The Spike in Rhino Poaching

As part of the Makeover Monday series we revisited a data visualizations that was published by the World Wildlife Fund (WWF) as part of a report on whether rhino poaching figures were indeed improving in South Africa.

The original infographic:

RhinoGraph-2016Stats-2 (1).jpg

What works well?

  1. Single color keeps it easy to find what you are looking for

  2. Bar chart is easily to read and intuitive to many readers

What can be improved?

  1. The use of the rhino in the bar chart is busy and slightly confusing (e.g. 2006 and 2007 both have one rhino but represent different totals).

  2. The year highlight unnecessarily draws your attention away from the main bars.

  3. The graphic does not using highlight coloring to draw attention to the most important elements.

  4. The overall feeling of the graphic doesn’t align well with the subject.

Below is our makeover:

How to not fail when launching data analytics programs.

In life we spend a lot of time thinking about how to succeed and that’s usually a great starting point in mapping out a plan.

However, there are cases where the best place to start is by thinking about how to avoid failure. This is almost always true in cases where the cost and likelihood of failure is high.

After 15 years of developing and implementing analytics programs I believe they fall into this category of “avoid failure” and the evidence seems to support my hypothesis.

Here’s a quick visual to drive home the point.

analytical program success failure 2.gif

Failure is currently the norm but why is this with so much talent and money going into analytics right now?

Most will tell you it is complicated (it is) but I’m going to share an approach to avoiding failure in implementing analytics programs that is actually quite simple (not easy) and relatively affordable (but not cheap).

Failure Facts

Late in 2017, Gartner estimated that big data and analytics projects fail to materialize 60% of the time. That figure was later updated to be 85%!

Gartner’s top data and analytics predictions for 2019 include “80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization through 2020.”

According to the 2019 New Vantage Survey, 77% percent of respondents to their annual survey, say that business adoption of analytics continues to be their biggest challenge.

Even more troubling, the results from a 2018 Chief Marketing Officer Surveyconducted by Duke University, Deloitte LLP and the American Marketing Association found that less than 2% of marketing leaders believe their companies have the right talent to leverage marketing analytics.

How To Fail

There are many ways to fail and there are many reasons why the failure rate is so high! Here’s the all time greatest hits for how to fail in analytics.

Let IT get in the way. Technology solutions looking for a problem is one of the most common issues associated with analytics failures. Designing and implementing a system for business intelligence and data analytics before you know what you need and want is a sure fire way to fail. I’ve seen some massive failures where millions of dollars and countless hours were wasted because of this issue. Most anyone working in this field could likely say the same. Don’t lead with IT.

Work with whoever is currently around. Highly capable analytical talent with industry specific expertise is rare, difficult to locate and not likely just hanging around. The good ones are in high demand so you can expect to pay for it. This and other real factors mean most projects are staffed by current team members. While your people may be great at what they currently do (which, they will presumably have to continue doing), and even better if they are good analysts, not taking seriously the independent discipline and the level of effort associated with implementing an analytics program is a fatal mistake.

Photo by  Brandi Ibrao  on  Unsplash

Believe you know it all. Data analytics is a complex space with regular technological and methodological advances that are too much for most teams to work out for themselves. Some examples might include how you could use text mining and natural language processing to better understand your customers or the market or utilizing new products like Google Data Studio to create easily accessible dashboards in a jiffy. The options are seemingly endless. More likely than not you have a dearth of analytics knowledge in your organization so before you think you’ve got it all figured out, think again.

Don’t account for flexibility. What you need today may not be what you will need tomorrow, literally. The ability to add and remove capabilities and capacity when needed is fundamental to making the most of your analytics investment. One example I’ve seen time and again is companies hire an analyst in hopes they have the right skills only to find out that that their limitations become your limitations which often leads to floundering initiatives.

Don’t get the right kind of help. Jumpstarting these initiatives in-house proves impossible for many. Traditional consulting arrangements require lengthy contracts and high levels of effort to manage. On top of this, many firms are working harder at keeping their team on your payroll even after the work has been completed. This can lead to protracted engagements that find you doing more for the consultants than they are doing for you.

How To Succeed

While success doesn’t come easy, this approach is directly related to the reasons for failure and is based on 15 years working from within with large and small organizations and as a management consultant.

Start using [your] data today. The journey of 1,000 miles begins with the first step. Data analytics is no different. Begin by taking stock of what data you have and begin working with it using whatever tools you and your team are familiar with. Don’t worry about its limitations or yours. Get excited and get moving!

Get curious. Now is the time to start asking questions, reading (looks like you can check that box right now), engaging with experts on social media and branching out. Start asking the really important questions about your customers, the market, trends and opportunities for the future. These questions are the starting point for shaping your analytical needs, creating your strategy and maximizing the impact of your investment.

Don’t let IT get in the way. Resist the temptation to make IT the focus. Do not fall victim to regular sales calls from vendors trying to sell you the latest BI platform. That time is spent far better working with your data, your team, and learning more about what is needed and possible.

Use lightweight tools. This is the flipside of not letting IT get in the way. The world of analytical tools is becoming richer by the day. Automated machine learning platforms are speeding the time from data collection, cleaning and loading to insight like never before. Using basic tools and techniques for each stage of the analytics process (question formation, data collection, preparation, analysis and visualization, repeat) is your best bet. Any tools that are not working for you can be easily discarded for something newer, better, lighter, faster or easier. Don’t be afraid of open source software. Some of our favorite tools include the KNIME analytics platform, Tableau and Microsoft’s Power BI. That said, nothing beats the power and flexibility of coding in R or Python, if you have the skills.

Get the right help. Even with the advent of powerful and lightweight tools, one of your primary needs will be in finding the right partner to help support you in this journey. The partner you look for should be able to dedicate a team to your effort including an analytics translator, data scientist, data analyst and visualization specialist. They should support you in all phases of the data analytics lifecycle including doing the analytical work. Your contract with them should also include an ad-hoc support option for brainstorming and responding to questions as they arise. For many companies, outsourcing analytics is a great options for reducing risk and quickly getting insights you can use. Our firm, Primary Notes, specializes in this style of support.

Visualize and communicate. Great data visualizations amplify great insights. Data storytelling and visualization are fundamental to the success of your analytics program and special emphasis should be placed on this from the start. No matter how good your analytics are, if they do not manifest themselves in compelling, well designed and presented visualizations, your ability to drive action from the insights will be hampered.

Maintain a business first focus. Your team should always be lead by questions that are important for your mission, not in using technology and analytics for their own sake or simply because they are popular. A business first focus will ensure you are articulating problems that need solutions rather than the opposite. Make it a regular part of your leadership meetings to capture any questions that come up whether or not you think they can be answered by data analytics. These questions should be available to everyone on the team and everyone should be able to provide their input as questions arise.

Iterate. Experiment. Fail. Succeed! Data analytics is a practice which means that you need to exercise these skills repeatedly and regularly in order to improve or maintain proficiency. Regular experimentation with the types of questions you ask and the analytics you perform will keep things interesting for everyone, develop new skills and insights and bring you closer to meeting your ultimate objectives (in addition to avoiding the dreaded failure).

The most successful teams will deliberately stretch themselves so when failures occur they see them as high learning potential that will facilitate growth. Leadership should support this high growth mentality and be invested in times of success and failure.

Don’t Be Another Statistic

Success in data analytics, like in business and life, is hard fought and requires work and focus. Remember, there are no [IT] shortcuts but with clear direction, knowing what to avoid and what matters and getting the right help you’ll be on your way to avoiding the dreaded analytics failure that is so common.

London Bus Safety

We took a look at data collected from the Transport for London reporting system on accidents involving buses in London between 2015 and 2018. The data show a disproportionate number of incidents affecting passengers.

Our approach was to keep focused on the high level figures and the impacts to passengers. Starting off with some BANs showing the sheer number of recorded incidents each year, moving to a tree map to emphasize the impacts on passengers. This is followed up by a lollipop chart showing a more specific breakdown of the passenger incident types and resulting injuries.

Here’s the original report and here is the live visual on our Tableau Public page.

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Women in Power

With the midterms around the corner, and the significant divide between the parties and the sexes, we thought it would be a good time to look at the historical trend of women in power - in particular the US House of Representatives.

While neither party has reached parity, Democratic women have been more successful at breaking the glass ceiling than their Republican counterparts.

What do the 2018 midterms have in store with regard to reaching party of the sexes in the halls of power?

A full interactive version can be found here on Tableau Public.

Women in Power.JPG

Avocado Price Spikes

For anyone that regularly eats avocados, it was all but impossible to ignore some recent price spikes of the little green dream. What you may not have noticed was that the prices quickly came back down to record lows following the spikes. This data set from the online data visualization series Makeover Monday (#MakeoverMonday on twitter) From the original writing by Oakridge Wealth Advisors:

Avocados Are Getting Expensive – Commodity Fact

A smaller than usual harvest of avocados in California and Mexico has elevated the price of avocados nationwide. California is the largest grower of avocados in the United States, while Mexico is the largest producer of the fruit worldwide. Avocados from the U.S. account for 7% of worldwide production, while Mexico supplies 32% of the world’s avocados. As a result, wholesale prices for the fruit have soared 75% since the middle of July, according to data from the Hass Avocado Board and the U.S. Department of Agriculture.


In addition to a poor harvest, a growing demand for the fruit has also added upward pressure on the price. There are seven varieties of avocados grown commercially in California, with the Hass variety as the most popular, accounting for roughly 95 percent of all avocados grown.

To the left is the original data visualization.

And here is our makeover visualization:


Planes vs. Trains

Adapted from the article and data set used here, we wanted to offer our perspective on Planes vs. Trains - Cost and Speed analysis. The article offers a perspective on the total costs and benefits of both modes of travel.

While we like their more in depth analysis, especially the review of the CO2 emissions as part of the total cost, we believe that the core of the argument for most travelers comes down to convenience (speed) and price.

We’ve plotted the cost of 6 highly traveled routes in western Europe, with ticket costs for each week of 6 weeks leading to the departure date. We chose to show an average of the 6 trips so we can make an overall judgement on price and trip duration.

On trip duration, even if we were to add an hour or two, we would still be well ahead of the train for a relatively small incremental higher cost.

What do you think? When given the choice, what are your priorities?

Bon Voyage!

Planes and Trains.JPG

Nike's Footprint

For week  36 of the Makeover Monday project, we worked with a data set provided by Nike, that includes data about their contract manufacturing footprint worldwide. While there was a great depth of data provided we thought it would be most helpful to keep it clean and simple, focus on the country specific attributes and drive the message with a single map and typography. 

The live version updates the map and the data based on your country selection. To see the live version, click here


Wearable Tech

From head to toe, we're getting busy with wearable technology. This burgenoning market is exploding and the wrist device dominates the landscape. 

We took a look at the number of models, pricing and where they go on the body. 

This was part of the Makeover Monday series

For a look at the interactive version on Tableau Public, please click here. See our other visualizations and connect with us on Twitter and Facebook

Wearable Tech Market_Primary Notes Analytics.jpg

Syrian Deaths

As part of Makeover Monday, a weekly competition to re-visualize a public data set, share your work and critique, we visualized data from

Week 34 ACLED: Visualizing the Syrian Conflict (abridged from the full data set of 74 countries with nearly 200,000 deaths in a single year). A snapshot from hell and inhumanity at its worst.

To see our interactive version on Tableau Public, click here

Data Source: ACLED

#MakeoverMonday #ACLEDconflictviz @ACLEDINFO @DataDotWorld @PrimaryNotes

Syrian Conflict Fatalities and Events 2017:18_PrimaryNotesAnalytics.jpg

Optimizing Political Canvassing

If you're trying to increase the speed of political canvasing, using a population density map is a critical early step. 

We were supporting a House Delegate candidate in Maryland's Montgomery County and she was coming toward the closing days of a political campaign and wanted to be sure they were maximizing their speed of getting house to house and covering the entire district. This is important, especially in races where you don't have any polling to tell you what the electorate thinks of your candidate or how impactful your get out the vote efforts have been. You need to know that you are being efficient between houses and across the entire population.

The method we employ to maximize the efficiency of canvassers is to do a density cluster analysis of the target voters. We wanted to identify the most dense to least dense clusters of target voters across the county so we could prioritize the highest density for canvasing, working our way to less dense regions. This has three benefits:

  1. It helps the campaign keep it's eye on the entire district and which regions have been contacted and which have not. This gets everyone on the same page as to what has been done, what we're doing and what we need to do. Almost like a Kanban board but a map. :)

  2. It minimizes the effort and time it takes to canvas the maximum number of voters.

  3. It ensures some signage coverage in the most voter concentrated areas across the entire area so that even if you don't canvas every house, you would have a minimum number of yard signs out to help build awareness and votes.

The maps:

Initial map showing just the point and their density values. The red shows us where the high density clusters are, followed by yellow then green.

Initial map showing just the point and their density values. The red shows us where the high density clusters are, followed by yellow then green.

This is a screen shot of the full scale hi-res map we delivered to the campaign. They were able to use these digitally and in print to manage field operations.

This is a screen shot of the full scale hi-res map we delivered to the campaign. They were able to use these digitally and in print to manage field operations.

This is a zoomed in shot of the high-res map. Notice the cluster count which helped with cutting turf.

This is a zoomed in shot of the high-res map. Notice the cluster count which helped with cutting turf.

We also developed an interactive web map where the campaign was able to turn on and off the points per index value as well. Hovering over individual dots shows the name, address and other voter data.

We also developed an interactive web map where the campaign was able to turn on and off the points per index value as well. Hovering over individual dots shows the name, address and other voter data.