Announcing PowerDrill for Oracle EID 3.1

If you had distill what we at Ranzal’s Big Data Practice do down to its essence, it’s to use technology to make accessing and managing your data more intuitive, more useful.  Often this takes the form of data modeling and integration, data visualization or advice in picking the right technology for the problem at hand.

Sometimes, it’s a lot simpler than that.  Sometimes, it’s just giving users a shortcut or an easy way to do more with the tools they have.  Our latest offering, the PowerDrill for Oracle Endeca Information Discovery 3.1, is the quintessential example of this.

When dealing with large and diverse quantities of data, Oracle Endeca Studio is great for a lot of operations.  It enables open text search, it has data visualization, it enriches data, it surfaces all in-context attributes for slicing and dicing and it helps you find answers both high-level, say “Sales by Region”, and low, like “My best/worst performing product”.  But what about the middle ground?

For example, on our demo site, we have an application that allows users to explore publicly available data related to Parks and Recreation facilities in Chicago.  I’m able to navigate through the data, filter by the types of facilities available (Pools, Basketball Courts, Mini Golf, etc.), see locations on a map, pretty basic exploration.

The Parks of Chicago

The Parks of Chicago

Now, let’s say I’m looking for parks that fit a certain set of criteria.  For example, let’s say I’m looking to organize a 3-on-3 basketball tournament somewhere in the city.  I can use my discovery application to very easily find parks that have at least 2 basketball courts.

Navigate By Courts

Navigate By Courts

This leaves me with 80 potential parks that might be a candidate for my tournament.  But let’s say I live in the suburbs and I’m not all that familiar with the different neighborhoods of Chicago.  Wouldn’t it be great to use other data sets to quickly explore the areas surrounding these parks quickly and easily?  Enter the Power Drill. Continue reading

Data Discovery In Healthcare — 1st Installment

Interested to understand how cutting edge healthcare providers are turning to data discovery solutions to unlock the insights in their medical records?  Check out this real-world demonstration of what a recent Ranzal customer is doing to unlock a 360 degree view of their clinical outcomes leveraging all of their EMR data — both the structured and unstructured information.

Take a look for yourself…

Fun with Shapefiles: The Two Utahs

A little midweek enjoyment, courtesy of our Advanced Visualization Framework.  Below, you can see a county-by-county map of Utah and all of its Oil and Gas Fields.

You can wave over counties and fields and get some basic statistics related to the county or field that you are inspecting.  For fields, we have the oil/gas field status, the year it was opened and other basic information such as whether or not it was merged with another field.

We had to “dumb it down a bit” and put it in to an iframe (WordPress!) but you can still some of the detail.  It’s obviously not as flexible as our “real visualizations” (no zooming, no refining, etc.) that render inside of Oracle Endeca Studio but gives you a sense of how quickly and easily our technology incorporates advanced GIS data into a Data Discovery application.

Deploying Oracle Endeca Portlets in WebLogic

We’re long overdue for a “public service” post dedicated to sharing best practices around how Ranzal does certain things during one of our implementation cycles.  Past installments have covered installation pitfalls, temporal analysis and the Endeca Extensions for Oracle EBS.

In this post, we’re sharing our internal playbook (adapted from our internal Wiki) for deploying custom portlets (such as our Advanced Visualization Framework or our Smart Tagger) inside of an Oracle Endeca Studio instance on WebLogic.

The documentation is pretty light in this area so consider this our attempt to fill in the blanks for anyone looking to deploy their own portlets (or ours!) in a WebLogic environment.  More after the jump… Continue reading

What You Can Do…

Last week, we announced general availability of our Advanced Visualization Framework (AVF) for Oracle Endeca Information Discovery.  We’ve received a lot of great feedback and we’re excited to see what our customers and partners can create and discover in a matter of days. Because the AVF is a framework, we’ve already gotten some questions and wanted to address some uncertainty around “what’s in the box”.  For example: Is it really that easy? What capabilities does it have? What are the out of the box visualizations I get with the framework?

Ease of Use

If you haven’t already registered and downloaded some of the documentation and cookbook, I’d encourage you to do so.  When we demoed the first version of the AVF at the Rittman Mead BI Forum in Atlanta this spring, we wrapped up the presentation with a simple “file diff” of a Ranzal AVF visualization.  It compared our AVF JavaScript and the corresponding “gallery entry” from the D3 site that we based it on.  In addition to allowing us to plug one of our favorite utilities (Beyond Compare 3), it illustrated just how little code you need to change to inject powerful JavaScript into the AVF and into OEID.


Talking about the framework is great, but the clearest way to show the capabilities of the AVG is by example.  So, let’s take a deep dive into two of the visualizations we’ve been working on this week.  First up, and it’s a mouthful, is our “micro-choropleth”. We started with a location-specific Choropleth (follow the link for a textbook definition) centered around the City of Chicago.  Using the multitude of publicly available shape files for Chicago, the gist of this visualization is to display some publicly available data at a micro-level, in this case crime statistics at a “Neighborhood” level: It’s completely interactive, reacts to guided navigation, gives contextual information when you mouse over and even gives you the details about individual events (i.e. crimes) when you click in. Great stuff but what if I don’t want to know about crime in Chicago?  What if I want to track average length of stay in my hospital by where my patients reside?   Similar data, same concept, how can I transition this concept easily?  Well, our micro-choropleth has two key capabilities, both enabled by the framework, to account for this.  Not only does it allow my visualization to contain a number of different shape layers by default (JavaScript objects for USA state-by-state, USA states and counties, etc.), it also gives you the ability to add additional ones via Studio (no XML, no code). Once I’ve added the new JavaScript file containing the data shape, I can simply set some configuration to load this totally different geographic data frame rather than Chicago.  I can then switch my geographic configuration (all enabled in my visualization’s definition) to indicate that I’ll be using zip codes rather than Chicago neighborhoods for my shapes. Note that our health care data and medical notes are real but we de-identify the data, leaving our “public data” at the zip code level of granularity.  From there, I simply change my query to hit population health data and calculate a different metric (length of stay in Days) and I’m done! That’s a pretty “wholesale” change that just got knocked out in a matter of minutes.  It’s even easier to make small tweaks.  For example, notice there are areas of “white” in my map that can look a little washed out.  These are areas (such as the U.S. Naval Observatory) that have zip codes but lack any permanent residents.  To increase the sharpness of my map, maybe I want to flip the line colors to black.  I can go into the Preferences area and edit CSS to my heart’s content.  In this case, I’ll flip the border class to “black” right through Studio (again, no cracking open the code)… …and see the changes occur right away. The same form factor is valid for other visualizations that we’ve been working on.  The following visualization leverages a D3 force layout to show a Node-Link analysis between NFL skill position players (it’s Fantasy Football season!) and the things they share in common (College attended, Draft Year, Draft Round, etc.).  Below, I’ve narrowed down my data (approximately 10 years worth) by selecting some of the traditional powers in the SEC East and limiting to active players. This is an example of one of our “template visualizations”.  It shows you relationships, interesting information but really is intended to show what you can do with your data.  I don’t think the visualization below will help you win your fantasy league though it may help you answer a trivia question or two.

However, the true value is in realizing how this can be used in real data scenarios.  For example, picture a network of data related to intelligence gathering.  I can visualize people, say known terrorists, and organizations they are affiliated with.  From there, I can see others who may be affiliated with those organizations in a variety of ways (family relations, telephone calls, emails).  The visualization is interactive, it lends itself to exploration through panning, scanning and re-centering.  It can show all available detail about a given entity or relationship and provide focused detail when things get to be a bit of a jumble: And again, the key is configuration and flexibility over coding.  The icons for each college are present on my web server but are driven entirely by the data, and retrieved and rendered using the framework.  The color and behavior of my circles is configurable via CSS.

What’s In The Box?

So, you’re seeing some of the great stuff we’ve been building inside our AVF.  Some of the visualizations are still in progress, some of them are “proof of concept” but a lot of it is already packaged up and included. We ship with visualizations for Box Plots, Donut Charts, Animated Timeline (aka Health and Wealth of Nations), and our Tree Map.  In addition, we ship with almost a dozen code samples for other use cases that can give you a jump start on what you’re trying to create. This includes a US Choropleth (States and Counties), a number of hierarchical and parent-child discovery visualizations as well as a Sunburst chart. In addition, we’ll be “refreshing the library” on a monthly basis with new visualizations and updates to existing ones.  These updates might be as simple as demonstrations of best practices and design patterns to fully fledged supported visualizations built by the Engineering team here in Chicago.  Our customers and partners who are using the framework can expect an update on that front around the first of the month.

As always, feedback and questions welcome at product [at]