Business intelligence: at times, the phrase has seemed like an oxymoron. The task of identifying, extracting, and analyzing business data has never been easy, and every day it seems to get more complicated with the addition of new sources of unstructured content. With its latest announcement today, June 13, Endeca Technologies, Inc., aims to make business intelligence (BI) more agile with Endeca Latitude 2, the latest major release of the company’s enterprise agile business intelligence platform
Paul Sonderegger, chief strategist at Endeca, says, “The main thing that we’re trying to do is help turn big data into better decisions… To do that we’re building software that guides people to better choices both in life and at work.” To that end, Latitude 2 introduces new technology in the Endeca MDEX Engine and key capabilities that accelerate the time to deploy agile BI solutions. Among the features that help Latitude 2 achieve this goal are a new analytical core designed for intra-query parallelism that fully utilizes today’s multi-core processors, real-time data-driven schema changes, industry standard ETL integration, and the foundation for a next-generation semantic interface.
In his May 2011 report “It’s The Dawning Of The Age Of BI DBMS” Forrester VP and principal analyst serving business process professionals Boris Evelson writes, “Agility and flexibility challenges now represent BI’s next big opportunity. Business process professionals realize that earlier-generation BI technologies and architecture, while still useful for more stable BI applications, fall short in the ever-faster race of changing business requirements." This, of course, is just the problem that Endeca hopes to solve with Latitude 2.
But what does all of this mean in the real world? “Toyota had a big product recall last year and one of the real challenges was that these were new questions they needed to ask,” says Sonderegger. “They had no idea this was going to happen, so they didn’t have answers to questions.” Getting fast answers to these new questions—such as “Which models use a specific type of pedal assembly?”—meant bringing together data from data warehouse for vehicles, other internal applications, as well as information from NITSA boards, and warranty claims. These warranty claims provided a special challenge, says Sonderegger, who points out that this information is only partly structured with technician notes, misspellings, and more throwing curves at Latitude. Anyone who remembers the media attention Toyota’s recall garnered, will also recognize that time was of the essence for the automaker.
Latitude’s drag and drop capabilities, which make it simpler to build an interactive analytical app, came in especially handy when dealing with these complicated warranty claims. Sonderegger says that in this particular instance, Latitude 2 can help find the real cause behind a variety of related warranty claims.
With Endeca’s help, Toyota was able to bring its business and IT departments together, using Latitude’s agile delivery to answer questions and present information in a way that made sense to both sides. “Toyota found a technology they felt could make a difference,” says Sonderegger.
Evelson says, “[Latitude 2] can easily handle disparate, unlike data. For example in an office supply retailer or manufacturer one can’t describe writing products (pens, pencils) and office furniture with the same attributes (aka dimensions). With a regular database one would need to create complex structures to handle these. Endeca and similar products handle these with much less effort.”
In this space, Evelson says Attivio is Endeca’s main competition and there are some significant differences. “Attivio requires a custom built user interface. Endeca’s user interface is generated automatically,” he adds. “If you want to access Endeca or Attivio databases with third party tools, it’s a bit easier with Attivio, since Attivio can speak SQL (a standard database query language) and Endeca only speaks XQuery (query language specifically used for XML databases).”
“Traditional databases require a model. There are two parts to a model: logical and physical. Logical model describes how data entities are related to each other. Physical model is all about query optimization,” says Evelson. “With Endeca and similar products you don’t need a physical data model. Also, logical models can be automatically inferred from XML data sources. The result is that building a new or changing an existing BI application takes less time.”
Not all of Endeca’s efforts toward turning “big data into better decisions” have been behind the firewall. Also announced today was Endeca InFront, a customer experience management platform that enables businesses to deliver targeted and relevant customer experiences in any channel—all built on top of Latitude. Sonderegger says InFront “helps merchandisers better understand behavior of customers across all touch points.”
For instance, InFront can help a retailer who is seeing a decline in the sale of cotton sweaters determine why. By looking at an app with transaction data, user reviews, Twitter feeds and Facebook posts plugged in retailers can determine the problem. If a relatively common complaint like “too big” pops out, the system can filter out those mentions, drilling down by any number of qualities, such as color or price. That might then lead to the next most common complaint about those sweaters, perhaps the price, allowing retailers to decide what to do about the prices on this product to enhance sales.
Just as Evelson sees opportunity opening up for agile BI, Sonderegger says he sees a “sea change” happening—bring agility to the forefront. He hopes to help position Endeca at the forefront of that new wave. “What we’re seeing is a need for [better decision making] in both life and work… Consumers have been using the biggest body of data humanity has ever seen just in buying decisions,” he says. “We’re bringing innovations we pioneered in the online world into the enterprise.”