A dashboard or scorecard interface finally makes it easy for a majority of users to quickly find, analyze, and explore the information they need to perform their daily tasks.

There is a subtle distinction between a scorecard and dashboard. Each tool merges elements from the other, but at a high level they both target specific and different levels of the business decision making process.

At the highest, most strategic level of the decision making spectrum sit scorecards. Scorecards display periodic snapshots of performance associated with an organization’s strategic objectives and plans. The goal of the scorecard is to keep the business focused on a common strategic objective by making comparisons within your department, company and/or industry to indicate your direction. The primary measurement used in a scorecard is the key performance indicator. These key performance indicators are often a composite of several metrics or other KPIs that measure the organization’s ability to execute a strategic objective.

The common industry perception is that a dashboard displays information about a company at a given point in time that can be used to make better business decisions. Dashboards fall down one level from a scorecard in the decision making process; as they are less focused on a strategic objective and more tied to specific operational goals. An operational goal may contribute directly to one or more higher level strategic objectives. Within a dashboard, the execution of the operational goal itself becomes the focus, not the strategy. The purpose of a dashboard is to provide the user with actionable business information in a format that is both insightful and intuitive. Dashboards leverage their data primarily in the form of metrics and KPIs.

When we were doing our research, we stumbled across Dan Holowack’s blog. He provides us with a metaphorical solution to our problem that might help you better remember the differences between the two:

Imagine for a moment that you’re driving a car…

DASHBOARD

All the instruments in your center console are displaying current-state information. The speedometer says you’re traveling 45 km/h and the RPM gauge is at 3000. The odometer tracks and displays total distance traveled and a fuel gauge says you’re at half a tank. All these indicators display information for a ’single point in time’.

SCORECARD

Knowing all this dashboard information is great, but here’s why you need a scorecard… you’re heading straight for a BRICK WALL (10 seconds to impact). It’s fascinating how the dashboard can give you so much information, but it’s all quite useless without the support of Scorecards. Consider the Scorecard to be your own eyes. You notice you’re passing all the other drivers on the road and decide to slow down (for fear of a speeding ticket!). Similar to how you compare cars on the road, the Scorecard should make comparisons within your department, company and/or industry to indicate your direction (are you heading for a brick wall?).

Source: Dan Holowack, Love Data Intelligence blog

A nice little round-up for this post comes from TDWI : “Whereas a dashboard informs users what they are doing, a scorecard tells them how well they are doing. In other words, a dashboard records performance while a scorecard charts progress. In short, a dashboard is a performance monitoring system, whereas a scorecard is a performance management system.”

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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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Using filters is easiest to explain using an example, so we’ll have a look at the filter “top”. Top filters out all but the greatest value in a data set, but the principles are the same for all filters. Top can be extended to include more than one value, so for example top 2 zooms in on the two greatest values.

So here is our example pivot table without any filters :

This table can be saved to a dashboard and made public. Any filters then applied by users in the dashboard (called global filters) will be applied to the complete data set and to all queries in that dashboard.

If top 2 is applied to the query before saving to the dashboard, the following result is shown on the dashboard :

Say now the user applies a top 3 global filter (on the dashboard). This filter overrides the query specific top 2 filter and so three values will be seen. When the global filter is removed, the original top 2 filter becomes dominant again so only the highest two values are shown. The two queries below have top 2 applied; try changing this to top 3 by selecting the Top tab (on the far right) and changing the value from 2 to 3. You will see the results change for both queries :

If you then remove this global filter by unchecking the Top box, both queries revert to their original filters (both top 2).

If our top 2 filtered query is included in a dashboard where the other queries have the same filter (as in the example above), we see the top 2 option selected on the global filter panel. This is because top 2 is essentially a global filter in this case. If it is saved in a dashboard that contains queries with other filters or no filter, each keeps its own filters and no global filter toggle is checked.

The dashboard below contains a query with a top 3 filter, and one without a filter. Observe, no global filter toggle is checked.

So, you can see that it is important to apply filters at the right time to achieve the required result. Applying the filters strategically allows users of your dashboards to see the important results found during data exploration, but to override the filters and explore all of the data. It also allows users to compare like with like in dashboards once they have evaluated each visualization independantly.

There are lots of different filters available in Bime, allowing you to zoom in and analyse any cross-section of your data. Log in now to try them out.

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How to connect your GoogleSpreadhseet data sources? from wearecloud on Vimeo.

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Three stone cutters were asked about their jobs. The first said he was paid to cut stones. The second replied that he used special techniques to shape stones in an exeptional way, and proceeded to demonstrate his skills. The third stone cutter just smiled and said: “I build cathedrals.”

From Maverick by Ricardo Semler

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2- Agile coding

Any fool can make things bigger, more complex, and more violent. It takes a touch of genius-and a lot of courage-to move in the opposite direction.

Albert Einstein

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3- It’s not how good you are, it’s how good you want to be.

Nearly all rich and powerful people are not notably talented, educated, charming or good looking.

They become rich and powerful by wanting to be rich and powerful.

Paul Arden

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4- The much underappreciated principle of pleasure

Rather than survey a bunch of users on every decision, the Mac team decided each issue among themselves, invariably going for the option that might amuse a user the most, that would give a user the most pleasure, and therefore imbue the Mac with personality.

Nick Merritt about Apple

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5- Creativity under pressure

Instead of traveling by corporate jet, GM CEO Rick Wagoner will drive a Malibu hybrid for the 520-mile trek from Detroit to Capitol Hill; Ford CEO Alan Mulally will drive an Escape hybrid. The travel plans of General Bob Nardelli of Chrysler are secret for “security reasons” (guess he’s flying). (Update: Chrysler got wise and said he’s driving, too.) All three are to present their plans for $25 billion in taxpayer-backed loans on Thursday and Friday.

If they were very smart, the CEOs would drive unreleased, next-generation cars that get 100 MPG. They’d stop in a half-dozen towns along the way and invite a newspaper editorial board writer to ride shotgun for a dozen miles. They’d update their status on Twitter. They’d write a few posts for the company blog. They’d shoot video on a Flip camera and talk about how they screwed up at their first appearance, how they’re selling their fleet of corporate jets, and their plans for the future. If they behave like real people instead of CEO machines, they might arrive in D.C. backed by some pretty good word of mouth.

In other words, they would prove that in these recessionary times they know what it means to be entrepreneurial, not imperial.

Ben McConnell

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7- Gartner definition of Business intelligence

An interactive process for exploring and analyzing structured and domain-specific information to discern trends or patterns, thereby deriving insights and drawing conclusions. The business intelligence process includes communicating findings and effecting change.

8- Common Pitfalls in Dashboard Design

Dashboards are only as valuable as they are able to deliver—clearly, accurately, and efficiently—the important information that people must monitor to do their jobs. Dashboard vendors should be spending their time figuring out the best ways to support this need, rooted firmly in an understanding of visual perception (how people see) and human intelligence (how people think). Instead, most of them are spending their time creating the cutest, most photo-realistic gauges, meters, and traffic lights imaginable. This considerable effort does little to improve a dashboard’s ability to communicate, and in most cases actually achieves the opposite effect. Dashboards are not video games, they are computer-based displays for vital business communication. To build products that enable the development of effective dashboards, software vendors must lift their heads above the fray of feature-function competition and look past the tempting candy (the empty calories of superficial glitz) that is dangling right there in front of their eyes. They must step back and look around long enough to remember what business intelligence is all about, and then take the time to design software that really works and really matters.

Stefen few

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9- Tufte’s best principle

Above all else show the data

Edward Tufte in “The Visual Display of Quantitative Information”

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10- Jelled team

A jelled team is a group of people so strongly knit that the whole is greater than the sum of the parts. The production of such a team is greater than that of the same people working in unjelled form. Just as important, the enjoyment that people derive from their work is greater than what you’d expect given the nature of the work itself. In some cases, jelled teams working on assignements that others would declare downright dull have a simply marvelous time.

Once a team begin to jell, the probability of success goes up dramatically. The team can become almost unstoppable, a juggernaut for success. [...] They’ve got momentum.

Peopleware by Tom DeMarco and Timothy Lister

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