sea colouration to get:
Some purists might suggest also turning the land white (or maybe a shade of grey), others would mention that the grid-lines add little value (especially as they are not numbered). Both would probably have a point, however I think that use can also push minimalism too far. I am pretty happy that our final map delivers the information it is intended to convey much more accurately and more immediately than any of its predecessors.
Comparing the first two rainbow maps to this last one, it is perhaps easy to see why so many people engaged in the design of data visualisations want to see an end to ROYGBIV palettes. In the saying, there is a pot of gold at the end of the rainbow, but of course this can never be reached. I strongly suspect that, despite the efforts of the #endtherainbow crowd, an end to the usage of this particular palette will be equally out of reach. However I hope that this article is something that readers will bear in mind when next deciding on how best to colour their business graph, diagram or data visualisation. I am certainly going to try to modify my approach as well.
The story of hurricanes and data visualisation will continue in Part II – Map Reading, which is currently forthcoming.
For some more thoughts on the public perception of science, see Toast.
I guess it’s appropriate from at least one point of view.
“It is a truth universally acknowledged, that an organisation in possession of some data, must be in want of a Chief Data Officer”
— Growth and Governance, by Jane Austen (1813) 
I wrote about a theoretical job description for a Chief Data Officer back in November 2015 . While I have been on “paternity leave” following the birth of our second daughter, a couple of genuine CDO job specs landed in my inbox. While unable to respond for the aforementioned reasons, I did leaf through the documents. Something immediately struck me; they were essentially wish-lists covering a number of data-related fields, rather than a description of what a CDO might actually do. Clearly I’m not going to cite the actual text here, but the following is representative of what appeared in both requirement lists:
Solid commercial understanding and 5 years spent in [insert industry sector here]
The above list may have descended into farce towards the end, but I would argue that the problems started to occur much earlier. The above is not a description of what is required to be a successful CDO, it’s a description of a Swiss Army Knife. There is also the minor practical point that, out of a World population of around 7.5 billion, there may well be no one who ticks all the boxes .
Let’s make the fallacy of this type of job description clearer by considering what a simmilar approach would look like if applied to what is generally the most senior role in an organisation, the CEO. Whoever drafted the above list of requirements would probably characterise a CEO as follows:
The best salesperson in the organisation
The best accountant in the organisation
The best M&A person in the organisation
The best customer service operative in the organisation
The best facilities manager in the organisation
The best janitor in the organisation
The best purchasing clerk in the organisation
The best lawyer in the organisation
The best programmer in the organisation
The best marketer in the organisation
The best product developer in the organisation
The best HR person in the organisation, etc., etc., …
Of course a CEO needs to be none of the above, they need to be a superlative leader who is expert at running an organisation (even then, they may focus on plotting the way forward and leave the day to day running to others). For the avoidance of doubt, I am not saying that a CEO requires no domain knowledge and has no expertise, they would need both, however they don’t have to know every aspect of company operations better than the people who do it.
The same argument applies to CDOs. Domain knowledge probably should span most of what is in the job description (save for maybe the three items with footnotes), but knowledge is different to expertise. As CDOs don’t grow on trees, they will most likely be experts in one or a few of the areas cited, but not all of them. Successful CDOs will know enough to be able to talk to people in the areas where they are not experts. They will have to be competent at hiring experts in every area of a CDO’s purview. But they do not have to be able to do the job of every data-centric staff member better than the person could do themselves. Even if you could identify such a CDO, they would probably lose their best staff very quickly due to micromanagement.
A CDO has to be a conductor of both the data function orchestra and of the use of data in the wider organisation. This is a talent in itself. An internationally renowned conductor may have previously been a violinist, but it is unlikely they were also a flautist and a percussionist. They do however need to be able to tell whether or not the second trumpeter is any good or not; this is not the same as being able to play the trumpet yourself of course. The conductor’s key skill is in managing the efforts of a large group of people to create a cohesive – and harmonious – whole.
The CDO is of course still a relatively new role in mainstream organisations . Perhaps these job descriptions will become more realistic as the role becomes more familiar. It is to be hoped so, else many a search for a new CDO will end in disappointment.
Having twisted her text to my own purposes at the beginning of this article, I will leave the last words to Jane Austen:
“A scheme of which every part promises delight, can never be successful; and general disappointment is only warded off by the defence of some little peculiar vexation.”
Most readers will immediately spot the obvious mistake here. Of course all three of these requirements should be mandatory.
To take just one example, gaining a PhD in a numerical science, a track record of highly-cited papers and also obtaining an MBA would take most people at least a few weeks of effort. Is it likely that such a person would next focus on a PRINCE2 or TOGAF qualification?
My objective in this brief article is to compare how much time and effort is spent on certain information-related activities in an organisation that has adopted best practice, compared to what is typical in all too many organisations. For the avoidance of doubt, when I say people here I am focussing on staff who would ostensibly be the consumers of information, not data professionals who are engaged in providing such information. What follows relates to the end users of information.
What I have done at the top of the above exhibit (labelled “Activity” on the left-hand side) is to lay out the different types of information-related work that end users engage in, splitting these into low, medium and high valued-added components as we scan across the page.
Low value is number crunching and prettifying exhibits for publication
Medium value is analysis and interpretation of information
High value is taking action based on insights and then monitoring to check whether the desired outcome has been achieved
In the centre of the diagram (labelled “Ideal Time Allocation”), I have shown what I believe is a best practice allocation of time to these activities. It is worth pointing out that that I am recommending that significant time (60%) is spent on analysis and interpretation; while tagged as of medium-value, this type of work is a prerequisite for the higher value activities, you cannot really avoid it. Despite this, there is a still 30% of time devoted to the high-value activities of action and monitoring of results. The remaining 10% is expended on low-value activities.
At the bottom of the chart (labelled “Actual Time Allocation”), I have tried to estimate how people’s time is actually spent in organisations where insufficient attention has been paid to the information landscape; a large number of organisations fit into this category in my experience. I am not trying to be 100% precise here, but I believe that the figures are representative of what I have seen in several organisations. In fact I think that the estimated amount of time spent on low value activities is probably greater than 70% in many cases; however I don’t want to be accused of exaggeration.
Clearly a lack of robust, reliable and readily available information can mean that highly skilled staff spend their time generating information rather than analysing and interpreting it and then using such insights as the basis for action. This results in the bulk of their work being low valued-added. The medium and high value activities are squeezed out as there are only so many hours in the day.
It is obvious that such a state of affairs is sub-optimal and needs to be addressed. My experience of using diagrams like the one shown here is that they can be very valuable in explaining what is wrong with current information arrangements and highlighting the need for change.
An interesting exercise is to estimate what the bottom of the diagram would look like for your organisation. Are you close to best practice, or some way from this and in need of urgent change?
I find myself frequently being asked questions around terminology in Data and Analytics and so thought that I would try to define some of the more commonly used phrases and words. My first attempt to do this can be viewed in a new page added to this site (this also appears in the site menu):
I plan to keep this up-to-date as the field continues to evolve.
I hope that my efforts to explain some concepts in my main area of specialism are both of interest and utility to readers. Any suggestions for new entries or comments on existing ones are more than welcome.
Today’s interview is with Allan Engelhardt, co-founder and principal of insights and analytics consultancy Cybaea. Allan and I know each other from when we both worked at Bupa. I was interested to understand the directions that he has been pursuing in recent years.
Allan, we know each other well, but could you provide a pen picture of your career to date and the types of work that you have been engaged in?
I started out in experimental physics working on (very) big data from CERN, the large research lab near Geneva, and worked there after getting my degree. Then, like many other physicists, I was recruited into financial services, in my case to do risk management. From there to a consultancy helping business make use of bleeding edge technology and then on to CRM and customer loyalty. This last move was important for me, allowing me to move beyond the technology to be as much about commercial business strategy and operations.
In 2002 a couple of us left the consultancy to help customers move beyond transactional infrastructure, which is really what ‘CRM’ was about at the time, to create high value solution on top, and to create the organizational and commercial ownership of the customer needed to consistently drive value from data, inventing the concept of Customer Value Management which is now universally implemented by telcos across the world and increasingly adopted by other industries.
There is no ISO definition of either insight or analytics. As an expert in these fields, can I ask you to offer your take on the meaning of these terms?
To me analytics is about finding meaning from information and data, while insights is about understanding the business opportunities in that meaning. But different people use the terms differently.
I must give you an opportunity to both explain what Cybaea does and how the name came about.
At Cybaea we are passionate about value creation and commercial results. We have been called ‘Management consultants with a black belt in data’ and we help organizations identify and act upon data driven opportunities in the areas of:
Customer Value Management (CVM), including acquisition, churn, cross-sell, segmentation, and more, across online and offline channels and industries, both B2C and B2B.
Customer Experience and Advocacy, including Net Promoter System and Net Promoter Economics, customer journey optimization, and customer experience.
Innovation and Growth, including data-driven product and proposition development, data monetisation, and distribution and sales strategy.
For our customers, CVM projects typically deliver additional 5% EBITDA growth annually, which you can measure very robustly because much of it is direct marketing. Experience and Advocacy projects typically deliver in the region of 20% EBITDA improvement to our clients, but it is harder to measure accurately because you must go above the line for this level of impact. And for Innovation and Growth, the sky is the limit.
As for the name, we founded the company in 2002 and wanted a short domain name that was a real word. It turned out to be difficult to find an available, short ‘.com’ at the peak of the dot-bomb era! We settled on ‘cybaea’ which my Latin dictionary translated as ‘trading vessel’; historically, it was a type of merchant ship of Greek origin, common in the Mediterranean, which Cicero describes as “most beautiful and richly adorned”. We always say we want to change the name, but it never happens; I guess if it was good enough for Cicero, then it is good enough for us.
While at Bupa you led work that was very beneficial to the organisation and which is now the subject of a public Cybaea case study, can you tell readers a bit more about this?
This was working with Bupa Global; a Bupa business unit that primarily provides international private medical insurance for 2 million customers living in over 195 different countries. Towards the end of 2013, Bupa Global set out on a strategic journey to deliver sustained growth. A key element of this was the design and launch of a completely new set of products and propositions, replacing the existing portfolio, with the objective of attracting and servicing new customer segments, complying with changing regulation and meeting customer expectations.
The strategic driver was therefore very much in the Innovation and Growth space we outlined above, and I joined Bupa’s global Leadership Team to create and lead the commercial insights function that would support this change with deep understanding of the target customers and the markets in which they live. Additionally, Bupa had very high ambitions for its Net Promoter programme (Experience and Advocacy) where we delivered the most advanced installation across the global business, and for Customer Value Management we demonstrated nearly 2% reduction in the Claims line (EBITDA) from one single project.
For the new propositions, we initially interviewed over 3,000 individuals on five continents to understand value- and purchase drivers, researched 195 markets to size demand across all customer segments, and further deep-dived into key markets to understand the competitors with products, features, and prices, as well as the regulatory environment, and distribution options. This was supported by a very practical Customer Lifetime Value model, which we developed.
Suffice to say that in two years we had designed and implemented a completely new set of propositions and taken them live in more than twenty priority markets where they replaced the old products.
The strategic and commercial results were clearly delivered. But when I asked our CEO what he thought was the main contribution of the team and the new insights function, he focused on trust: “Every major strategic decision we made was backed by robust data and deep insights in which the executive team had full confidence.”
In a period of change, trust is perhaps the key currency. Trust that you are doing the right things for the right reasons, and the ability to explain why that is. This is key to get everybody behind the changes that need to happen. This is what the scientific method applied to data, analytics, and insights can bring to a commercial organization, and it inspires me to continue what we are doing.
We have both been engaged in what is now generally called the Data arena for many years, some aspects of the technology employed have changed a lot during this time. What do you think modern technology enables today that was harder to achieve in the past and are there any areas where things are much the same as they were a decade or more ago?
Ever since the launch of the Amazon EC2 cloud computing service in late 2006 , data storage and processing infrastructure has been easily and cheaply available to everybody for most practical workloads. So, for ten years you have not had any excuse for not getting your data in order and doing serious analysis.
The main trend that excites me now is the breakthroughs happening in Deep Learning and Natural Language Processing, expanding the impact of data into completely new areas. This is great for consumers and for those companies that are at the leading edge of analytics and insights. For other organizations, however, who are struggling to deliver value from data, it means that the gap between where they are versus best practice is widening exponentially, which is a big worry.
Taking technology to one side, what do you think are the main factors in successfully generating insight and developing analytical capabilities that are tightly coupled with value generation?
Two things are always at the forefront of my mind. The first is kind of obvious, namely to start with the business value you are trying to create and work backwards from that. Too often we see people start with the data (‘I got to clean all the data in my warehouse first!’), the technology (‘We need some Big Data infrastructure!’), or the analytics (‘We need a predictive churn model!’). That is cart before the horse. Not that these things are not important; rather, that there are almost certainly a lot of opportunities you could execute right now to generate real and measurable business value and drive a faster return on your investments.
The second is to not under-estimate the business change that is needed to exploit the insights. Analytical leaders have appetite for change and they plan and resource accordingly. Data and models are only part of the project to deliver the value and they are really clear on this.
Looking at the other side of the coin, what at the pitfalls to look out for and do you have any recommendations for avoiding them?
The flip-side of the two points previously mentioned are obvious pitfalls: not starting from the business change and value you are trying to create. And it is not easy: great data scientists are not always great commercially-minded business people and so you need the right kind of skills to bridge that gap. McKinsey talks of ‘business translators who combine data savvy with industry and functional expertise’, which is a helpful summary . Less helpfully they also note that these people are nearly impossible to find, so you may need to find or grow them internally.
Which gets to a second pitfall. When thinking about generating value from data, many want to do it all themselves. And I understand why: after all, data may well be a strategic asset for your organization.
But when you recruit, you should be clear in your mind if you are recruiting to deliver the change of creating the first models and changed business processes, or if you are recruiting to sustain the change by keeping the models current and incrementally improving the insights and processes. These two outcomes require people with quite different skills and vastly different temperaments.
We call them Explorers versus Farmers.
For the first, you want commercially-focused business people who can drive change in the organization; who can make things work quickly, whether that is data, analytics, or business processes, to demonstrate value; and who are supremely comfortable with uncertainties and unknowns.
For the second, you want people who are technically skilled to deliver and maintain the optimal stable platform and who love doing incremental improvements to technology, data, and business processes.
Explorers versus Farmers. Call them what you will, but note that they are different.
Many companies are struggling with how to build analytical teams. Do they grow their own talent, do they hire numerate graduates or post graduates, do they seek to employ highly skilled and experienced individuals, do they form partnerships with external parties, or is a mixture of all of these approaches sensible? What approaches do you see at Cybaea clients adopting?
We are mostly seeing one of two approaches: one is to do nothing and soldier on as always relying on traditional business intelligence while the other is to hire usually highly technical people to build an internal team. Neither is optimal in getting to the value.
The do-nothing approach can make sense. Not, however, when it is adopted because management fears change (change will happen, regardless) or because they feel they don’t understand data (everybody understands data if it is communicated well). Those companies are just leaving money on the table: every organization have quick wins that can deliver value in weeks.
But it may be that you have no capacity for change and have made the informed decision that data and analytics must wait, reflecting the commercial reality. The key here is ‘informed’ and the follow-on question is if there are other ways that the company can realise some of the value from data right now.
The second approach at least recognises the value potential of data and aims to move the organization towards realising that value. But it is back to those ‘business translator’ roles we discussed before and making sure you have them, as well as making sure the business is aligned around the change that will be needed. Making money from data is a business function, not a technical one, and the function that drives the change must sit within the commercial business, not in IT or some other department that is still an arms-length support function.
We see the best organizations, the analytical leaders, employing flexible approaches. They focus on the outcomes and they have a sense of urgency driven from the top. They make it work.
I know that a concept you are very interested in is Analytics as a Service (AaaS). Can you tell readers some more about what this means and also the work that Cybaea is doing in this area?
There is a war on analytical talent and a ‘winner takes it all’ dynamic is emerging with medium-sized enterprises especially losing out. Good people want to work with good people which generates a strong network effect giving advantage to large organizations with larger analytical teams and more variety of applications. Leading firms have depth of analytical talent and can recruit, trial, and filter more candidates, leaving them with the best talent.
Our analytics-as-a-service offering is for organizations of any size who want to realise value from data and insights right now, but who are not yet ready to build their own internal teams. We partner with the commercial teams to be their (commercial) insights function and deliver not just reports but real business change. Customers can pay monthly, pay for results, or we can do a build-operate-transfer model.
One of our first projects was with a small telco. They were too small to maintain a strong analytical team in-house, purely because of scale. We set up a monthly workshop with the commercial Marketing team. We analysed their data offline and used the time for a structured conversation about the new campaigns and the new changes to the web site they should implement this month. We would point them to our reports and dashboards which had models, graphs, t-tests, and p-values in abundance, but would focus the conversation on moving the business forward.
The following month we would repeat and identify new campaigns and new changes. After six months, they had more than 20 highly effective and precisely targeted campaigns running, and we handed over the maintenance (‘farming’) of the models to their IT teams. It is a model that works well across industries.
Do you have a view on how the insights and analytics field is likely to change in coming years? Are there any emerging areas which you think readers should keep an eye on?
Many people are focused on the data explosion that is often called the ‘Internet of Things’ but more broadly means that more data gets generated and we consume more data for our analytics. I do think this opens tremendous opportunities for many businesses and technically I am excited to get back to processing live event streams as they happen.
But practically, we are seeing more success from deep learning. We have found that once an organization successfully implements one solution, whether artificial intelligence or complex natural language processing, then they want more. It is that powerful and that transformational, and breakthroughs in these fields are further expanding the impact into completely new area. My advice is that most organizations should at least trial what these approaches can do for them, and we have set up a sister-organization to develop and deliver solutions here.
What are your plans for Cybaea in coming months?
I have two main priorities. First, I have our long-standing partner from India in London for a couple of months to figure out how we scale in the UK. This is for the analytics as a service but also for fast projects to deliver insights or analytical tools and applications.
Second, I am looking to identify the right partners and associates for Cybaea here in the UK to allow us to grow the business. We have great assets in our methodologies, clients, and people, and a tremendous opportunity for delivering commercial value from data, so I am very excited for the future.
Allan, I would like to thank you for sharing with us the benefit of your experience and expertise in data matters, both of which have been very illuminating.
The first episode of Buffy the Vampire Slayer aired on 10th March 1997. To commemorate its 20th anniversary – and of course to celebrate 1st April 2017 – peterjamesthomas.com is pleased to present this comprehensive – and wholly indispensable – illustrated list of business terminology, Slayer-style:
I must admit that it was the word “stakeholder” which first planted the seed that grew into this article. Whenever I hear the rather vapid term in a business context, an image of Sarah Michelle Gellar wafts unbidden into my consciousness; which is no doubt what the person using the term intended all along of course.