On the menu today: Bime Mobile to consume your dashboard on any device (we mean it), a premium -tailor made- connector for database.com, new calculations and improvements on several visualizations.

Entering Bime Mobile

As the industry is shifting to mobile, we are happy to announce that you can now consume your dashboard on iPhone, iPad, Android Phones and Android Tablets. Most people want native applications on their device not just the time consuming, hard-to-read-on-mobile web page reports you often get. We heard you! Bime Mobile is available right now on the App Store and on the Android Market. The experience of browsing dashboards and visualizations has been optimized for touch and mobile device.

Bime Mobile makes your dashboards consumable on all the major devices available today: Web, Mac Desktop, Windows Desktop and mobile devices. This opens new realms of user options as for the first time you can instantly push data from Google Analytics, Google Spreadsheet, all the major RDBMS and OLAP Engines, Excel, Salesforce, database.com and CSV file to your workforce, partners or customers, even if they are on the move.

Best of all? It’s free, it’s just included in our current offering at no extra charge.

Database.com connector

Few companies have been as disruptive as Salesforce.com. They almost invented SAAS, thus changing forever the way people build, deliver and consume software. You know what? They are about to do the same thing they did with CRM but with Databases. Database.com is basically a database-as-a-service. It’s built on top of the same tried and tested architecture that fuelled the CRM. So it’s multitenant, automatically backed-up and basically free from all the operational aspects of running a database.

Database.com is not a key-value store like most of the multitenant cloud database out there instead database.com is relatively similar to a relational database. So it’s good for BI, as you can join and aggregate data within a database.com query. Bime’s database.com connector leverage all that and comes into 2 flavors. This allows us to offer the same flexibility as our relational database connectors so you can take a snapshot of your data in Bime’s memory (the in-memory approach) or you use the power of the datasource and each of your actions is translated into the language of that datasource (the delegated approach).

The delegated approach offers the user some interesting features. Most importantly, you don’t have any pre-design to do when defining your connection in Bime. Enter your credentials and that’s it. You can query and browse all the objects available in the API.

Time comparison calculated measure

Why?

How to calculate the percent growth of profits for each month of the current year compared to last years?
How to compare the current month with the average of the last 5 months?
How to calculate the year to date actual turnover compared to the year to date budget ?

These are the kind of questions you can answer with Time comparison calculated measures. Comparison is one of the most common data analysis tasks and time is the most used dimension so we tried to make this analysis simple and flexible.

How?

Let’s say we want to answer our first question:
How to calculate the percent growth for each month of the current year compared to last years?

Let’s begin by creating our calculation.

To create a Time comparison calculated measure, you have to create it from the pivot table – just like any other calculation – by clicking on the calculation menu.

The form gives all you need to configure your calculation.

Define the base measure you want to use. In our case it will be profit as we want to calculate profits growth.

Next, let’s pick up the time dimension you want to compare. In our case we have only one, shipping date.

Then we have to choose the level. This one may seems a bit tricky at first, but is actually very simple once you get it. If you pick up “month” then this will compare months together, if you pick up “year” this will compare years together. In our case we want to compare months.

Finally, you’ll have to define the offset of the start and the offset of the end. In our case we want to compare the months of the current year with the same months of the past year. To do so, we have to define a -13 months offset for both start and end.

Let’s spend a bit of time on this and explain what Bime will do here. When on January 2011 it will go back 13 months and take the beginning of this month. Same thing with the end offset but with the end of the month.

If we had to answer the second question, compare the current month with the average of the last 5 months? We would have defined a start offset of -6 month and a stop offset of -1 month.

Once you are done, let’s save this and begin to use it. By dropping year and month in my query as well as my Profits measures I can see what’s going once I drop my Time comparison calculated measure.

By doing so I can see that for this calculation in january 2011 I have the data of January 2010. In February 2011 I have the one of 2010 so on and so forth. It worked!

To then get my percent growth of profits each month of the current year compared to last I just have to make a growth computation in classical calculated measure. With the formula: (Profits – Last year profit) / Last year profit * 100

Technical consideration.

If you want to understand a bit more of what’s going on under the hood please continue. Bime like most OLAP tools is context based, each cell in your result has a cell coordinate in the original data space. For instance, lets take from the previous example, January 2011. When Bime evaluates your time comparison caculated measures it understands it’s current context, that is January 2011, and based on your configuration knows how to move from this context to reach another, for example February 2011.

Calculation delegated to the database.

Bime has it’s own full featured calculation engine which provides a consistent set of features on top of all the data sources we support. Nevertheless sometimes you want to use the calculation engine of the underlying data source especially when it’s a relational database.

Why?

• Line by line calculation. Bime always works at the aggregated level. In the case of RBDMS we try hard to work on aggregated result as it gives a huge performance boost compared to working line by line. The drawback of this approach is that it prevents some calculations from working the way they should. Let’s take a simple example of the computation of Turnover = Unit price * Order Quantity.
  Bime in RDBMS delegated turns this formula into:
  Turnover = SUM(Unit price) * SUM(Order Quantity). As Bime works on aggregation, the calculation won’t give you what you want, the Unit price * Order Quantity computation on each line. With Calculatation delegated to the database now you can write this formula and ask the database to do the computation.
• Database specificity: some databases have special capabilities like statistical aggregators, geo spatial calculations which you can use straight from Bime.
• Performance. Some actions run faster in Bime some run faster in the database. All calculations that involves a lot of different elements will run faster in the database as we won’t have to retrieve all the data in Bime to do the computation.

How?

Create calculated measures or attributes that are delegated to the database. Use the SQL syntax of the database to write it, not the Bime syntax… and that’s pretty much it!

Enhanced pivot table layout.

In the last version of the pivot table, we gave all the query axis the same weight in the interface. Filters had the same space allowed as rows, explosions or trend line measures. That was wrong.

We learnt from usage that most have queries got one element (max) in explosion. Some queries can have a very large number of measures and filters, usually not that many in terms of row elements.

Now the user interface of the pivot table better reflects your usage. The explosion axis has 2 place holders. It can grow to more but the default is 2.

We have moved the filters place holder to the bottom of the chart. This leaves space to add lots of filters. This is especially important as those filters can be turned into data filter prompts in dashboards.

All that releases a lot space for the measures place holder to accommodate many more measures than before. Also an added benefit is that the measure in the browser are now very close to the measure’s query place holder so it’s much more convenient.

Mix calculation types together

Have you dreamt of creating a group out of a calculated attribute that is using a set based on another calculated attribute? Now it’s possible! Joke aside, more often than not you want to combine the calculation types to achieve the right data transformation and presentation and now it’s easily doable in Bime.

Anti-alphabetical sort

Bime has a lot of ways to sort. Some of them are simple and some are more advanced.

Before V3.5 Bime has 3 simple sort: Alpabetical ascending, Sort ascending on the first measure, sort descending on the first measure. Now you have a fourth simple sort: alpabetical descending.

Improved CSV export.

We gave some love to the CSV export: you can define in the account settings, the character of separator (; tab etc…) to suit your needs. We have also tried to make the export better by removing unecessary characters and column names in the export. To define you settings go to: Admin > Account.

Sortable data grid.

You can now sort the datagrid in your dashboards by clicking on the column names, however, this option is deactivated once the datagrid has grouped columns or complex results.

Extended Range Date.

This is a major update of the options you have to restrict the time frame. We included new elements like: “All history”, and flexible offsets.

Below is just a short example of how this setting can give you the data of the last complete month.

Fine grained control on data filters in dashboards.

You can now reorder and hide your filters in dashboards. Go in edit mode and click on edit the data filters. From here uncheck the filters you want to hide and click on the up / down button to reorder them. Filters have also been optimized in terms of performance and should be much faster.

Better color settings.

Bime has a(n) (overly?) clever feature for assigning colors. It’s a form of cache that affects colors automatically if the element has already be seen. For exemple, one column chart has an element called product 1 and bime will affect it a color red. If a second chart has an element product 1 then bime will also affect it a color red. This is really great to achieve consistency automatically in dashboards.

Nevertheless, it was also a pain. As soon as you begin to define precise color settings in your dashboards the cache would override the user settings and it was really hard to do what you want in fine tuning your color settings.

This has been updated. For the sake of retro compatibility your dashboard has the same behaviour as before but if you update the color settings you’ll see that you now have to choice:

• Let Bime set the color for you, like before.
• Define colors. This option will now garantee that the color will be applied in your chart even if elements of it have been seen before.

Copy and paste your calculations.

Bime is browser based but has a desktop-like user interface. Users expect the same capability as in their desktop apps. Consequently, you can now copy and paste most things in Bime, including your calculations. Just select copy and paste wherever you want – even in another connection.

Automatically update row selector with new values.

Let’s pretend you have a chart with a row selector containing product categories and all of them are selected. Your company launches a new product with a new category. By clicking on the header of the selector you can now tell Bime to select all the elements in it. In our example, the new product will be added to the list of selector AND be automatically selected.

This feature is especially useful in keeping your dashboard up to date without having to keep refreshing your data sources.

The theme for the next release will be performance. The aim is to make your analysis and dashboards run fast without having to do any particular tuning. We call this feature: out-of-the box performance.

It will involve a killer new feature called Bime DB and lots of optimizations. This will ship as usual with a bunch of UI and dashboard visualization improvments.

In our newsletter this month:

• » We Are Cloud Launches Bime V3.0 at DEMO Fall 2011
• » Watch Bime V3.0 in action
• » Bime V3.0 support documents
• » Dashboard of the Month: DEMO Conference Stats

Read it here!

We did follow the proceedings live on Twitter though (almost as good as being there) and saw that people really liked our tagline: “Mine Your Own Business”.

To hear the tagline being broadcast proudly on stage by Rachel herself, you can watch the 6 minute presentation below.

Want to get right down into the nitty gritty details? Read the QueryBlender documentation.

Don’t forget: there are more Bime videos for you to enjoy here.

The Query Blender is a big deal.

It allows you to fire analytical queries across multiple data sources.

No need to move your data around with ETL, no need to federate everything in a data warehouse. You can simply seamlessly query your different systems and join everything on the fly.

Mix Without Moving.

Like this video? Tweet it! » Tweet

Now you’ve seen what it’s all about, how about watching a real life example? Click play below!

Like this video? Tweet it! » Tweet

What do you think? Let us know in the comments!

Bime with QueryBlender Lets Companies Mine Their Own Business, Mix and Match Data Sets to Turn Questions into Valuable Insights

DEMO FALL 2011, SANTA CLARA, CA, September 13, 2011 — We Are Cloud, the “ICT European start-up of the year,” today announced the release of Version 3.0 of Bime, the global and elegant business intelligence (BI) service for the age of cloud computing, at DEMO Fall 2011.

With Bime V3.0, even small and medium-size enterprises can mine their own business with a powerful cloud BI service that turns big data into big dollars, fast. For the first time, its unique QueryBlender allows users to connect the dots between disparate, live data sets — no matter whether they reside on a local drive or are streaming through web services around the globe.

With QueryBlender, Bime is the first cloud BI solution that unlocks the power of federated databases, so users can query dozens of sources and make sense of them – regardless of query language, file, and meta data formats. QueryBlender lets users mix and match virtually any information, from legacy spreadsheets and large relational databases to live data streaming in from Google Analytics, Google Apps, salesforce.com, or Amazon Web Services.

“For the past three years, we have been working with our customers from China to the Americas and Europe to build a BI solution that gives straight answers to hard-core business questions,” said Rachel Delacour, We Are Cloud co-founder and CEO. “Bime is easy to use, fast to deploy and so affordable that every manager and knowledge worker can tap into the sea of data we’re all swimming in. Bime V3.0 lets everybody shine new light on their data so they can make better decisions faster.”

Bime V3.0 is a pioneering BI service born in the cloud and built for the cloud. It runs in any browser and lets users within minutes explore, visualize, publish and share important insights from their enterprise and the Web at large. The new product makes collaborative decision-making for a mobile workforce and global organizations a snap.

“Everyone is awash in data, but starved for information,” said Nicolas Raspal, CTO and co-founder of We Are Cloud. “With We Are Cloud, you can ask almost any question and see an answer in milliseconds. Our customers can effortlessly work with any type of data, no matter what the format or the location — that’s the key competitive advantage they want. We deliver business intelligence at the speed of thought.”

Co-workers and external partners get an instant read on important developments and trends through live dashboards and widgets they can access on the go. Dashboards and widgets can be emailed or embedded on any web page, while owners retain full control over who can access the data visualization and for how long.

Bime V3.0 requires no large downloads, no time-consuming set-up and no tedious uploads of data sets. Companies can tap into the power of cloud BI without giving up control over their data, yet are able to perform sophisticated calculations fast, thanks to a proprietary in-memory OLAP calculation engine. Bime V3.0 scales with each user’s individual requirements. They can choose to seamlessly upload data into its Déjà Vu distributed cache, or work on millions of data sets by harnessing the power of the most powerful database stacks and cloud computing platforms, from Google and salesforce.com to Hadoop MapReduce.

About We Are Cloud:

We Are Cloud is the creator of Bime, a pioneer solution of cloud BI, born in and built for the cloud. Bime delivers simple-to-use yet powerful BI, analytics, data visualization and dashboarding in a software-as-a-service application, making data analysis fast, easy and low-cost. We Are Cloud was founded in 2009 in Montpellier, an academic R&D hotbed in the South of France. The company has received several rounds of angel funding. We Are Cloud has been recognized as a trailblazer in the cloud BI field, most recently as “Best European Start Up of the Year” at ICT Spring 2011 in Luxembourg and “Best Cloud Application” by the Cloud Computing World Series Awards in London. To find out more, visit: http://bimeanalytics.com.

About DEMO:

Produced by the IDG Enterprise events group, the worldwide DEMO conferences focus on emerging technologies and new products innovations, which are hand selected from across the spectrum of the technology marketplace. The DEMO conferences have earned their reputation for consistently identifying cutting-edge technologies and helping entrepreneurs secure venture funding and establish critical business. For more information on the DEMO conferences, visit http://www.demo.com/.

Media Contact:
Andrea Heuer
Heuer Media for We Are Cloud
press@wearecloud.com
+1-917-886-5113

You can access the detailed summary of the new features here.

The main features of this update:

• Grid Visualisation Enhancement
• Change the rendering of the dual axis
• Dashboard Home
• New Query And Connection Library
• Single Sign On (SSO)
• New Languages: Chinese and English (max compatibility)
• Calculations Improvement
• Want to mix your data? Contact us.

You can access the detailed summary of the new features here.

Don’t hesitate to contact us if you have any questions: wecare@wearecloud.com.

If you don’t already have a Bime account, now is definitely the time to get one!

Connect to your RDBMS from a web browser!

With V2.6 we introduced a subtle change but one with a big impact. You can now connect directly to your database from Bime web without using Bime Desktop.

Why is it important?

Because we had a lot of problems in the past with the Bime Desktop approach. Especially, with dashboards. It was fine to use Bime Desktop for designing the dashboard but you couldn’t publish a dashboard with full interactivity (ie: requiring the full dataset) or in real time (plugged into the database). Now it is super convenient as you can do everything from the browser (design and dashboard access).

How can I enable it?

From the Bime side: do nothing. In the browser Bime will just try to connect to the database with the credentials you gave. From the database side, you have to ensure that the database is accessible for the domain proxy.bimeapp.com and for the user credentials you setup in your connection. The documentation has been updated here.

What about Bime Desktop?

So why use Bime Desktop anymore? Bime Desktop is still the tool of choice to use for uploading snapshots of data to Déjà Vu, our distributed cache. Bime Desktop is still there for the scheduling refresh and also for loading Excel files.

Other enhancements

• You can now drill down and drill through from the maps.
• In Google Analytics, you can use a custom segment definition as a filter. Just to illustrate why this is useful, there is an interesting discussion here.
• The interface has been refined to fit on smaller screen resolution. More to come on this!

I hope you’ll like this as much as we do! If you haven’t got an account already, don’t forget to get one here.

More data visualization goodness: axis sharing between charts

What on earth is axis sharing? Let’s take an example, this query displays the profit per states exploded by region. As no single state can be in 2 regions, the result look like this:

(Click to see it full screen). This chart is really sparse because the same axis as been used for all charts. This is really great when you have to do comparisons, but in this case it’s better to have a specific axis for each chart. You now have a new option in the chart menu : explosion > share category axis that gives you this chart:

The same principle can be applied for numeric axes.

More data visualization goodness: sizing of gauges and bullet chart

You can now set the size of the Gauges and Bullet:

Better interaction with your connection: contextual member retrieve

For Google Analytics and RDBMS connections you have now by default a contextual member retrieve. What is that? We list only the elements available for the current query. If you have selected one region for example, we only list the state that belongs to this region. If you have a restriction on the year 2009, when you ask for the products, Bime only gives you the products sold in 2009. You can change this behaviour by clicking on the “Restrict to query” option.

Better interaction with your connection: rename and move attributes directly from the pivot table

You can rename elements (attributes and measures) directly from the pivot table. No more need to go back to the connection builder.

You can move an attribute to a measure and a measure to an attribute on the fly also without having to go back to the connection builder.

Why is it important? It saves a lot of time, as we learnt from your feedback, it’s a very common operation.

Export option in published charts

You can now restrict the export option in published dashboards.

We are very pleased to announce the release of Bime V2.5. It includes a lot of new features, including a major one: new compression schemes that will give you the ability to export more data inside Déjà Vu. It sounds geeky, but it is real game breaker and the good news is it is transparent for you, the user. Bime just runs faster while handling more data.

This is part one of a two part series.

Introducing the Cloud Pack Format: a smart compression scheme for data sharing in the cloud

In a nutshell, Cloud Pack Format is a new way of storing the data in memory and in Déjà Vu. The main benefit is it’s much smaller than the previous format of Bime that was already compressed compared to the original data source. The main user benefits are that putting data in Déjà Vu is faster, retrieving data from Déjà Vu is faster and that you can put more data in Déjà Vu. For RDBMS for example, we raised the limit in terms of number of rows from 500 000 to 3 million. It’s kind of cool to be able to analyze, share and eventually publish this amount of data online in a very simple and economical way: that is no infrastructure on your side and a cost effective model.

Obviously, the number of rows is only one part of the equation: the attribute cardinality (number of different values) and the number of columns also impact the limit. Anyway, it is almost a 10 fold increase in capacity than with our previous format.

Example?

This dashboard contains a dataset based on a 200MB datasets with 700 000 lines and it contains all the book titles sold on Amazon since 1951.

Load it here:

How to I enable Cloud Pack format?

In RDBMS, Excel and CSV connectors you have an option that is called compression. You can choose between Medium (the default), No compression (the previous format) and High.

How to I choose the right compression?

Compressed data need to be decompressed at some point. So there is a tradeoff in terms of query performance when you use the Cloud Pack Format. Medium compression is able to answer most of your queries without having to decompressed. In a lot of cases, it will even be faster than non compressed format. That is why now this is the default and the setting we advise if you don’t want to fine tune your connection. High compression compress a lot more the data in memory but slow down heavily complex queries. No compression is the old format that is in most cases the fastest to answer queries.

How does Bime handle any type of data set?

Long story short: Bime has a connector for any size on the data continuum. Obviously, with the Cloud pack, Bime can be your BI solution (except may be an ETL complex data manipulation) for small to medium datasets. The more the you go up in chain the more Bime is used as a “client” and delegates the heavy work.

Hint for a future release: the Hive connector.

More data visualization goodness: the funnel chart

Funnel charts are a type of chart, often used to represent stages in a sales process or conversion path in web analytics. This type of chart can also be useful in identifying potential problem areas in an organization’s sales processes.

More data visualization goodness: dual axes

Bar, Column, Line and Area charts now support dual axes. So you can now display two quantitative scales on a single axis (either X or Y).

As Stephen Few pointed out:

A graph should only include a dual-scaled axis [...] when needed to compare data sets that have different units of measure

Dual axis is a great addition to all you can use in Bime to analyse several measures at the same time regardless of their units. In previous versions of Bime you had color and size encoding and Log Axis. Now with the dual axis it’s even richer.

More Calculation goodness: Week numbers function

Week number is a tricky thing: do they begin on Monday, Tuesday etc…? This is the main reason why weeks are not part of the standard time dimension Bime build when it encounters a date. Nevertheless, the calculation engine has now an handy function called WEEK_NUMBER that takes a date in parameter and an offset. This offset is the day of week the week “starts” on for your locale – it can be from 0 to 6. If dowOffset is 1 (Monday), the week returned is the ISO 8601 week number.

Better data filters in dashboards

The previous version data filter prompt was not good:

Too much space between them, the text was centered, no max size in width gave scrollbar, selected elements were hard to relate to the attribute you were filtering…

Bime V2.5:

Public facing URL can now be changed

The user can now change the publication url. So instead of the default:

https://toto5.bimeapp.com/players/dashboard/A8ACE50029BE771D502A016…

you can have:

https://toto5.bimeapp.com/players/dashboard/my_customer_dashboard

This opens the door to the following workflow for managing the dashboard life cycle:

1- Create a dashboard and publish it in the wild by customizing the url.

2- Requirements change. Copy the dashboard and eventually also copy the underlying connections (in the dashboard menu click on duplicate and edit the connections) so everything is decoupled from the production dashboard and you safely change everything.

3- Once your changes are done just change the published url to the production one. It’s done. You have replaced your production dashboard. You can eventually delete the old production one if you want and cleanup the old connections.

This simple workflow can be extended to include staging or review steps.

Don’t forget to sign up for the free trial ASAP so you can test out the new features.