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Here are a few tips about getting the most out of your data visualizations.

1. Gather data

Use tools like Bime to mash up your multiple data sources to get unique insights.

2. Use great design

Lots of applications come with pre-built designs that you can use. Others let you customize to a certain extent. If you are using graph-based data, choose your charts carefully – and as long as the data allows, keep it simple and functional.

3. Interactivity

Whenever you are working with data online, you have the chance to provide your users with interactivity. Sometimes, static infographics are good enough to display information but sometimes you will get significantly more interest and understanding if you provide a dashboard that users can play around with for themselves.

4. Quirky is as important as correct

It’s generally not enough to just present the raw facts – interesting comparisons and strong imagery improve the shareability of any piece. We are all wired to remember (and therefore to repeat) comparisons better than plain facts.

5. Know your targets

You need to know first of all who your targets are, and then find a way to reach them. Make sure your visualization is seen by your target market, whether colleagues, customers or senior management, and make an effort to target any niches within that market.

6. Provide the embed code (with the link)

You should provide the embed code primarily for two reasons:

- to make it easy for non-tech-savvy bloggers to share your content
- to make sure (if you can) to get a link out of it

If you can style and include the link in a relevant way (especially if it links to more data or more information) you are likely to increase the chance that the people embedding your content will embed the link along with it.

7. Take information out
There’s a tradition among users of showing absolutely everything – all data points, the whole range, every column and row. But the best visualizations are about clarity with unnecessary information excluded.

8. Self-sufficiency
All graphs, charts and infographics should be self-sufficient. I.e., other information should not be required in order to understand them. Each should have a clear title, legend, source, labels etc.

Adapted from Data Visualization Techniques by SEOmozBlog.

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The dashboard contains 3 visualizations which all show different relationships.

The first, the treemap, shows the number of users in July 2010 in relation to the percentage penetration (number of people on Facebook as a percentage of the country’s population). The size of each individual box within the treemap shows the total number of Facebook users in the country. The strength of the color shows the percentage penetration, with the darkest blue being attributed to a higher percentage of users, and the lightest blue being attributed to the lowest. As you can see from the visualization, the USA has more than 3 times the number of users as any other country in the data, but not the highest number of people on Facebook as a percentage of the country’s population. As it is difficult to distinguish different shades of blue when the data is so close, you can hover over the points to get more detailed information. By doing this we can quickly see that Hong Kong has the highest concentration of users with regards to its population. Another (perhaps easier) way of finding this out is by simply altering the filters for the data which can be found underneath the attributes box on the right hand side.

The second visualization is the geovisualization, or heat map. We have chosen graduated circles to display the information but you can change it back to the original heatmap format by clicking on “Rendering Mode” in the bottom left hand corner. Here we see the relationship between the population and the percentage penetration. The size of the graduated circles show proportionately the population size, and the number inside demonstrates the percentage penetration. A very simple but a highly visual representation, the heatmap is probably the best way to get an overall geographical idea of where Facebook’s users are around the world.

Lastly, our third chart is the classic pie chart. It is better to use this type of chart when there is not too much data (pie charts work best with smaller data sets because after a certain point the data becomes impossible to read) so we have used the filters to filter out any country with a population of less than 10 million. This gives us a nice overview of the relationship between the percentage penetration and the number of users. We have also chosen to display the percentage penetration measure by size, to make it really clear which countries have a high percentage (USA, UK) and which have a low percentage (India, Mexico). Bime automatically chooses different colors for each segment which make the chart aesthetically pleasing and easy to read.

Don’t forget to make each visualization fullscreen when you look at it, by clicking the little box in the top right hand corner of each chart, so you can really see the details of your data set. Alternatively you can view all three together fullscreen by clicking “Fullscreen” in the bottom left hand corner of the dashboard. If you want to export a PDF or an image of your visualization, Bime makes it super easy to do this – no more messing around with printscreen – simply click the “Export PDF” or “Export Image” buttons at the bottom of the dashboard (N.B. heatmaps cannot be exported). Equally you might choose to export your data to an Excel spreadsheet.

Datasource: The Guardian.

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The problem of choosing colors for data visualization is expressed by this quote from information visualization guru Edward Tufte:

“Avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm.” — Envisioning Information, Edward Tufte, Graphics Press, 1990.

Tufte calls the most important use of color in information presentation “labeling”. By this he means the function of distinguishing one element from another.

According to the Dataspora Blog, color is one of the most abused and neglected tools in the field of data visualization. It is abused when we make poor color choices; it is neglected when we rely on poor built-in color defaults. Yet despite its traditional poor treatment by groups of engineers and end-users alike, if used well, it can enhance and clarify a presentation. Color used poorly is likely to obscure information rather than highlight it. While there is a strong aesthetic component to color, using it well in information display is essentially about function: what information you are trying to convey, and how or whether color can enhance it.

Take a look at this example using Dr Who villains data below. At the top of the dashboard you see the information (total number of episodes and villain) displayed in a black and white bar chart. This graph is clear in displaying the two variables, and use of color would just confuse the reader because consciously or not, when people look at a data display and see visual differences, they try to determine the meaning to those differences (and in this case colour would add no meaning or value). But what if we wanted to cross compare this information with another measure? This is where color comes in handy.

Why use color in data graphics?

If the data is simple, a single color is sufficient, even preferable, as you see from the first chart. However, if we want to layer another dimension of data — first year appeared — into our chart, we can choose to do this by color. If we take a look at the same chart, but with the added dimension of first year appeared, this is displayed by the chart at the bottom. You can see that with a splash of color it is much clearer to see not only the number of episodes each villain appeared in, but also which ones appeared in the early episodes and which ones only came about more recently.

So why bother with color?

First, as compared to most print media, computer displays have fewer units of space, but a broader color gamut. So color is a compensatory strength.

For multi-dimensional data, color can illustrate additional dimensions inside a unit of space — and can do so immediately. Color differences can be detected within 200ms, before you are even conscious of paying attention. But the most important reason to use color in multivariate graphics is that color is, in itself, multidimensional.

We could have used other methods other than color – plotting symbols or small multiples for example – but to avoid having to change the chart type, we found color to be the most suitable. This shows color can be used in powerful ways to enhance the meaning and clarity of data displays, but only when we understand how it works and what it does well. Our advice : Whenever you’re tempted to add color to a data display, ask yourself these questions: “Will this color serve a purpose?” and “Will it serve this purpose effectively?”, and if the answer is yes to both – by all means go ahead and use it. As Tufte says, “If the information is worth displaying, it’s worth displaying well.”

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Just about any industry sector can benefit from location intelligence. Financial, insurance, retail, communications, even governmental agencies can benefit from geographical information displays (e.g. through Google Maps) as they give you a real picture of ‘where your data is’. Take the heatmap feature in Bime as an example.

The great thing about the heatmap is that it is not simply a pretty picture – hover over the heatpoints and you can bring up the detailed data lying behind them by clicking on it. This is particularly helpful when different areas of focus are close together, like on the dashboard above. By clicking on “Rendering mode” you can switch between the heatmap or graduated circles view. You can filter your view by states by checking or unchecking the relevant boxes under “attributes” on the right. You can also filter by the measure (profit) by using the slider below the list of attributes.

Geographic data visualizations can help businesses answer fundamental questions, such as:

- What geographic area do we serve?

- Are our sales territories and client clusters properly aligned?

- What areas of potential clients might we have missed or overlooked?

Now we’ve discussed why an organization might need geocoding, but how does it make use of it? Here is one example of many.

Geocoding as a Marketing Tool

Many businesses use geomarketing to perform specific market/region data analysis for the purpose of making sound business decisions. Geomarketing allows businesses to analyze customers on digital maps, acquire valuable market data, and plan their next move, whether this be at a local, national, or global level. This enables organizations to better coordinate all of their domestic and international sales and marketing operations – for improved efficiency.

For marketers, these types of visualizations are critical for targeting demographics for customer profiling and for customizing marketing messages to a specific audience – based on the geographic location of potential customers.

Bime’s geo visualization allows you to see where you are in the real world, quite literally, using the Bime functionality on top of Google Maps. We’ve been using this feature for a while now – just as a line graph is probably the most effective way of displaying time data, the heatmap is definitely the way to go for geographic data. With this, you can visualize where your staff, clients, suppliers, or any other facet of your business is concentrated. As we outlined above, this can do wonders for your organization, and equally importantly, keep you one step ahead of the competition.

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We’ve made a video to go along with the release of the Google Analytics Connector in Bime. In under 4 minutes we take you through a few of the basic operations you can now do using your Google Analytics data. We explain how to create a connection, how to deploy queries and how to ask different questions of your data. It then delves even deeper by demonstrating filters and calculated attributes. So by now you must be curious about how it all works…. see for yourself, here:

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5 trends we have identified in the BI and Data Visualization world this year are:

1. Growth of the Data Warehousing Market

Recently we’ve seen a trend toward consolidation and building a centralized enterprise data warehouse. As a result, a massive modernization drive intended to improve overall decision-making is now taking place. Within the past year, data warehousing solutions have continued to become more and more popular because of their high levels of performance, ability to incorporate analytics, and their integration within larger BI platforms. As the costs of space and processing speed become lower, vendors can give organizations more powerful offerings but with lower price tags. This has helped to expand the use of data warehouses within organizations, both by the number of companies adopting data warehouses, and by the types of applications that can be used.

2. Social Media for BI

Providers are slowly integrating social networking functions into their solutions. Customers are beginning to expect rich interactive experiences that mimic their internet use, and BI can match these expectations if it draws on the information from interactions that occur in social computing environments. Due to the ability to create powerful interactions and improve ease of use, more providers are focusing on developments that mimic social media and Web 2.0 interactions. In doing so, the role of BI has become increasingly popular and more widely applied within organizations because of the assumed ease of use.

3. Increased Use of Different Data Sources

The reality of today is that in order to stay ahead of competitors, companies are required to integrate various information sources to get additional value from their data and a full operational view of the organization. Vendors are now developing business-focused applications that take these requirements into account and offer customers a ready-made solution that targets business issues being faced by companies within different markets.

4. Renewed Focus on Fraud Detection and Security

Because of the amount of media coverage of fraudulent activities, the ability to detect fraud and to maintain a secure environment is an area constantly at the forefront of IT. Organizations are required to make sure that information within the firewall is not left at risk. So as threats against IT security have continued to augment, organizations have been more committed to tightly monitoring their environments and the information within them. Providers have done a good job ensuring security – however, because of increased risk, organizations and vendors alike will focus more on maintaining high level security within their information centers, especially due to all the new forms of data being integrated into analytics platforms.

5. Advanced Data Visualizations

Data visualization offerings keep expanding, and the inclusion of geo-spatial analysis has helped bring analytics to the forefront of visualization. For organizations looking at product, geographical or customer data, the ability to identify trends using maps has helped them to recognize trends a lot faster. Although this technology has been around for a while and is not “new” as such, it is only recently that companies have started to integrate this type of functionality on a regular basis within their overall dashboard use.

The pressure is on!

Within the BI space, constant advancements in technology means trends are always shifting. Depending upon economic and market factors, organizations may not adopt improved offerings from developers. On the other hand, early adopters often look to BI for inspiration on ways in which they can help their business overtake the competition. Overall, 2009 saw many releases within back-end databases and front-end visualizations. On top of this, there has been serious demands for low-cost solutions with quick implementation times, and solution providers are under pressure to improve upon their current products throughout the rest of 2010. Here in Montpellier, we have been continuously updating Bime to bring you all of these things, as well as top-of-the-range features, simplicity of use and a beautiful, clean interface.

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But there are strong backlashes from traditional data visualization gurus arguing that a lot of the new graphics being created are unclear or even misleading. The general consensus seems to be that newly invented inverted double swirl plot would actually be better as a bog standard bar chart.

But do these critiques miss the point somewhat? These novel plots are not always about trying to make data easier to understand but more exciting to understand. Most are designed to capture attention, rather than inform quickly. Where they are difficult to interpret the reader feels challenged to make sense out of the numbers.

To be fair to the critics, there are numerous examples where it is nearly impossible to draw a logical conclusion, even after prolonged study, and others that lead to the wrong conclusions.

So what should businesses take from this? The first rule of presenting your data post-analysis is to know your target audience. Consider selecting simple, informative graphs for the intrinsically interested, but a more adventurous one for you marketing literature.

However, where so many of these new visualizations are let down is through a lack of labelling and explanation. Lack of rigour in the final presentation will inevitably call the analysis and data collection in to question.

Does the design fail if people do not get it immediately? Not always, but it certainly does fail if people can’t get it at all.

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Looking through the public profiles people are tracking everything from exercise regimes to irrational fears, from fuel consumption to religious activity. It seems to me that there are three main groups of users:

1. The efficiency drivers: those who track everything to give an objective view of where they can save money and time
2. The motivational diarists: those who record things like dietary intake and gym routines to chart their achievements and, I suspect, act as a record of shame when things go wrong.
3. The curious masses: those who enjoy visualizations of the number of cups of tea they’ve consumed this month or how many hours a year they spend on the toilet.

Data analysis and visualization in the business world has always had a strong arm in efficiency drives, so it is only natural that our first group have taken to do the same with their own finances. It can be quite revealing to find that your monthly coffee expenditure could pay for a weekend break for two.

However, it is the second cohort that interests me most. It is an old mantra that what doesn’t get measured doesn’t get done, but measures and targets handed down by management often produce the opposite of the desired effect. Perhaps empowering people with tools to track and visualize their own productivity could increase motivation, without the feeling that Big Brother is watching your every move. Or then perhaps it would just be another distraction.

As data recording becomes quicker and data visualization becomes easier, those who embrace personal data analysis may find a personal competitive advantage just as businesses do everyday. Whether businesses can harness this effectively to motivate their employees remains to be seen.

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• The Cloud Is Not Reliable
• Security and Privacy Are Not Adequate in the Cloud
• I Can Get All the Benefits of the Cloud by Creating My Own In-house Cloud or Private Cloud
• If I Can’t Move Everything at Once, the Cloud Isn’t for Me
• Cost Is the Biggest Driver of Cloud Adoption

• The application can be accessed securely from any machine, anywhere.
• SaaS products are built for collaborative working and the easy results sharing
• Software licensing can be scaled up or down on a monthly basis, giving flexibility and eliminating the problem of “shelf-ware”
• The multi-tenant system means that you have huge computing resources at your disposal and without requiring disproportionate investment for variable usage
• The software is continuously updated and improved by the vendor, allowing it to evolve with your changing requirements and feedback
• There is no capital expenditure required for set up (far from true with traditional outsourcing models)
• Traditional software and server vendors are selling technology to IT professionals. SaaS vendors are only selling a service to the end-user, meaning that they are 100% focused on end-user experience.
• SaaS removes limitations of what you can achieve using your in house IT hardware and support. All you need to do is work out what you want your software to do and how much you want to pay for it, then find the product that fits best.

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