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March 7, 2013

Data Agnosticism Part of Dell’s Quest

Isaac Lopez

One of the first challenges that organizations face when implementing a big data solution is gathering their disparate stores of data into a single location in order to process and turn it all into analysis.

According to Dell, at the center of all this is the ability for both data and systems to remain agnostic, freeing the ability to perform analysis on more variable data types. Datanami caught up with Joanna Schloss, product marketing manager at Dell, who explained why they believe their data agnosticism is central to getting organizations from data to analysis quicker.

Schloss, who joined Dell through the Quest Software acquisition, explained that Dell’s Toad application (a.k.a. Tool for Oracle Application Developers) addresses this challenge. Toad’s primary value proposition, says Schloss, explaining what she means by “database agnosticism,” is to access data wherever it may be including SQL, NoSQL, Sybase, IBM DB2 (among others), and coming soon, says Dell, Hadoop.

According to Schloss, there are some common themes to the data environment that Dell can solve uniquely particularly where it comes to synthesizing silos of data (including structured and unstructured, as well as governed and ungoverned). “We are database agnostic, and we are really actually data visualization and data end-tool agnostic. What we really need to be able to do is to aggregate and manage that content in a single thread.”

This agnosticism extends beyond the datacenter and into the cloud. Schloss comments that Dell’s acquisition of Boomi in 2011 means that they can also target cloud-based databases. ”Boomi’s primary claim to fame is that it can access data in and out of the cloud – so it can perform traditional ETL on premise and in the cloud, which is a very unique position for it to be in,” said Schloss.

But wrangling the data to the point where it can be virtualized for rapid consumption is only half the battle, says Schloss.  “Once people stand up all of this back-end infrastructure, the very next step they want is ‘how do I get value out of this infrastructure.’”  Dell’s answer to this can be found in their Kitenga Analytics suite, says Schloss. Dell announced last week that Kitenga has reached iteration 2.0, adding new search, indexing and sentiment analysis (among other features).

The primary purpose for all of the data aggregation happening in the back-end is analysis, comments Schloss. Kitenga analytics was brought on board to be a keystone of their big data analytics platform.  Kitenga addresses two key issues that organizations are facing as they wrestle with their data, says Schloss.

The first, she says, is the platform authoring side where Kitenga bundles open source technologies so that they can build map reduce jobs against Hadoop very quickly and easily through a drag and drop graphical interface.  This allows users to focus their energy on setting up ontologies which will be functional and customer specific so they can quickly get to analysis, explained Schloss. “If you can’t get to analysis, then you really don’t have any value.”

The second part that Kitenga focuses on, comments Schloss, is the consumption side, where Dell sees a shortage of data scientists.  “Dell’s VP of R&D recognized that when there is a shortage, there is the opportunity to innovate to help bridge that gap,” said Schloss.  “We recognize that people who consume this information are by no means data scientists, and don’t wish to be data scientists, so the other part of the Kitenga solution is highly visual.  It’s delivered through a portal, and is very easy to consume.  It has a very Google-esque interface for the search side, and provides all sorts of beautiful visualizations.”

Dell boasts that they have two customers using their end-to-end big data offering now, including an unnamed pharmaceutical company.  “Regulatory information was scanned in as PDFs and they became sort of a black hole of information,” explained Schloss.  “On top of that, web sites were coming up with different formulations of active compounds.”

Schloss explained that Dell’s customer needed to understand if the store of formulations that were being found on the internet met with the regulatory needs of the active ingredient.  Additionally, they needed to combine scanned unstructured data with volumes of web content and create a unified view for where the compound was active and whether or not it was in compliance.  “Kitenga gave the organization visibility, higher efficiencies, and enabled them to do analytics that they weren’t able to do previously,” said Schloss.

Related Articles:

Dell Announces Kitenga Analytics 2.0 for BI 

Dell Enters into Acquisition Agreement 

Quest Software Tops IDC Charts for Database Tools