About our customer Wilfred Mijnhardt & the Erasmus Research Institute of Management (ERIM):

Wilfred Mijnhardt (left) is executive director of the Erasmus Research Institute of Management, at the Erasmus University Rotterdam, The Netherlands. The institute is the joint initiative of the Rotterdam School of Management, the international business school at Erasmus University and the Erasmus School of Economics. ERIM is a community of around 350 researchers in the field of management. Wilfred himself has been at the Institute for 12 years and has helped build the organization from scratch into a research institute with a worldwide reputation. ERIM is consistently ranked among the top 3 research institutes in management in Europe and top 25 worldwide.

About the Erasmus Institute’s needs :

The institute produces lots of data to disclose and much of this data goes into publications like an annual report which is viewed primarily by academic stakeholders but also by business and a wider societal audience.

ERIM strives for academic excellence and impact by targeting top journals in management research. Wilfred wanted to show with dynamic and web-based analytics that ERIM had effectively brought the field of management research at Erasmus University to a top international level, covering a perspective of 10 years. He wanted to produce dashboards with statistics and intelligence to gain insight into the scope of their academic performance pattern with KPIs such as the number of publications, selections on the top publications, per countries, per field of research etc.

Displeased with the blandness of the school statistics profiling reports, Wilfred looked for a way to bring his data to life! With so much data from a range of different sources, Wilfred needed to get his dashboards set up. He needed a solution which was then user friendly and quick & easy to use, which offered a variety of visualizations and dashboarding tools to extract the true potential from his data. As Wilfred notes, the ability to share his dashboards and for his users to be able to interact with the data, was also of optimal importance:

“Visitors to the website, especially our academic peers, would like to play with the data and experience the transparency of our performance system, not just look at it”

Wilfred commented that the combination was the key, the process of: obtaining the data from multiple databases, analysing and processing this data, and then displaying it through effective visualizations. All three needed to be efficient to fully leverage the true value of the data. It was the combination that delivered the magic.

Internationalisation in 2010 (zoomed in on Europe), taken from the ERIM Input Dashboard

Why Bime ? How has Bime responded to Wilfred’s needs ?

With much talk in the industry about the potential of using analytics to track school profiling reports, Erasmus were keen to be the first movers within the academic and university market. The Institute is very proud of their academic results and publications and was keen to implement a modern solution which could fully exhibit their data. Bime was suggested as the solution to fit their needs by our Dutch partners, KJP Consultants. Wilfred comments that the “liveliness of the visualizations in Bime was the initial trigger”.

In continued and “very pleasant” co-creation with Bime, the institute now puts out a dynamic annual report with trends and analytics. The dashboards represent data for input, output and productivity at this institute and are drawn from multiple databases holding Erasmus’ research information systems. At time of writing they have produced 6 years worth of dynamic annual reports and have plans to soon expand this to cover a 10 year period, all of which are available to view at any time online on their website.

In a break from traditional dashboards, the dashboards take several seconds to load because they update with real-time data and are not static.They provide updates and interactive features for the viewers in the context of the website. One of the main advantages Wilfred has found is that stakeholders can interact with the dashboards to fully explore the data. This helps reassure them about the information organization, that performance measurements systems are in good shape at the company and also fully shows off the data. Wilfred is impressed with the strong range of visualizations available in Bime and in particular makes use of the publishing options, which allow ERIM to embed the dashboards in reports and emails as well as sharing them with stakeholders, fellow academia and the world, on the institute website. Working in conjunction with the website, the dashboards allow users to learn a lot simply by scrolling the dashboards, providing “a very rich insight of company performance over the years”.

Erasmus Research Staff, taken from the ERIM Input Performance Dashboard

What about our Bime partner KJP Consultants ? What help did they provide Wilfred ?

After first signing up for Bime, Wilfred worked closely with KJP Consultants and was in dialogue with Bime HQ in France, to make Wilfred’s vision a reality and KJP provided conclusive support to fully explore and optimize the functionality of Bime. KJP particularly assisted Wilfred to fuse his many databases in Bime and in harnessing the post-processing power of Bime. From a technological point of view KJP helped Erasmus take multiple data sets and create a chain of information, connected by Bime to provide an overview of all the Institute’s data. Wilfred describes KJP as the “bridge” between the analytics and the corporate world. He added that it was a fantastic help to have a local Bime consultant and the KJP team provide “great support” and have “huge empathy” for the customer.

International Articles, ISI Highly Cited Classification, taken from the ERIM Output Dashboard

Did Wilfred realize his vision with Bime ?

Erasmus share their dashboards online, through their website, available here. Wilfred has received a lot of positive feedback particularly from fellow professors and from the academia network worldwide. Users enjoy how the dashboards present live analysis and how they are extremely collaborative, with many users asking Wilfred how he creates such dashboards. The institute is proud of their cutting edge research and are now pleased to have an innovative tool to mirror their research and to inform their stakeholders.

Erasmus currently has plans to utilize the analytical power of Bime to do more data crunching and use BIME as a presentation layer on top of some of the more technical academic software such as SPSS and STATA and make the results more user-friendly with Bime.

View ERIM’s Input, Output & Performance dashboards here.

Thank you to our customer Wilfred Mijnhardt for accepting this interview and to our Bime partner for Benelux, KJP consultants. November 2011.

Did you know? The Doctor has fought more than 300 villains and monsters in his time. Find out who they are – and which appeared most, as well as when they first hit the screens. However, the data doesn’t include villains exclusively in Doctor Who books, audio books and spin-off shows – only the televised serials and only enemies (as opposed to protagonists).

The infamous Dalek has appeared more times than any other Doctor Who villain.

This particular data was sourced from: The Guardian Data Blog.

Data Format: (Online) – Google Spreadsheet.

Click on the image below to access the dashboard.

When to use a line chart:

When NOT to use a line chart:

It can often be a challenge, however, to display a lot of trends on one line chart clearly and succinctly.

With Bime there are a few easy ways to display a vast amount of data on a line chart, in a simple and concise way.

To quote one of my favourite acronyms, KISS – Keep it simple, stupid! Only show the metrics which you need to display and are relevant to your analysis. In Bime there are several easy ways to do this. Firstly, by clicking the drop down arrow next to your attribute on the frame and selecting or deselecting the elements you wish to display. After executing the calculation you can again select which elements you wish to show by clicking them in the row selector on the left of the chart (hold ‘ctrl’ or ‘cmd’ to select multiple elements and ‘shift’ to select a list of elements).

Alternatively, you can filter your measures by only showing the top or bottom x number of results using the ‘Top’ tab of the post-processing menu.

Another quick technique is to simply highlight a measure using the mouse. Just hover the cursor over the name of an element in the legend on the right and that element will be shown while the others are faded out. For example in the image below we are able to just show the values for ‘Internet Explorer’.

In Bime you can switch through your selected row members automatically, simply click the ‘play’ button above the list of row members or manually click through.

To make a chart less cluttered, group similar values. For example in the initial image in this post we had a visualization looking at the web browsers used to access a website, in which we had several different Blackberry browsers which can obviously be grouped together. To group attribute elements; click the arrow next to the attribute you wish to make a group from, select ‘create a group’ to bring up the group selection box and then follow the instructions in the images below.

In Bime it is possible to display several measures on a line chart, in a succinct way, to maintain the simplicity of the visualization, even when the values are far apart numerically. The three examples below show both visitors per browser and visit bounce rate. Bime offers 3 options:

Hopefully this post has given you some ideas of how to best utilize line charts. A line chart is just one of the many stunning visualizations available in Bime.

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The study is based on 150 Fortune 1000 companies and thus the results are very vague. That said, the study comes up with some interesting findings. With a 10% increase in the usability of its data, the average Fortune 1000 company from the sample could increase their return-on-equity by 16%. Some of the other key finds are neatly summarized in the stylish infographic we found on ReadWriteWeb below.

The study further highlights the need for companies to turn their data into dollars. Bime lets the small guys unlock the value of big data!

Start analyzing now. Sign up for a free trial (no credit card needed).

SOURCES: The University of Texas and Sybase, ReadWriteWeb

Data sourced from: Mashable 2011

Diving into the data
It was interesting to see that there are still some iPhone owners that have the very first generation iPhone that came out in 2007 – against almost half of the surveyed who hold 2010′s 4th generation handset.

Text messaging proved to be the activity which iPhone owners dedicated the most time to above all other activities (19% of device usage time was attributed to SMS activity) – followed closely by calling (17%), emailing, apps and Internet (all 16%).

The next metric we looked at was daily iPhone usage – how many times the owner uses his or her phone during a given day. If you’re a smartphone user yourself, the results are probably relatively unsurprising – more than 80% of owners surveyed said they used their iPhone more than 15 times a day. The numbers peaked with 33% of respondents citing that they use their iPhone between 30 and 49 times a day. If you use your iPhone, say, 40 times a day, that’s once every 36 minutes!

Next up was jailbreaking information – what percentage of phones had been jailbroken vs. the percentage of those that hadn’t. Just over 1 in 10 owners reported jailbreaking their iPhone – 11.7% of total respondents to be exact.

The data also touched on some of the changes that iPhone owners wanted to see in the upcoming iPhone 5, where longer battery life, no long-term contracts and unlimited data took the top spots. Other interesting, but less coveted ones, included unlimited storage in iCloud, full HD video and 3D display. Near the bottom of the preference list was a feature Apple is renown for, and one which has been the subject of wide speculation: style and design – proving that the 5th generation’s predecessors have been so far satisfactory in this area. It perhaps should also perhaps be acknowledged that some of these wishes such as the length of contract are not totally decided by Apple and would be down to the network providers.

Apps are a huge part of the reason for owning an iPhone, and the distribution of free and paid apps was reflected in the data. Paid apps generally seem to dominate where an owner has 10 applications or less, and then free apps take over where the number of installed apps rises above 11.

Lastly, the sample was U.S. based, so the network distribution data was restricted to American carriers. AT&T was the sole carrier of the iPhone in the U.S. until February 2011, and so understandably claims three-quarters of the market share. The stats show that Verizon has a respectable 19.5% share of the iPhone market, whereas other carriers combined make up the last 5%.

Access the dashboard by clicking the image below

Do you have an iPhone? What improvements would you like to see in the iPhone 5? Hit the comments.

This ‘data explosion‘ is one of the reasons companies need tools like Bime – new innovations have enabled more and more data to be managed and supported, and this data needs to be analyzed in order to produce value for an organization.

Start analyzing now.

Click the image to enlarge

SOURCES: Cisco; comscore; MapReduce, Radacti Group; Twitter; YouTube

A collaboration between GOOD and Oliver Munday, in collaboration with IBM.

To give you an idea of what you could achieve in a few minutes with Bime, we built a basic dashboard containing (dummy) Adwords campaign metrics data. Imagine the possibilities if you spent a couple of hours on it!

You can access the dashboard by clicking the screenshot below.

Tweet this dashboard! Tweet

Don’t forget to check out some of the other sample dashboards we have built in the Bime showcase.

The graphics below are only images, if you open the dashboard in the web player in your browser these visualizations are interactive and using the row selector tool, more information can often be examined. To access the interactive dashboard click here. The only measure shown from the original data-set was the fee amount. The remaining averages, calculations and queries have been executed within Bime. In Bime you can easily distinguish between the attributes by assigning a color, thus, throughout our dashboard red=English Premier League and blue=French Ligue 1. Throughout the dashboard, ‘N’ refers to the number of transfers. Thanks to the concise way we were able to display the data, and by querying the wider dataset, a number of conclusions can be drawn.

The first conclusion to draw is that the Premiership is a bigger money league, something often assumed, but now clear to see. The average fee in the Premiership is €6.6m, against €3.8m in Ligue 1, and the distribution of fees is startling. In Ligue 1, only Pastore (€42m to PSG) arrived for more than €12m, while the Premiership saw 21 players move over that price. The discrepancy is even bigger if PSG and the funding from their new backers is taken out of the equation; without this, the average fee drops to €3m, and the highest fee, in or out, would have been €12m – and he (Gervinho) went to Arsenal. In Bime, queries can easily be adapted, such as with the example above (L1 clubs without PSG). This can be performed using groups, and including / excluding the attributes or measures you wish to include in the query.

In total, €198m moved within the Premiership, which had a net spend out to lower domestic leagues of €70m, and sent €149m net abroad. The most expensive market was internal, averaging €9m per player (possibly reflecting the much touted ‘English player premium’). Conversely, the majority of Ligue 1′s outside spend was domestic, with €32m net staying in France and only €14m going overseas. In Bime, using color weighting (and also if needed but not shown below, size weighting) on a range of different visualizations, we are able to show several different measures on one chart. The pie charts below, taken from the dashboard, show both where transfers were made (segment) and average transfer fee (color weighting).

Other differences in approach can be seen – players are twice as likely to move on a free in Ligue 1, with the area treemap (below) showing the clubs who did the most free transfer and loan moves. While the domestic loan markets are fairly even, with both leagues mostly sending out their younger players to lower domestic leagues to gain experience, the Premiership is nearly six times more likely to send players overseas for a spell, Arsenal picking up plane tickets for seven players, Chelsea for four, and Liverpool and Manchester City three.

The effect of PSG’s newly fattened wallet may be a cause for concern in France, as the massive cash injections at City and Chelsea were in England, but we will have to wait and see if this is the bellwether for a change in approach in Ligue 1. As things stand, Ligue 1 may be seen as a ‘junior’ league within Europe, but with UEFA’s financial fair play rules coming into view, the less bloated finances of the French league (see the bubble chart below) combined with the possibility of soon leap-frogging Italy in the rank co-efficients, may be a recipe for longer-term stability as Ligue 1 teams are able both to attract, and hold onto, important players.

With the exception of PSG French clubs tend to spend well within their means. Click the chart to view a fully interactive dashboard to see which circles belong to which clubs.

The dashboard summary of the summer transfer window highlights how Bime can take complex and large amounts of data, manipulate it and display it in an easy-to-digest way with some stylish looking visualizations. To explore how Bime can transform your data, sign up for a free trial (no credit card required!).

Access the dashboard here

Authors: Philippa Booth & Jonathan Gardiner

As before, there are a few things to note before diving into the data.
1. The data is taken from a SAMPLE, not from the whole G+ population.
2. Edited – people are indexed at random for the sample (apparently you don’t add yourself). Apologies for having previously reported otherwise, it was an error on our part.
3. It’s a snapshot in time! The numbers will have again slightly shifted since the dashboard was created. We used data from over a period of 2 days, between the 16th and the 18th of August 2011.

Sourcing our data from the same place as before, findpeopleonplus.com, we created an interactive Bime dashboard that shows some comparisons between the demographics of the Google+ user base back in July, and how it looks now, halfway through August.

This time, the sample was much bigger at between 9,647,653 and 14,536,129 users, at the time of creation.

Gender

• Surprisingly, not much has changed! Females increased from 28 to 30% of the total sample.

Companies

• There were several new entrants to top 100 companies including: School, IT, HP, Life, Government, Express, Sony, Orange, YMCA, Vodafone, Subway and others.
• Some that were previously in the top 100 have been knocked out: JP Morgan and IBM India to name a couple.
• Please note: we took out all forms of “Myself” from the August companies snapshot… this included “Me”, “Myself” and “Me and Myself only” (??!), because there were so many different forms it was hard to see the real underlying trends.
• There are several different variations of the same employer (e.g. Wal-Mart and Walmart). We tried to add these up wherever necessary but will have missed a few for sure. So just be aware of that.
• Company distribution is very similar to July, but the order of position has changed a little. Most importantly, most of the top companies have more than doubled the number of G+ users in the past 2 weeks.

Occupations

• Again, for occupations there were several different versions of the same thing => Teach, teacher, educator; System administrator, systems administrator; Grad student, graduate student.
• In July the Geeks ruled – now in August the Students are taking over!

User growth

• User growth stats were sourced from here, here and here, not from findpeopleonplus.
• Google+ has been credited with being the fastest growing social network in terms of user signups, with it reaching an impressive 25 million users less than 1 month after its launch.

Geographic distribution

• User distribution by country for August was pretty much the same pattern, just with increased numbers.

Active or not active?

• Another interesting analysis that was in the data was the number of “active” vs “inactive” users. A tweet from findpeopleonplus confirms that a user is classed as active if they have posted at least once publicly on G+. Findpeopleonplus have said that they are going to rename this field. We took this analysis out as a lot of people feel it is not accurate enough.

Want to display this infographic on your site?

Simply copy and paste the code below into the html of your website to display the dashboard presented above:

Similarly worldwide Chrome is increasingly popular. Currently it is still the third most used browser with 22% of the market, behind IE (42%) and Firefox (28%) but experts predict this too will soon change and it won’t be long before Chrome users exceed the number of Firefox users worldwide and continuing at this rate, Chrome may even be setting its sights on IE.

A slight anomaly to this overall worldwide trend appears to be looking at Europe as a whole, where Firefox is almost as popular as IE and Chrome is currently the third most popular browser by some distance.

We were able to visualize some of this data using Bime and have included data on which web browsers our own visitors to the website use; to enjoy click the image below!

To view our other example Bime dashboards click to visit our showcase.