Wednesday Feb 26, 2014

Oracle repeats as BI and Analytics Leader in Gartner MQ 2014

For the 8th consecutive year, Oracle is a Leader in Gartner’s Magic Quadrant for Business Intelligence and Analytics Platform. Gartner declares that “the BI and analytics platform market is in the middle of an accelerated transformation from Business Intelligence (BI) systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization.” Oracle offers all these wide-ranging capabilities across Business Intelligence Foundation Suite, Advanced Analytics and Real-Time Decisions.

Gartner specifically recognizes Oracle as a Leader for several key reasons. Oracle customers reported among the largest BI deployments in terms of users and data sizes. In fact, 69% of Oracle customers stated that Oracle BI is their enterprise BI standard. The broad product suite works with many heterogeneous data sources for large-scale, multi-business-unit and multi-geography deployments. The BI integration with Oracle Applications, and technology, and with Oracle Hyperion EPM simplifies deployment and administration. Not cited in the Gartner report is that Oracle BI can access and query Hadoop via a Hive Oracle Database Connector eliminating the need to write MapReduce programs for more efficient big data analysis.

“The race is on to fill the gap in governed data discovery,” professes Gartner. In this year’s MQ, all the Leaders have been moved “westward,” to the left, to open up white space in the future for vendors who address “governed data discovery” platforms that address both business users’ requirements for ease of use and enterprises’ IT-driven requirements, like security, data quality, and scalability. Although in Gartner’s view no single vendor provides governed data discovery today, Oracle Endeca Information Discovery 3.1, which became available in November 2013 after Gartner conducted the MQ report, is a complete enterprise data discovery platform that combines information of any type, from any source, empowering business user independence in balance with IT governance. Users can mash-up personal data along with IT-provisioned data into easy to use visualizations to explore what matters most to them. IT can manage the platform to meet data quality, scalability and security requirements. Users can benefit from additional subject areas and metadata provided by integration with Oracle BI.

Gartner additionally cites other Oracle strengths such as more than 80 packaged BI Analytic Applications that include pre-built data models, ETL scripts, reports, and dashboards, along with best practice, cross-functional analytics that span dozens of business roles and industries. Lastly, Oracle’s large, global network of BI application partners, implementation consultants, and customer install base provide a collaborative environment to grow and innovate with BI and analytics. Gartner also cites the large uptake in Oracle BI Mobile enabling business users to develop and deliver content on the go.

Tuesday Feb 25, 2014

Big Data and Analytic Top 10 Trends for 2014

Oracle’s Top 10 Big Data and Analytics Trends for 2014 are now here!  Read what hundreds of IT decision makers are saying about their big data and analytics plans in mobile, cloud, Hadoop, discovery, predictive, and decision optimization technologies, practices, and skills.

[Read More]

Tuesday Apr 09, 2013

Big Data Analytics - Advanced Analytics in Oracle Database

That's the title of a new white paper we've just posted. From the executive summary:

Big data doesn’t only bring new data types and storage mechanisms, but new types of analysis as well. In the following pages we discuss the various ways to analyze big data to find patterns and relationships, make informed predictions, deliver actionable intelligence, and gain business insight from this steady influx of information. 

You can check it out here.

Tuesday Mar 05, 2013

Reducing Hadoop TCO

I've been to a number of big data trade shows over the last year, and without fail I have the same conversation with many different people. It goes something like this.

 We discuss Oracle's Big Data Platform and I mention the Big Data Appliance (BDA). "Oh, yes" they say. "That's a great looking machine, but we can build a Hadoop cluster much cheaper than that, so we're not interested." 

 The first thing I do is ask them what kind of cluster they are building. They always say something like "I can get 40 $5K servers in a rack for $200K".

"But that's not an equivalent cluster," I will say. The most important number in Hadoop clusters is the amount of storage. When was the last time you heard somebody talk about a 400 core Hadoop cluster? They always say how many terabytes (or even petabytes) their cluster can store. Those smaller servers often only have a few TB of storage, compared with 36TB on each BDA node. So we quickly establish that their equivalent cluster is no such thing. Often it would actually take 2 or 3 such racks to match the capacity of the Big Data Appliance and their "equivalent" system is much more expensive than they thought.

But it's not just about buying servers. When you buy an engineered system you're also getting the rack, the cables, the switches, pre-installed software, tuning, optimization, integrated support and so on. Add those into the picture, and the Big Data Appliance is much lower cost.  Take a look at this ESG white paper that goes through all the numbers in detail. Here's the key segment from the executive summary:

"Based on ESG's modeling of a medium-sized Hadoop-oriented big data project, the preconfigured Oracle Big Data Appliance is 39% less costly than a “build” equivalent do-it-yourself infrastructure. And using Oracle Big Data Appliance will cut the project length by about one-third."

If you're building a Hadoop cluster, or looking to expand an existing one, you should keep Oracle Big Data Appliance on your shortlist and give it a closer look. 

Tuesday Feb 07, 2012

Big Data Analytics – The Journey from Transactions to Interactions

Big Data Defined

Enterprise systems have long been designed around capturing, managing and analyzing business transactions e.g. marketing, sales, support activities etc. However, lately with the evolution of automation and Web 2.0 technologies like blogs, status updates, tweets etc. there has been an explosive growth in the arena of machine and consumer generated data. Defined as “Big Data”, this data is characterized by attributes like volume, variety, velocity and complexity and essentially represents machine and consumer interactions.

Case for Big Data Analysis

Machine and consumer interaction data is forward looking in nature. This data available from sensors, web logs, chats, status updates, tweets etc. is a leading indicator of system and consumer behavior. Therefore this data is the best indicator of consumer’s decision process, intent, sentiments and system performance. Transactions on the other hand are lagging indicators of system or consumer behavior. By definition leading indicators are more speculative and less reliable compared to lagging indicators; however, to predict the future with any confidence a combination of both leading and lagging indicators is required. That’s where the value of big data analysis comes in, by combining system and consumer interactions and transactions, organizations can better predict the consumer decision process, intent sentiments and future system performance leading to revenue growth, lower costs, better profitability and better designed systems.

So, which business areas will benefit via big data analysis? Think of areas where decision-making under uncertainty is required. Areas like new product introduction, risk assessment, fraud detection, advertising and promotional campaigns, demand forecasting, inventory management and capital investments will particularly benefit by having a better read on the future.

 Big Data Analytics Lifecycle

The big data analytics lifecycle includes steps like acquire, organize and analyze. Big data or machine/consumer interaction data is characterized by attributes like volume, velocity and variety and common sources of such data include sensors, web logs, status updates and tweets etc. The analytics process starts with data acquisition. The structure and content of big data can’t be known upfront and is subject to change in-flight so the data acquisition systems have to be designed for flexibility and variability; no predefined data structures, dynamic structures are a norm. The organization step entails moving the data in well defined structures so relationships can be established and the data across sources can be combined to get a complete picture. Finally the analysis step completes the lifecycle by providing rich business insights for revenue growth, lower costs and better profitability. Flexibility being the norm, the analysis systems should be discovery-oriented and explorative as opposed to prescriptive.

Getting Started

Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyzes them alongside your existing data to find new insights and capitalize on hidden relationships. Learn how Oracle helps you acquire, organize, and analyze your big data by clicking here.

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