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Blogs > MWD Advisors

Oracle Endeca 3.1 - putting unstructured data analysis in the hands of more users
Helena Schwenk By: Helena Schwenk, Principal Analyst, MWD Advisors
Published: 14th January 2014
This work is licensed under a Creative Commons License
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It’s been just over two years since Oracle acquired Endeca for its unstructured data analysis and search capabilities. Since then the company has worked towards integrating and exploiting Endeca’s various technical assets in both the company’s web commerce and Business Intelligence products.

As part of this initiative Oracle recently released an update to its self-service discovery offering—Information Discovery 3.1—that packages up capabilities for self-service data mash-up and discovery dashboards, unstructured analysis and provides deeper integration with Oracle BI. As a whole this release is more squarely designed to help business users drive more value from their data, by helping them extend exploration and analysis capabilities to a greater range of sources and by easing the process of bringing in IT-provisioned sources defined in Oracle’s Common Enterprise Information Model.

Search and unstructured data analysis is often considered a complex technical challenge in BI circles. The irregular format and structure of the data means is not particularly compatible with traditional SQL-based query methods used in BI tools, making it hard for business users to ask questions of the data. Endeca helps solves this challenge.

When Oracle acquired the company it got its hands on its MDEX analytics engine—a technology designed to access, search and query unstructured data from sources including social media, websites, email and text alongside traditional structured data sources. One of its technical differentiators is a key-value store that supports various data types without the need for a universal data schema (an approach that has also grown in prominence alongside the NoSQL database movement). Instead, MDEX uses a Faceted Data Model that enables it to emerge and morph its schema in relation to the characteristics of the data, providing what could be described as a self-describing data model.

Endeca had two core offerings based on these technology underpinnings: InFront (designed to help businesses develop merchandising and content targeting tools for web commerce) and Latitude (used to develop analytic applications that bring together information from unstructured and structured information sources). Using these assets Oracle’s goal has always been to gain a stronger foothold in the unstructured data analysis market (as part of its wider Big Data strategy) and combine it with the company’s existing strengths in structured data management and analysis.

Information Discovery 3.1 (based on the former Endeca Latitude product) helps deliver on these aims. The offering, for example, allows organisations to create analytic applications that incorporate search and guided navigation capabilities for the analysis and exploration of multiple data types. Similarly the Studio tool enables business users to self serve by bringing together both enterprise and personal data (structured and unstructured) within pre-built templates and visualisations and in version 3.1 this includes the ability for data mash-ups.

Underneath the covers Endeca embeds comprehensive text analytic capabilities by applying algorithms to extract entities, concepts, summaries, and sentiment from unstructured data in a number of languages. In version 3.1 for example, business users can now perform their own light text mining to identify hidden concepts within raw text comments and fields and augment them with other forms of data. One area where we feel this is particularly useful and pertinent  is in the analysis of the voice of the customer/consumer. Information Discovery, for example, can be used to build applications that combine sales and marketing data loaded from a warehouse, BI app or CRM system alongside text fields or comments from consumers sources such as news articles, reviews, social media posts and blogs and so on. In this sense, Information Discovery acts as layer for pulling together and enriching both unstructured and structured data from which developers can build consumer and customer-orientated applications.

Given Endeca’s strengths it’s not surprising that Oracle is keen for the technology to play a wider role in other analytics platforms and offerings. Apart from Endeca Commerce, based on the former InFront offering, Endeca also runs as a component of Exalytics (Oracle’s in-memory analytics engineered system). Similarly, Oracle sees a fit where unstructured data is also integrated into ERP, CRM and supply chain applications too. It’s a strategy where Endeca can play an increasing role in helping to simplify the management and analysis of  unstructured data helping to alleviate the technical challenges to its access and putting it in the hands of more users.


Published by: IT Analysis Communications Ltd.
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