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Apple’s Health App and HealthKit – Platforms for Next Gen Healthcare?

Originally posted on Business Analytics 3.0:

mobile-applicationsGame on….I think we just witnessed a big next generation leap in Healthcare Data and Analytics.  Apple jumped into the health information business on June 2, 2014, launching both a new health app and a cloud-based health information platform with IOS 8.

The new App, called simply “Health”, will collect a number of body metrics including blood pressure, heart rate, and stats on diet and exercise.  Health will constantly monitor key health metrics (like blood sugar or blood pressure), and if any of them begin to move outside the healthy range, the app can send a notification to the user’s doctor.

The Health app will share all its information with a new cloud platform called “HealthKit.” The new health cloud platform is designed to act as a global repository for all the user’s health information. It will accept data collected by a variety of third-party devices and apps. For instance Nike is now working to…

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Posted by on June 2, 2014 in Uncategorized

 

Visualizing Voting Preferences for World Values

The other day I listened to a presentation by Melinda Gates prepared for the United Nations to deliver an update about progress towards the Millennium Development Goals (MDG). The eight goals of the MDG had been embraced by the UN back in 2005 for the time target of 2015. So it is reasonable to see whether the world is on track to reach each of these eight goals. To summarize, from the MDG Wikipedia page:

  1. Eradicating extreme poverty and hunger
  2. Achieving universal primary education
  3. Promoting gender equality and empowering women
  4. Reducing child mortality rates
  5. Improving maternal health
  6. Combating HIV/AIDS, malaria, and other diseases
  7. Ensuring environmental sustainability
  8. Developing a global partnership for development

A good listing of reports, statistics and updates can be found on the UN website here.

Sample Vote for 6 of 16 MDG choices

Sample Vote for 6 of 16 MDG choices

At the end of Melinda’s presentation is a link to a UN global survey on the MDG goals after 2015. I took this survey and found the visualization of voting results quite interesting. First, one is asked to select six out of a list of sixteen (6 of 16) goals which one thinks have the highest impact for a future better world. (The survey methodology is described in more detail here.) Here is a sample vote:

A nice touch is that for each of the sixteen goals there is a different color and when you check that goal, one of the sixteen areas on the stylized globe is filled with that color. Personal data such as name is optional, but some demographic information is required, including age, gender, educational level and country. Next, one can look at a summary of all currently tallied votes and compare them interactively to ones own vote (checkmarks on the right).

WorldVoteOverview

It is perhaps not surprising that I voted very similar to others in similar demographic cohorts.

  • Country: I picked five of the Top five goals like all other voters living in the US. I included ‘Political freedoms’ in my top six, which in the US only ranks 11th.
  • Age: I shared five of the Top six goals with people in my age group (world-wide). The one I did not check was ranked 4th (Better job opportunities). When you mouse over one of the goals, the display changes to highlight this goal in all columns:
Interactive Vote Analysis with highlighted goal

Interactive Vote Analysis with highlighted goal

  • Gender: Here I picked four of the Top five goals (did not include the ‘Better job opportunities’).
  • Education: I voted very similar to people with very high HDI (Human Development Index, a visualization of which we covered in a previous post) with five of the Top six.

From the above, it seems somewhat surprising that voters in the US did not ascribe a higher value to ‘Better job opportunities’, given how much economic values and topics like unemployment seem to dominate the media. That said, these votes should be a reflection about which goals are most valuable for making the world a better place – not just your own home country. Worldwide it seems that other, more fundamental goals are judged by voters in the US to be more important than ‘Better Job opportunities’.

Another chart on the results page is showing a heat map of the world countries based on how many votes have been submitted. I thought it was interesting that Ghana had submitted about twice as many votes as all of the US, and Nigeria about 7x as many. The country with most voters at this time is India, but not far ahead of Nigeria.

CountryTotals

A fairly useless dynamic animation in this map is a map pin drop of four people who voted similarly to me. I found this too anecdotal to be of any real interest and downright annoying that I couldn’t turn it off. and just focus on the vote heat map. For example, the total number of votes should be displayed in the Legend. I vaguely remember that it was several hundred thousand from 194 countries prior to starting the survey, but couldn’t get that data to display again without clicking on the Vote Again:

MyWorldVotes

 
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Posted by on September 21, 2013 in Education, Medical, Scientific

 

Personal Analytics with the Suunto Ambit


Suunto Ambit

Half a year ago my wife bought me the Suunto Ambit multi-function sport watch and heart rate monitor. It is a fantastic device, with very precise GPS, lots of add-on functionality and an interesting online portal and community.

There is some configuration and setup involved, such as pairing the Ambit with your heart rate belt and in the case of cycling with a cadence pod. You charge the batteries by plugging it into a USB port, which is also the way how you upload the data form the device to your computer or a website.

While the device itself and its programmability is quite advanced, I want to focus here on the associated online portal called Movescount.com where you can upload and visualize all your data for free – and share it with friends or the community if you’re so inclined. This amount of personal data collecting and analyzing is a fairly recent phenomenon, often referred to as Personal Analytics.

Each recorded session with the Suunto can be uploaded and classified into one of many sports, such as hiking, cycling, basketball, or indoor exercise. Each session is called a move, and with the portal you can collectively visualize all your moves. The current theme at movescount.com has a black background with mostly orange bars and charts. One of the first controls to organize your moves is either a list or a calendar control.

Calendar Control for Moves

Calendar Control for Moves

This already gives you a good overview over the type of sports activities and the distribution over weekday and weekends. A summary display is available in various forms, such as the following simple bar charts.

Summary information about heart rate zones

Summary information about heart rate zones

Another display format summarizes your selected moves, such as all moves in a particular month together with commensurate calorie consumption and breakdown of hours by type of move.

Moves Summary Display

Moves Summary Display

You can now select either a single or multiple moves (or group by the type of move) and display more information about this particular move. Note the x-axis can be set to display either distance or time and one can zoom in on any part of the entire recorded move. One can alos overlay multiple measures in the same chart by selecting more than one factor, although I find this to lead to very busy and confusing charts.

Graph and BarChart details per move

Graph and BarChart details per move

There are many individual measurements available for display, some based on individual sensors (like heart rate or GPS location or temperature or altitude / air pressure), others based on calculations and estimates (such as speed, recovery parameter “R&R”, EPOC or VO2).

MapGraph

Of particular interest to me as a cyclist is the ability to overlay the GPS-track on a Google map. Not only is it a very detailed recording of the route, but it is color-coded based on the currently selected measure. For example, the color-range shows the heart rate in the same colors as the above bar charts. One can clearly see where one is just warming up at the beginning (low heart rate, green color) or where one is riding up “into the red”, i.e. towards the limits of one’s own heart rate. Selecting points along the route displays some information about that particular point of the ride.

One interesting feature would be a time-geo correlation of any portable photo camera when taking pictures along the ride. Based on synchronized time one could then easily geo-code the photos even without any GPS capability within the camera itself.

The Suunto Ambit can do a lot more, including customizing the display mode and storing your configurations in so-called apps. One idea I have for this is to display an estimate of the total calorie consumption for a known route when continuing at the current pace (but I haven’t played with the programming yet). The Ambit seems to be particularly well suited to hiking, mountain biking or skiing due to its altimeter; however I don’t get to leverage that in flat Florida. Only the few bridges over the Intracoastal waterway show up as bumps in the vertical – with the corresponding acceleration of the heart rate on the uphill side.

One of the downsides is the fact that the heart rate sensor worn around the chest does not work in the water. Hence any swimming in the Ocean or the pool can not be measured precisely. (I replace the measurements with estimates.) And sure enough, just recently Suunto announced the new Ambit 2, which overcomes this limitation. Such is the world of new electronic toys, that the half-life of their innovation is getting shorter and shorter.

Bubble Chart of set of rides

Bubble Chart of set of rides

Measures in Bubble Chart

Measures in Bubble Chart

One last chart I wanted to point out is the flexible bubble chart. Shown above is a selection of all my rides in the first half of 2013 (47 rides minus two outliers, very long rides which would have changed the scale and compressed the rest of the chart). This gives a good feel for the distribution and variance of personal rides over a longer period of time – from the quick half hour duration to the more typical rides of a good 2 hours. Note that one can select any of about 30 measures in any of the three drop-down boxes (X-Axis, Y-Axis, Bubble-Size).

One side-effect of measuring and visualizing so many moves is that we find some interesting differences in our respective exercise habits and corresponding energy consumption. While I burn most of my calories on the bicycle, my wife gets more exercise out of indoor circuit exercises and Yoga than I do. For me, after literally decades of recreational cycling, I can raise my heart rate to much higher levels for extended periods of time on the bike compared to indoor circuit exercises. In a way that is not surprising, given the strength and oxygen consumption of the large leg muscles compared to smaller shoulder and arm muscles. But I would not have expected the difference to be so pronounced and could not have quantified it nearly as precisely as without such personal analytics.

It can be expected that the field of healthcare and personal analytics will converge and provide much more personalized data and insight into the specific life of any patient. Medical indicators like heart rate, blood sugar, blood pressure or factors like exercise and diet will become much more quantifiable and individually tracked over time. The hope is that this will also lead to better, more personal and generally more preventive care and medical treatments to any personal condition.

 
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Posted by on June 30, 2013 in Recreational

 

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Apple, Amazon, Google, Microsoft

Last year we looked at the four companies and compared their business model over two quarters: Apple (hardware), Amazon (retail), Google (advertising), Microsoft (software). It struck me how far the integrated Wolfram Alpha technology has come in the last two years. It combines the symbolic computing capabilities of the Mathematica platform with curated data (for example financial data) and some pretty impressive linguistic analysis capabilities for free-form text input.

For example, in Wolfram Alpha, just type in the following query: “Googe vs. Amazon vs. Apple vs. Microsoft” The results are shown as a series of three screen-shots below:

ComparisonWolframAlpha1

ComparisonWolframAlpha2

ComparisonWolframAlpha3

Not only do you get the various data such as the fundamentals or the analysis of a mean-variance optimal portfolio displayed, but you get all the code needed to programmatically load such data. For example, if you want to get the breakdown of the analyst ratings, the system will expand it as follows:

AnalystRatings

So far we haven’t done any coding or bothered with integrating any data source. This amount of integration and automation is pretty impressive. I am often surprised how few companies are taking advantage of such advanced technology platforms.

 
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Posted by on April 28, 2013 in Financial

 

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Visualizing Conversion Rates (Funnels, Bullet Charts)

Most sales processes go through a series of stages, from first contact through successive engagement of the potential client to the close. One can think of these as special cases of attrition-based workflows. These are very typical in online (B2C eCommerce) or tele-marketing (call centers) and companies usually collect a lot of data around the conversion rates at each step. How can one visualize such workflows?

One metaphor for these processes is that of a sales funnel. A lot of leads feed in on one side, and at each stage fewer pass through to the next. It is then straightforward to want to visualize such funnels, such as shown here by Tableau.

Tutorial video explaining how to create a funnel chart using Tableau

Tutorial video explaining how to create a funnel chart using Tableau (Source: Tableau Training Video)

Aside from the somewhat tedious steps involved – custom field calculations, left-right duplication of the chart, etc. – it turns out, however, that funnel charts are not easy to interpret. For example, it is not well suited to answer the following questions:

  • What’s the percentage reduction at each step?
  • Comparing two or more funnels, which has better conversions at each step?
  • What are the absolute numbers in each step?
  • Are the conversion rates above or below targets at each step?

Ash Maurya from Spark59 wrote a very useful article on this topic entitled “Why not the funnel chart?“. In it he looks specifically at comparisons of funnels (current vs. prior time intervals or A|B testing).

Time Series comparison of funnel performance (Source: Ash Maurya's Spark59 Blog)

Time Series comparison of funnel performance (Source: Ash Maurya’s Spark59 Blog)

In a next step he shows that the funnel shape doesn’t add to the readability. Instead simple bar charts can do just as well:

Same information with Bar Charts (Source: Ash Maurya's Spark59 Blog)

Same information with Bar Charts (Source: Ash Maurya’s Spark59 Blog)

For a multi-step funnel, the problem remains that with the first step set to 100%, subsequent steps often have fairly small percentages and thus are hard to read and compare. Suppose you are sending emails to 100,000 users, 30% of which click on a link in the email, of which only 10% (3% of total) proceed to register, of which only 10% (0.3% of total) proceed to subscribe to a service. Bars with 3% or even 0.3% of the original length will be barely visible. One interesting variation is to normalize each step in the funnel such that the new, expected conversion number (or that from the prior period) is scaled back to 100%. In that scenario it is easy to see which steps are performing above or below expectations. (Here big jump in Registrations from Jan to Feb, then small drop in Mar.)

Bar Charts with absolute vs. relative numbers

Bar Charts with absolute vs. relative numbers

Next, Ash Maurya uses the Bullet Chart as introduced by Stephen Few in 2008. The Bullet Chart is a variation of a Bar Chart that uses grey-scale bands to indicate performance bands (such as poor – ok – good) as well as a target to see whether the performance was above or below expectations. The target bar allows to combine two charts into just one, giving a compact representation of the relative performance:

Funnel Chart showing funnel performance (Source: Ash Maurya's Spark59 Blog)

Bullet Chart showing funnel performance (Source: Spark59 Blog)

Various authors have looked at how to create such bullet charts in Excel. For example Peltier Tech has looked at this in this article called “How to make horizontal bullet graphs in Excel“. There is still quite some effort involved in creating such charts, as Excel doesn’t directly support bullet charts. Adding color may make sense, although it quickly leads to overuse of color when used in a dashboard (as Stephen Few points out in his preference for grey scales).

Funnel Graphs in Excel (Source: Peltier's Excel Blog)

Bullet Graphs in Excel (Source: Peltier’s Excel Blog)

Another interesting approach comes from Chandoo with an approximation of a bullet graph in cells (as compared to a separate Excel chart object). In this article “Excel Bullet Graphs” he shows how to use conditional formatting and custom formulae to build bullet graphs in a series of cells which can then be included in a table, one chart in each row of the table.

In-Cell Funnel Graph in Excel (Source: Chandoo's Blog)

In-Cell Bullet Graph in Excel (Source: Chandoo’s Blog)

It is somewhat surprising that modern data visualization tools do not yet widely support bullet charts out of the box.

Measuring how marketing efforts influence conversions can be difficult, especially when your customers interact with multiple marketing channels over time before converting. To that end, Google has introduced multi-channel funnels (MCF) in Google Analytics, as well as released an API to report on MCFs. This enables new sets of graphs, which we may cover in a separate post.

 
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Posted by on March 31, 2013 in Industrial

 

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Magic Quadrant Business Intelligence 2013

It’s that time of the year again: Gartner has released its report on Business Intelligence and Analytics platforms. One year ago we looked at how the data in the Magic Quadrant – the two-dimensional space of execution vs. vision – can be used to visualize movement over time. In fact, the article Gartner’s Magic Quadrant for Business Intelligence became the most viewed post on this Blog.

I had also uploaded a Tableau visualization to Tableau Public, where everyone can interact with the trajectory visualization and download the workbook and the underlying data to do further analysis. This year I wanted to not only add the 2013 data, but also provide a more powerful way of analyzing the dynamic changes, such as filtering the data. For example, consider the moves from 2012 to 2013 of some 21 vendors:

Gartner's Magic Quadrant for Business intelligence, changes from 2012 to 2013

Gartner’s Magic Quadrant for Business intelligence, changes from 2012 to 2013

It might be helpful to filter the vendors in this diagram, for example to show just niche players, or just those who improved in both vision and execution scores. To that end, I created a simple Tableau dashboard with four filters: A range of values for the scores of both vision and execution scores, as well as a range of values for the changes in both scores. The underlying data is also displayed for reference, which can then be used to sort companies by ordering along those values.

Here is an example of the dashboard set to display the subset of 15 companies who increased either both or at least one of their vision or execution scores without lowering the other one.

Subset of companies who improved vision and/or execution over the last year.

Subset of companies who improved vision and/or execution over the last year.

That’s more than 70% of platforms, with the increase in vision being more pronounced than that of execution. That’s considerably more than in the previous years (2013: 15; 2012: 6; 2011: 6; 2010: 3; 2009: 9) – making this collective move to the top-right perhaps a theme of this year’s report.

Who changed Quadrants? Who moved in which dimension?

Last year Tibco (Spotfire) and Tableau were the only two platforms changing quadrants, then becoming challengers. This year both of them “turned right” in their trajectory and crossed over into the leaders quadrant due to strong increases in their vision capabilities. (QlikTech had been on a similar trajectory, but already crossed into the leader quadrant in 2012. It also strengthened both execution and vision again this year.)

Another new challenger is LogiXML. Thanks to ease of use, enhancements from customer feedback and a focus on the OEM market its ability to execute increased substantially. From the Gartner report summary on LogiXML:

Ease of use goes hand-in-hand with cost as a key strength for LogiXML, which is reflected by its customers rating it above average in the survey. The company includes interfaces for business users and IT developers to create reports and dashboards. However, its IT-oriented, rapid development environment seems to be most compelling for its customers. The environment features extensive prebuilt elements for creating content with minimal coding, while its components and engine are highly embeddable, making LogiXML a strong choice for OEMs.

A few other niche players almost broke into new quadrants, including Alteryx (which had the biggest overall increase and almost broke into the visionary quadrant), as well as Actuate and Panorama Software.

The latter two stayed the same with regards to execution (as did SAP) – while all three of them moved strongly to the right to improve on the vision score (forming the Top 3 of vision improvement).

Information Builders and Oracle stayed where they were, changing neither their execution nor vision scores.

Microsoft and Pentaho stayed about the same on vision, but increased substantially in their execution scores.  This propelled Microsoft to the top of the heap on the execution score, while it moved Pentaho from near the bottom of the heap to at least a more viable niche player position. Microsoft’s integration of BI capabilities in Excel, SQL Server and SharePoint as well as leveraging of cloud services and attractive price points make it a strong contender especially in the SMB space. Improvements of its ubiquitous Excel platform give it a unique position in the BI market. From the Gartner report:

Nowhere will Microsoft’s packaging strategy likely have a greater impact on the BI market than as a result of its recent and planned enhancements to Excel. Finally, with Office 2013, Excel is no longer the former 1997, 64K row-limited, tab-limited spreadsheet. It finally begins to deliver on Microsoft’s long-awaited strategic road map and vision to make Excel not only the most widely deployed BI tool, but also the most functional business-user-oriented BI capability for reporting, dashboards and visual-based data discovery analysis. Over the next year, Microsoft plans to introduce a number of high-value and competitive enhancements to Excel, including geospatial and 3D analysis, and self-service ETL with search across internal and external data sources.

The report then goes on to praise Microsoft for further enhancements (queries across relational and Hadoop data sources) that contribute to its strong product vision score and “positive movement in overall vision position”. This does not seem consistent with the presented Magic Quadrant, where Microsoft only moved to the top (execution), not to the right (vision). Perhaps another reason for Gartner to publish the underlying coordinate data and finally adopt this line of visualization with trajectories.

Deteriorate2013

Dashboard with filters revealing two platforms deteriorating in both vision and execution

Only two vendors saw their scores deteriorate in both dimensions: MicroStrategy gave up some ranks, but remains in the leader quadrant. The report cites a steep sales decline in 3Q12 and the increased importance of predictive and prescriptive analytics in this years evaluation among the reasons:

MicroStrategy has the lowest usage of predictive analytics of all vendors in the Magic Quadrant. A reason for this behavior might be the user interface that is overfocused on report design conventions and lacks proper data mining workbench capabilities, such as analysis flow design, thus failing to appeal to power users. To address this matter, MicroStrategy should deliver a new high-end user interface for advanced users, or consumerize the analytic capabilities for mainstream usage by embedding them in Visual Insight.

The other vendor moving to the bottom-left is arcplan, which is now at the bottom of the heap in the niche players quadrant.

Who moved to the top-left?

With the dashboard at hand, you can also go back and do similar queries not just for the current year 2013, but any of the five previous years. For example, who has moved to the top-left – improved execution at the expense of reduced vision – over the years?

In 2013 those were Targit, Jaspersoft, Board International. All three of them had a sharp drop in Execution in the previous year 2012. A plausible scenario of what happened is that these companies lost their focus on execution, dropped the scores and in an attempt to turn-around focused on executing well with a smaller set of features (hence lower vision).

In 2012 the only vendor to display a move to the top-left was QlikTech. They had some sales issues the prior year as well, although their trajectory in 2011 was only modestly lower in execution, mostly towards higher vision.

In 2011 Actuate and Information Builders moved to the top-left. Both had trajectories to the bottom-left the prior year (2010), with especially Actuate losing a lot of ground. With the Year slider on the top-left of the dashboard one can then play out the trajectory while the company filters remain, thus showing only the filtered subset and their history. Actuate completed a remarkable turn-around since then and is now positioned back roughly where it was back in 2010.

Dashboard with analysis of top-left moving companies.

Dashboard with analysis of top-left moving companies.

 

(Click on the image above or here to go to the interactive Public Tableau website.)

In 2010 there were five vendor moving to the top-left: Oracle, SAS, QlikTech, Tibco (Spotfire) and Panorama Software. Although in that case none of them did show a decrease in execution the previous year. That focus on execution may simply have been the result of the economic downturn in 2009.

Such exploratory analysis is hard to conceive without proper interactive data visualization. Given the focus of all the vendors it covers in this report, it seems somewhat anachronistic that Gartner in its report does not leverage the capabilities of such interactive visualization itself. In the previous post on Global Risks we have seen how much value that can add to such thorough analysis. (Much of this dashboard should be applicable for risk analysis as well, just that the two-dimensional space changes from platform vision vs. execution to risk likelihood vs. impact!) If Gartner does not want to drop on its own execution and vision scores, they better adopt such visualization. It’s time.

 
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Posted by on February 12, 2013 in Industrial

 

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Visualizing Global Risks 2013

Visualizing Global Risks 2013

A year ago we looked at Global Trends 2025, a 2008 report by the National Intelligence Commission. The 120 page document made surprisingly little use of data visualization, given the well-funded and otherwise very detailed report.

By contrast, at the recent World Economic Forum 2013 in Davos, the Risk Response Network published the eighth edition of its annual Global Risks 2013 report. Its focus on national resilience fits well into the “Resilient Dynamism” theme of this year’s WEF Davos. Here is a good 2 min synopsis of the Global Risks 2013 report.

We will look at the abundant use of data visualization in this work, which is published in print as an 80-page .pdf file. The report links back to the companion website, which offers lots of additional materials (such as videos) and a much more interactive experience (such as the Data Explorer). The website is a great example of the benefits of modern layout, with annotations, footnotes, references and figures broken out in a second column next to the main text.

RiskCategories

One of the main ways to understand risks is to quantify it in two dimensions, namely its likelihood and its impact, say on a scale from 1 (min) to 5 (max). Each risk can then be visualized by its position in the square spanned by those two dimensions. Often risk mitigation is prioritized by the product of these two factors. In other words, the further right and/or top a risk, the more important it becomes to prepare for or mitigate it.

This work is based on a comprehensive survey of more than 1000 experts worldwide on a range of 50 risks across 5 broad categories. Each of these categories is assigned a color, which is then used consistently throughout the report. Based on the survey results the report uses some basic visualizations, such as a list of the top 5 risks by likelihood and impact, respectively.

Source for all figures: World Economic Forum (except where noted otherwise)

Source for all figures: World Economic Forum (except where noted otherwise)

When comparing the position of a particular risk in the quadrant with the previous year(s), one can highlight the change. This is similar to what we have done with highlighting position changes in Gartner’s Magic Quadrant on Business Intelligence. Applied to this risk quadrant the report includes a picture like this for each of the five risk categories:

EconomicRisksChange

This vector field shows at a glance how many and which risks have grown by how much. The fact that a majority of the 50 risks show sizable moves to the top right is of course a big concern. Note that the graphic does not show the entire square from 1 through 5, just a sub-section, essentially the top-right quadrant.

On a more methodical note, I am not sure whether surveys are a very reliable instrument in identifying the actual risks, probably more the perception of risks. It is quite possible that some unknown risks – such as the unprecedented terrorist attacks in the US on 9/11 – outweigh the ones covered here. That said, the wisdom of crowds tends to be a good instrument at identifying the perception of known risks.

Note the “Severe income disparity” risk near the top-right, related to the phenomenon of economic inequality we have looked at in various posts on this Blog (Inequality and the World Economy or Underestimating Wealth Inequality).

A tabular form of showing the top 5 risks over the last seven consecutive years is given as well: (Click on chart for full-resolution image)

Top5RisksChanges

This format provides a feel for the dominance of risk categories (frequency of colors, such as impact of blue = economic risks) and for year over year changes (little change 2012 to 2013). The 2011 column on likelihood marks a bit of an outlier with four of five risks being green (= environmental) after four years without any green risk in the Top 5. I suspect that this was the result of the broad global media coverage after the April 2011 earthquake off the coast of Japan, with the resulting tsunami inflicting massive damage and loss of lives as well as the Fukushima nuclear reactor catastrophe. Again, this reinforces my belief that we are looking at perception of risk rather than actual risk.

Another aggregate visualization of the risk landscape comes in the form of a matrix of heat-maps indicating the distribution of survey responses.

SurveyResponseDistribution

The darker the color of the tile, the more often that particular likelihood/impact combination was chosen in the survey. There is a clear positive correlation between likelihood and impact as perceived by the majority of the experts in the survey. From the report:

Still it is interesting to observe how for some risks, particularly technological risks such as critical systems failure, the answers are more distributed than for others – chronic fiscal imbalances are a good example. It appears that there is less agreement among experts over the former and stronger consensus over the latter.

The report includes many more variations on this theme, such as scatterplots of risk perception by year, gender, age, region of residence etc. Another line of analysis concerns the center of gravity, i.e. the degree of systemic connectivity between risks within each category, as well as the movement of those centers year over year.

Another set of interesting visualizations comes from the connections between risks. From the report:

Top5Connections

Top10ConnectedRisks

Finally, the survey asked respondents to choose pairs of risks which they think are strongly interconnected. They were asked to pick a minimum of three and maximum of ten such connections.

Putting together all chosen paired connections from all respondents leads to the network diagram presented in Figure 37 – the Risk Interconnection Map. The diagram is constructed so that more connected risks are closer to the centre, while weakly connected risks are further out. The strength of the line depends on how many people had selected that particular combination.

529 different connections were identified by survey respondents out of the theoretical maximum of 1,225 combinations possible. The top selected combinations are shown in Figure 38.

It is also interesting to see which are the most connected risks (see Figure 39) and where the five centres of gravity are located in the network (see Figure 40).

One such center of gravity graph (for geopolitical risks) is shown here:RiskInterconnections

The Risk Interconnection Map puts it all together:

RiskInterconnectionMap

Such fairly complex graphs are more intuitively understood in an interactive format. This is where the online Data Explorer comes in. It is a very powerful instrument to better understand the risk landscape, risk interconnections, risk rankings and national resilience analysis. There are panels to filter, the graphs respond to mouse-overs with more detail and there are ample details to explain the ideas behind the graphs.

DataExplorer

There are many more aspects to this report, including the appendices with survey results, national resilience rankings, three global risk scenarios, five X-factor risks, etc. For our purposes here suffice it to say that the use of advanced data visualizations together with online exploration of the data set is a welcome evolution of such public reports. A decade ago no amount of money could have bought the kind of interactive report and analysis tools which are now available for free. The clarity of the risk landscape picture that’s emerging is exciting, although the landscape itself is rather concerning.

 
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Posted by on January 31, 2013 in Industrial, Socioeconomic

 

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