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December 13, 2010

Bime Tip: Decompose, Drill Through and Focus features

In the new version of Bime, you can use the Decompose, the Drill-Through & the Focus features. Here's a quick tutorial on how each one works.



Decompose

If you choose to decompose then you can analyze data using a different axis (i.e. a different attribute).

The cartesian and pie charts allow you to 'decompose' your visualization, both on queries and on dashboards (if allowed in the dashboard preferences). Click on a data range and a pop-up appears, offering you this option. Click, and you can select to which attribute you want to dig down into.

For example, this pie chart shows results based on one attribute (here, 'Year(Ship Date)') - but you can focus in on one element of that (here '2005') by using decompose.

Having opted to decompose using 'Sales Channel', the chart changes to show results only by the chosen Year, which has been moved to be a filter.

You can continue to 'pivot' your data by altering the filter. Alternatively, decompose another segment of the pie and each new view adds that segment's attribute to 'filters', enabling it to focus in on that segment, and uses the new chosen attribute in 'columns'.

Thus, decompose allows you either to drill down into the detail, or 'flip' the visualization from being defined by one attribute to another.



Drill through

If you choose to drill through then you can visualize the underlying data in each query.

In the pivot table, you can use the Drill Through feature on cartesian and pie charts to see underlying data for a selection of data points, displayed as detailed lines. For instance, the following figure illustrates the result of a query for which we visualize the Shipping Cost per Region. Let us imagine you want to see the underlying data for the Region Central. To do this, click on the Central horizontal bar and then click on the Drill Through item.

Once you click on Drill-Through for selected data points, Bime retrieves & computes all the underlying data and displays them in the Drill-Though pop-up illustrated by the image just below. In our current example, the Drill-Through operation has retrieved 2100 underlying lines (i.e. 21 pages of 100 lines). Consequently, keep in mind that the more numerous the underlying data are, the longer the Drill-Though computation time.

In the Drill-Through pop-up, you can navigate through the underlying data with the 'Next' & 'Previous' page buttons, visualize a specific page directly through setting the index of the wanted page in the numeric stepper, and exporting the underlying data as an CSV file through clicking on 'CSV'. In addition, you can modify the number of lines which are displayed per page. Then, click on 'OK' to close the drill-though pop-up.

To conclude, please note that the Drill-Through feature is available only on in-memory & RDBMS connections.





Focus


If you choose to use focus then you can restrict and execute an original query of the dashboard only for elements you have selected.

In the pivot table, the focus feature is a shortcut to filter elements visually. As illustrated in the following figure, let us imagine we are analyzing the profits we realize per customer segments.

Let us imagine now that we want to restrict our analysis to the Corporate & the Home Office customer segments. For this, we have only to multi-select Corporate & Home Office, and then click 'Focus on selection'.

As a result, the current result is restrict for Corporate and Home Office, as illustrated by the following figure.

Posted by We Are Cloud at 8:15 AM | Comments (24)

December 7, 2010

Pre-packaged dashboards or ad-hoc analysis: what's the best ?

When you provide pre-packaged dashboards, people want to be able to do ad-hoc analysis. When you give them a powerful BI tool for great ad-hoc analysis..they want pre-packaged dashboards. So what is the best solution, to provide one or the other, or both? We gathered some inspiration from LinkedIn members.

First of all it is important to note that pre-packaged dashboards and ad-hoc analysis tools have different use cases. Dashboards with already in-built queries are often used for a quick overall glance at data (what and how), whereas ad-hoc analysis is used to drill down deeper into the data (why). Pre-packaged dashboards are likely to be more useful to top level management. Ad-hoc analysis may be preferred by analysts and power users.

 

Why pre-packaged dashboards?

 

Pre-packaged can bring the quickest ROI, and if this is what an analyst is looking for then there is no reason why a pre-packaged solution could not sufficiently meet the needs of the organization. Getting results to users quickly can be a top priority for some people and pre-packaged functionality can bring speed to the process. Good for general trends and KPI measurement, dashboards should contain a mix of standardized and custom KPI's, as every organization is different, and an out-of-the-box solution is not likely to suit them all.

 

Why ad-hoc analysis?

 

Members of the LinkedIn discussion felt strongly that ad-hoc analysis was essential at some point, as pre-packaged dashboards are no replacement for custom analysis. With a pre-packaged solution there is the danger of having a "false sense of security" regarding your results, because of the level of detail a pre-packaged dashboard provides, which can lead to viewers jumping to the wrong conclusions. The order in which they should be used also cropped up in the discussion - and it was established that a pre-packaged dashboard should always be used before ad-hoc analysis. Why? Since it gives everyone a common set of metrics of which to base performance on. If you supply ad-hoc first, different people will begin basing their analyses on different criteria, which makes a useful dashboard more difficult to create. The advantage of ad-hoc reporting is the flexibility it gives.

 

Conclusion

 

To achieve the best of both worlds, our team is working hard to build a marketplace with dashboards for your department. Regarding the issue of quick ROI, we are thinking about the pricing of each: depending on number of stars, etc.. so who better to ask than our readers and customers? For a pre-packaged dashboard, with useful KPIs, which is ready within one hour, what would you be willing to pay?

We'd also like to thank those who contributed to the discussions we set up:

From LinkedIn: Christophe Dejoie, Shawn Hayes, Dmitry Gudkov, Ralph Winters, Hrvoje Smolić, Pedro Perfeito.

From Focus.com: Darren Bond, Kevin, Benjamin Breeland, Robert A. Marchello, Gil Hakami.

If you have an opinion on the topic we'd love to hear it too. You can comment on this post, or access the LinkedIn discussion or the Focus.com discussion.

Posted by We Are Cloud at 9:15 AM | Comments (690)

December 3, 2010

Which chart should I use and when? A guide to dashboard charts

With the advanced offerings of many BI solutions nowadays, you are often met with more types of chart than you know what to do with. But, remember, they are there for a reason : each one is useful in its own way. The trick is just knowing which one to use, and when. Hopefully this article will give you a better understanding of some of the more common charts you might be using.

To the casual user it may appear that different charts can simply be used interchangeably, and this is often the case, but not always the best solution. Experts have come up with guiding principles that can be applied to achieve maximum data visualization effectiveness. This also comes at a time when viewing data on mobile devices is becoming more and more popular, and this should be taken into account too.

Basic charts consist of:


  • Bar charts (and column charts)

  • Line charts

  • Point/Bubble charts

  • Pie charts

Why are they considered basic? Because in their most basic form they measure one element against one or more quantitative metrics associated with that element.

Take the bar chart. This should be used to display one element (product), against one quantitative metric (profit) or several quantitative metrics (profit and number of items sold). Because bar charts can become difficult to read if you add too many metrics, they work better if you use efficiently the space you have available.

Time or date dimensions are likely to be better displayed using either a line or a point/bubble chart. Just as with bar charts, multiple quantitative metrics can be displayed in a single chart, assuming that the chart does not get too crowded. Adding a mean statistic line onto a line chart can also be easier to read than for example adding one to a bar chart, and can show how far a measure has deviated from the average. However it is important to remember that these types of statistics should only be added in cases where they contribute positively to the understanding of the data, and only where they are justified - there is not much point adding a trend line to 3 data points or less because it implies a trend that probably does not exist!

The point/bubble chart is in it's simplest form basically a line chart but without the lines. It plots each data point as the line chart does and just does not connect them with a line. They are useful for displaying a single quantitative metric, but can be confusing if trying to display multiple metrics, unless the software you are using is designed particularly well. Ways of doing this could be encoding by color, or size for example. Joining the points on a point chart using a curved line can be visually appealing but also misleading in terms of accurate data analysis. This is because a curved line suggests that a polynomial relationship exists - which may or may not be true. Equally turning the lines of a line graph into curves can have the same effect. Avoid it if it changes the meaning of your data!

Last of the basic charts is the pie chart. Pie charts are ideal for showing data that has a number of components and only one quantitative metric. They require that the sum of the component values add up to 100% and that there are no negative components. For data points that are very small, grouping them into a category together ("other") is a good idea, to avoid crowding and confusion. Visualization gurus suggest that the maximum number of data points to display on a pie chart is between 7 and 10, although physically you could display any number of segments.

There are other types of chart which are perhaps less basic, but equally as common in dashboard apps.


  • Sparklines

  • Bullet charts

  • Guages

These can all be used to measure actual vs anticipated performance, current vs previous performance, and deviation from expected trends or benchmarks. They are also useful for giving a top-level overview for those that don't have the time or desire to examine data in great detail.

Sparklines are designed to show overall trends, as well as highlight the most recent data point in the context of the history of the data. Sparklines should be used to provide immediate and unambiguous trend comparisons among related data entities.

The bullet chart is common for displaying a quantitative value and a horizontal scale. This version of a bullet chart adds additional context by displaying a threshold via the black vertical line, and also encodes a qualitative measure using color. When should you use this type of chart? Comparing related metrics with a bullet chart can give you a quick snapshot of your data, so when you are looking for an instant comparison this is a good choice.

The guage uses a needle to indicate the current value of a metric. As with bullet graphs, guages make visual comparisons quick and easy. Although often visually appealing, these visualizations do not tend to use dashboard space effectively, so try to use them when space permits, and not at the expense of, say, bullet charts.





Final note

A problem you may come across in your dashboard once you have chosen all your charts carefully is that of data update. When you update data, this could skew your results considerably, and render some of your visualizations less effective. Unfortunately there is not much that dashboard designers can do about this, it is just a matter of you as the creator to test the extremes of your data set, if possible, and choose your visualizations with the results in mind.

Of course Bime incorporates all the types of chart I have discussed above, plus more. So next time you use Bime to analyze your data, I hope you are able to make the best visualization choices and use Bime to its full potential!

Posted by We Are Cloud at 10:15 AM | Comments (118)

November 25, 2010

Bime gives you real web analytics accuracy

If you are a marketer or web analyst, how can you be sure that visitors are actually engaging with your site?

Google Analytics can give you a high level overview - it can tell you the average time spent on your site for a period of time, how many page views you had and even the percentage that translates to during your specified period of time.

But what if you could go even further and actually identify that over the past 3 months, only 20% of your visitors are actually staying for longer than 3 seconds on a page?

Let's use a very basic hypothetical example. Say you had 100 visitors to your site on Monday. Google Analytics can instantly tell you that you had 100 visitors, and the average visitor spent 2 minutes browsing and visited 5 pages. It will even tell you that over the period of one week, you received 30% of your page views on Monday alone.

Good information to have, but not exactly ideal if you need more detailed information to make constructive changes to your marketing plan.

How do you know if 15 of your 100 Monday visitors spent 10 minutes each on your site, and the remaining 80 only spent 5 seconds each, bringing the average down? An even worse scenario... imagine that Google Analytics informs you that your average visitor visits 5 pages on each visit, but the harsh reality is that 80% of your visitors visit the home page and promptly leave because they can't figure out what you are selling. Turns out the average has been skewed by a bunch of your technical colleagues who have spent the week checking links and loading times on every single page on your website.

But here you are, thinking that your website is well-optimized for the product you are selling, and you therefore miss a huge opportunity and make no changes to your home page.

This is where Bime saves you. What if you could drill down into your data, and see exactly what percentage of your visitors are actually engaging with your content?

There are a number if ways to do this, but we'll just keep it simple. It's relatively safe to say that visitors spending over 5 seconds on the site, are probably reasonably engaged with the content, or have at least read the first few lines of copy.

Using Bime, you can create dynamic segments with this value, then apply it to your data.

dynamic segment

By creating the dynamic segment above, you are essentially saying "segment all the visitors that spent more than 5 seconds on my website".  When you use this segment to look at your data, you get a nice simple column chart like this:

chart

Nice, but does not really tell you all that much more.  Enter Bime's post-processing options. 

post processing percentage  

Choose the type of post-processing you want to do.  In this case we'd like to see a percentage.

 pp percentage

Then by choosing where to apply it, you can modify your visualization to look how you want it to.

This will give you something much more useful, like so:

graph

You can even switch between clustered and stacked visualizations - whatever you prefer to look at and find easier to read :

stacked

You can see that the total number of visits has been set to 100% for each month, as you want to focus on how many of your current visitors are spending more than 5 seconds on the site, not how many visitors you are actually receiving. Now we can instantly see that for example February was your best month and September your worst.  We can confirm that the amount of time spent on your site is getting progressively worse by adding a trend line:

trend line

Nothing is stopping you from analyzing other elements too - page views, time spent on page, time spent on site are all good ones. Simply create more dynamic segments with your chosen values.

choose segment

This is an example of some of the amazing things you can do with Bime (check out the main website for others!), and how easy it is to do them. We are not saying that Google Analytics is not a good analytics platform - quite the opposite in fact - Bime simply enhances its abilities and makes your data even MORE useful!

Posted by We Are Cloud at 9:45 AM | Comments (2)

November 22, 2010

Panel Discussion Video: Business Intelligence in the cloud

We stumbled across this nice little video of three experts talking about Business Intelligence in the cloud. They are: Shawn Rogers, currently Vice President of Research at Business Intelligence at Enterprise Management Associates; John Myers, Principal Consultant and Senior Analyst at Blue Buffalo Group; and Donald Farmer, a principal program manager of the SQL Server Business Intelligence management team at Microsoft.

Listen to them discussing what you should be thinking about when moving your BI to the cloud.

Posted by We Are Cloud at 6:30 AM | Comments (124)

November 15, 2010

Surveys Reveal SaaS BI Market Growth

According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago - 15% in 2009 compared to 7% in 2008.

An article by InfoWorld’s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.

So why is interest in SaaS BI increasing? Where on-premise BI applications fall down is at the often massive upfront and ongoing development costs, especially for bigger organizations. Reach is also an important factor - BI tools can get to typically under-served users, such as front-office workers, a lot faster with SaaS deployment.

For example, a large retailer may have massive amounts of data related to sales, inventory levels, and many other things. In-house IT staff may not have the skills and experience needed to implement an on-premise BI solution with their existing system, while an off-the-shelf SaaS BI solution can potentially meet most of an organization's needs without the development expense and support headaches.

A host of SaaS BI vendors are taking advantage of that interest, with solutions like Bime starting to make their mark.

Posted by We Are Cloud at 4:15 AM | Comments (4)

November 3, 2010

What's the Next Big Thing in BI?

Taking our inspiration from a thread at ebizq.com, we thought about what the next "big thing" in BI might be. Here we share with you some ideas that were brought to the table by thread participants.

How about "actual" business intelligence? In other words, moving beyond static reports on historic datasets to more interactive, analytic-supporting insights on business past and present performance, including of course more real-time, operational, intelligence. (Paul Vincent)

Another suggestion was End-User Driven Visualization. What does this mean? Large reports, even end-user driven ones are pretty useless if they just present large amounts of data in text format or similar. Some feel that users should not have to take it upon themselves to learn the ins and outs of data visualization themselves, when the tool itself could do it for you. Intelligent tools that can understand the context of the data being presented and can choose one or a few of the most appropriate ways of displaying the information would be huge.

Integration was next on the list. Rather than standalone products, BI will start to be incorporated with other solutions to provide analysis and trend data related to specific business processes or applications, particularly around B2B interactions. On-demand business intelligence and SaaS based solutions will come out ahead in this trend because of ease of integration. (Margaret Dawson)

The next big thing, according to Augusto Albeghi, is simulation and budget. The future of BI should lie in actual decision instruments that can easily model the impact of change on the existing business. This process has a lot to do with budget but current systems usually fix issues who are not perceived as such or are scarcely relevant to business.

What else was mentioned? Mobile BI on tablets, better predictive analytics, better data visualization, ubiquitous BI.

Outside of the thread, we also noticed that analysts such as Gartner have predicted a social future for business intelligence - i.e. businesses that leverage social media within their business intelligence software will gain an advantage over others. Business intelligence software developers should be taking note and making their analytics and reporting offerings more "social" by taking the technologies and principles behind Twitter, LinkedIn etc. and applying them to their solutions.

What do YOU think will be the next big thing in Business Intelligence?

Posted by We Are Cloud at 3:30 AM | Comments (979)

October 28, 2010

IT project management dashboard

IT project management dashboard

With this featured dashboard see various data for a typical IT project such as % overdue incidents per project and average time to repair per project. Explore visualizations such as the grid, the pie chart, the bullet chart and the bar chart.

Posted by We Are Cloud at 4:45 AM | Comments (990)

October 18, 2010

Next-gen BI, SaaS and cloud based platforms included in Top 15 Technology Trends EA Should Watch by Forrester

Forrester's Gene Leganza compiled a list of the hottest trends enterprise architects and IT managers need to watch over the next two to three years. Forrester began summarizing technology trends last year to help enterprise architects create their organizations' technology watch lists, and this year have identified 15 technology trends for enterprise architects to watch.

What were this year's categories? "Empowered" technologies, process-centric data and intelligence, agile and fit-to-purpose applications, and smart technology management.

This first item on the list was business intelligence, as it increasingly combines real-time access with pervasiveness, agility, and self-service: "Firms will increase their use of analytics to improve their speed of response to changing market conditions... IT needs to enable successful end user BI self-service to keep runaway BI costs in check." (Forrester, 2010).

Third on the list was the standard adoption of SaaS and cloud-based platforms: "Even if IT mostly uses IaaS and PaaS options, the business will benefit from rapid deployment... IT execs must triage where they invest their resources as well adopt new application support practices for SaaS, while completely re-evaluating capacity issues as well as architecture standards for applications developed or hosted in the cloud." (Forrester, 2010).

Another interesting technology identified in the list was the targeting of text and social networks with web analytics: "These new technologies bring powerful analytics to bear on rich areas as yet untapped for intelligence about customers and products... They will provide a challenge for information architects and tax the semantic capabilities of existing investments." (Forrester, 2010).

Other trends in the top 10 included: business rules processing and policy-based SOA moving to mainstream adoption, system management enabling continued virtualization, collaboration platforms becoming people-centric, and integration of customer continuity platforms with business apps.

Gene Leganza states on his blog the reason behind the annual publishing of The Top 15 Technology Trends EA Should Watch: "You need a technology watch list. You need to know what's coming, what's beginning to be adopted, what's reaching the mainstream now. And more than a watch list, you need a process for regularly evaluating the capabilities of emerging technology and mapping them to the needs of your organization."

Posted by We Are Cloud at 4:30 AM | Comments (0)

October 12, 2010

Periodic Table of Visualization Methods

Here is the Periodic Table of Visualization Methods from KPI Library. Click on the image below to access the interactive version. You can hover over each element for an example.

If we were to list the "elements" that exist in Bime that are on this chart, then they would be the following:

Tb = Table (in Bime we call this visualization 'Grid')
Pi = Pie Chart
L = Line Chart
B = Bar Chart
Ac = Area Chart
Sc = Scatterplot (we call this 'Bubble Chart')
Tp = Tree Map
R = Radar Chart Cobweb
Da = Data Map (we call it 'Heat Map')

Notice they all come from 2 groups: Data Visualization and Information Visualization.
Data Visualization is classed as "Visual representations of quantitative data in schematic form (either with or without axes)."
Information Visualization is classed as "The use of interactive visual representations of data to amplify cognition. This means that the data is transformed into an image, it is mapped to screen space. The image can be changed by users as they proceed working with it."

Bime also has other visualizations not included in this periodic table. These are:

  • Guage


  • Multi Chart


  • Bullet Chart


  • Column Chart


  • Sparkline

  • Do you know of any other interesting visualizations that are not displayed here? We would love to hear from you!

    Posted by We Are Cloud at 3:30 AM | Comments (412)