Read Up on the Overall Big Data Solution
By Jean-Pierre Dijcks-Oracle on Oct 25, 2011
On top of the NoSQL Database release I wanted to share the new paper on big data with all. It gives you an overview of the end-to-end solution as presented at Openworld and places it in context of the importance of big data for our customers.
This is a a quick look at the Executive Summary and the Introduction (or click here for the paper):
Today the term big data draws a lot of attention, but behind the hype there's a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of non-traditional, less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Decreases in the cost of both storage and compute power have made it feasible to collect this data - which would have been thrown away only a few years ago. As a result, more and more companies are looking to include non-traditional yet potentially very valuable data with their traditional enterprise data in their business intelligence analysis.
To derive real business value from big data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data types and analyze them alongside your existing data to find new insights and capitalize on hidden relationships.
With the recent introduction of Oracle Big Data Appliance, Oracle is the first vendor to offer a complete and integrated solution to address the full spectrum of enterprise big data requirements. Oracle's big data strategy is centered on the idea that you can evolve your current enterprise data architecture to incorporate big data and deliver business value. By evolving your current enterprise architecture, you can leverage the proven reliability, flexibility and performance of your Oracle systems to address your big data requirements.
Defining Big Data
Big data typically refers to the following types of data:
- Traditional enterprise data - includes customer information from CRM systems, transactional ERP data, web store transactions, general ledger data.
- Machine-generated /sensor data - includes Call Detail Records ("CDR"), weblogs, smart meters, manufacturing sensors, equipment logs (often referred to as digital exhaust), trading systems data.
- Social data - includes customer feedback streams, micro-blogging sites like Twitter, social media platforms like Facebook
The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it's often the most visible parameter, volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data:
- Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes. Smart meters and heavy industrial equipment like oil refineries and drilling rigs generate similar data volumes, compounding the problem.
- Velocity. Social media data streams - while not as massive as machine-generated data - produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day).
- Variety. Traditional data formats tend to be relatively well described and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information.
- Value. The economic value of different data varies significantly. Typically there is good information hidden amongst a larger body of non-traditional data; the challenge is identifying what is valuable and then transforming and extracting that data for analysis.
To make the most of big data, enterprises must evolve their IT infrastructures to handle the rapid rate of delivery of extreme volumes of data, with varying data types, which can then be integrated with an organization's other enterprise data to be analyzed.
The Importance of Big Data
When big data is distilled and analyzed in combination with traditional enterprise data, enterprises can develop a more thorough and insightful understanding of their business, which can lead to enhanced productivity, a stronger competitive position and greater innovation - all of which can have a significant impact on the bottom line.
For example, in the delivery of healthcare services, management of chronic or long-term conditions is expensive. Use of in-home monitoring devices to measure vital signs, and monitor progress is just one way that sensor data can be used to improve patient health and reduce both office visits and hospital admittance.
Manufacturing companies deploy sensors in their products to return a stream of telemetry. Sometimes this is used to deliver services like OnStar, that delivers communications, security and navigation services. Perhaps more importantly, this telemetry also reveals usage patterns, failure rates and other opportunities for product improvement that can reduce development and assembly costs.
The proliferation of smart phones and other GPS devices offers advertisers an opportunity to target consumers when they are in close proximity to a store, a coffee shop or a restaurant. This opens up new revenue for service providers and offers many businesses a chance to target new customers.
Retailers usually know who buys their products. Use of social media and web log files from their ecommerce sites can help them understand who didn't buy and why they chose not to, information not available to them today. This can enable much more effective micro customer segmentation and targeted marketing campaigns, as well as improve supply chain efficiencies.
Finally, social media sites like Facebook and LinkedIn simply wouldn't exist without big data. Their business model requires a personalized experience on the web, which can only be delivered by capturing and using all the available data about a user or member.
The full paper is linked here. Happy reading...