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.

Thursday Nov 17, 2011

Introducing the Industry's First Analytics Machine, Oracle Exalytics

Analytics is all about gaining insights from the data for better decision making. The business press is abuzz with examples of leading organizations across the world using data-driven insights for strategic, financial and operational excellence. A recent study on “data-driven decision making” conducted by researchers at MIT and Wharton provides empirical evidence that “firms that adopt data-driven decision making have output and productivity that is 5-6% higher than the competition”. The potential payoff for firms can range from higher shareholder value to a market leadership position.

However, the vision of delivering fast, interactive, insightful analytics has remained elusive for most organizations. Most enterprise IT organizations continue to struggle to deliver actionable analytics due to time-sensitive, sprawling requirements and ever tightening budgets. The issue is further exasperated by the fact that most enterprise analytics solutions require dealing with a number of hardware, software, storage and networking vendors and precious resources are wasted integrating the hardware and software components to deliver a complete analytical solution.

Oracle Exalytics In-Memory Machine is the world’s first engineered system specifically designed to deliver high performance analysis, modeling and planning. Built using industry-standard hardware, market-leading business intelligence software and in-memory database technology, Oracle Exalytics is an optimized system that delivers answers to all your business questions with unmatched speed, intelligence, simplicity and manageability.

Oracle Exalytics’s unmatched speed, visualizations and scalability delivers extreme performance for existing analytical and enterprise performance management applications and enables a new class of intelligent applications like Yield Management, Revenue Management, Demand Forecasting, Inventory Management, Pricing Optimization, Profitability Management, Rolling Forecast and Virtual Close etc.

Requiring no application redesign, Oracle Exalytics can be deployed in existing IT environments by itself or in conjunction with Oracle Exadata and/or Oracle Exalogic to enable extreme performance and best in class user experience. Based on proven hardware, software and in-memory technology, Oracle Exalytics lowers the total cost of ownership, reduces operational risk and provides unprecedented analytical capability for workgroup, departmental and enterprise wide deployments.


Click here to learn more about Oracle Exalytics.

 

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