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Big Data, Big Opportunity

David Baum

The amount of business data that is generated has risen steadily every year, especially since 2000, when the shift from analog to digital formats began in earnest. Today the digital revolution is in full swing. All kinds of information can be stored in binary format—not just data from enterprise applications, but also from Web pages; social media sites; email exchanges; search indexes; clickstreams; equipment sensors; and all types of multimedia files, including audio, video, and photographic. According to IDC, the world produced 1 trillion GB of data in 2010, fueled by billions of mobile phones, tens of billions of social media posts, and an ever-expanding array of networked sensors from cars, utility meters, shipping containers, shop floor equipment, point-of-sale terminals, and many other sources.

CIOs are trying to make sense of this massive influx of data and to develop analytic platforms that can synthesize traditional structured data with semistructured and unstructured datatypes. According to more than 1,000 CIOs and business executives that IDC interviewed last year, business analytics was rated as the #1 technology area that would enable their organizations to gain a competitive edge in the year ahead.1

Total data volume has grown rapidly since 2000 with pervasive digitization of contentFigure 1. Total data volume has grown rapidly since 2000 with pervasive digitization of content [Source: Hilbert and Lopez, Science, 2011].

Why the excitement? When properly captured and analyzed, big data can provide unique insights into market trends, buying patterns, maintenance cycles, and many other business issues—lowering costs and enabling more-targeted business decisions. A recent survey by the Independent Oracle Users Group (IOUG) found that approximately 48% of enterprises expect a significant or moderate increase in unstructured data analysis over the next five years.

The Possibilities
CIOs in just about every industry are waking up to the possibilities of big data. For example, manufacturing companies commonly embed sensors in their machinery to monitor usage patterns, predict maintenance problems, and enhance build quality. Studying these data streams allows them to improve their products and devise more-accurate service cycles. Advertisers use location data from telecommunications providers to target consumers when they are in close proximity to a store, a coffee shop, or a restaurant, and then respond with up-to-the-second promotions. Retailers depend on social media and Weblog files to improve customer segmentation and create tailored marketing campaigns. Social media sites like Facebook and LinkedIn use big data to personalize interactions among their members. Energy companies gather a tremendous amount of data as they monitor the flow of gas and liquids in their wells and pipelines. Careful analysis gives them an early warning about potential maintenance and supply problems.

One of the best examples of big data in action is “sentiment monitoring,” which entails aggregating data from call center operations, Web clicks, news feeds, blogs, and social media postings to identify positive or negative feedback about a company, product, or campaign.

Electronic sensors monitor not only mechanical and atmospheric conditions, but also the biometrics of the human body. In healthcare there is a huge opportunity not only to improve patient outcomes but also to monitor trends in healthcare diagnoses, treatments, and claims to make better clinical and administrative decisions.

Although each of these industry examples illustrate the potential of big data in specific instances, the opportunities become even more compelling once data is analyzed in aggregate form. If you can aggregate the data from thousands of patients, you can see how changes in biometrics are a precursor to serious medical conditions, possibly preventing a stroke or heart attack. If a thousand customers echo a similar preference, a thousand sensors reveal a pattern of equipment failure, or a thousand cardiac monitors show a correlation between biometric levels and adverse reactions, then we can begin to turn trends into predictions—and ultimately use big data to take corrective or pre-emptive action.

The Technology
Traditional business data is easily structured into columns and rows and stored in a relational database. Big data is often unstructured (audio, video, photographic) or semistructured (media posts, geospatial data, email). It takes a specialized set of tools to obtain value from these types of data.

In conjunction with Oracle Exadata Database Machine and Oracle Exalytics Business Intelligence Machine, Oracle Big Data Appliance delivers everything an organization needs to acquire, organize, analyze, and make decisions that maximize the value of big data. Figure 2 shows three Oracle Big Data Appliances streaming data from sensors and social media, acquiring this data, organizing it, and leveraging Oracle Exadata for analysis.

Oracle’s end-to-end, engineered solution for Big Data.Figure 2. Oracle’s end-to-end, engineered solution for big data.

According to Willie Hardie, vice president of database product marketing at Oracle, this unique Oracle infrastructure addresses the full spectrum of big data requirements:

  1. Acquire. Because big data implies data streams of high velocity and variety, the acquisition tools must deliver low latency for capturing data and executing simple queries. They must also be able to handle very high transaction volumes, often in a distributed environment, and support flexible, dynamic data structures.
  2. Organize. The infrastructure must be able to process and manipulate data in the original storage location; support very high throughput; and handle a large variety of data formats, from unstructured to structured. Oracle automates each of these items in conjunction with the Hadoop Distributed File System (HDFS) and MapReduce programming paradigm.2
  3. Analyze. Aggregated results are loaded into a relational database management system, often on an Oracle Exadata Database Machine, so that they can be analyzed alongside traditional enterprise data stored in a data warehouse. The analytic infrastructure must support statistical analysis and data mining on a wide variety of datatypes and be able to scale to accommodate immense amounts of data.
  4. Decide. The final step is to automate decisions based on analytical models. Oracle Exalytics, Oracle Business Intelligence, and Oracle Analytic Applications automate querying, reporting, visualization, and presentation of the data.

As the linchpin of these new environments, Oracle big data Appliance combines optimized hardware with specialized software solutions to deliver a complete, easy-to-deploy solution for acquiring, organizing, and loading big data into Oracle Database 11g. It combines unique technology from Oracle with open source  Hadoop software to create a seamless solution for acquiring, organizing, and analyzing data for better decision-making, as shown in Figure 3.

Oracle addresses the full spectrum of big data requirements. Figure 3. Oracle addresses the full spectrum of big data requirements.

For CIOs who have spent years developing data warehouses and deploying business intelligence applications, the appeal of these engineered systems can be summed up in a single word: simplicity. With rapid deployment and very little effort, these integrated environments have demonstrated their ability to reduce IT costs through consolidation, improve performance of analytic applications, and facilitate better business decisions in real time.

“Together, Oracle Big Data Appliance, Oracle Exadata, and Oracle Exalytics provide the most comprehensive platform for addressing all the requirements for big data analytics,” says Hardie. “Because these solutions are preintegrated and preoptimized, it is easy to jump-start your big data projects.”

For CIOs who are trying to put these new analytic concepts in perspective and gain a rapid payback on their investments in big data platforms, Oracle’s integrated approach will accelerate time to market, reduce risk, and help them achieve a stronger competitive position.

1 “Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO” (September 2011).
2 Hadoop is an open-source project administered by the Apache Software Foundation.