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Doug Balog, IBM VP and Business Level Executive for Storage, presented Smart Archiving. Citing research by Jon Toigo, Doug indicated that 40 percent of data on disk should be archived. Sadly, a vast majority of companies continue to use their backups as archives. There is a better way to do archives, to address the needs of four use cases:
The IBM Information Archive for email, files and eDiscovery offers full text indexing. A well-deployed archive strategy can save up to 60 percent in backup costs, and reduce backup times by 80 percent. IBM offers advanced analytics and visualization for archive data.
An analysis of a global insurance company found that they kept, on average, 120 copies of every email sent. This was the combination of an average of 12 copies of the email, multipled by 10 backups of the email repository.
Banjercito, a bank in Mexico, has a 10-year retention requirement from government regulations.
The new LTFS Library Edition allows Library-based access to files stored on tape cartridges. The new TS3500 Library Connector means that a single system of connected tape libraries can hold up to 2.7 Exabytes (EB) of data.
Archive Industry Perspectives
Steve Duplessie from Enterprise Strategy Group [ESG] gave his views on the challenges of volume, access and cost. His definition for archive: the long term retention of information on a separate environment for compliance, eDiscovery and business reference purposes. Steve advocates a purpose-built solutiion for archive. There are three major challenges for implementing an archive solution:
Getting Participation -- Steve feels that key stakeholders have inappropriate expectations of what archive is, or can be.
Define Tasks -- Steve argues that archive is very much a process-oriented approach, and tasks must fit business process and procedures
Prepare for Future Content Types -- the frequent change of standard and proprietary data types poses a real challenge for long term retention of data
For example, the Financial Industry Regulatory Authority [FINRA] oversee 4,000 brokerage firms, and 600,000 broker/dealers. They have mandated the storing of digital data related to stock trades, and this can include text messages, voice messages, and emails. They continue to expand this definition, so soon this could include tweets on Twitter, for example.
Steve feels there are four key requirements for archive:
Support for email, such as an email application plug-in
Off-line access to archived data
Support for mobile devices, such as smartphones
Basic search capabilities
Companies are starting to take archive seriously. About 35 percent of firms surveyed have adopted archive, and another 36 percent plan to in the next 12-24 months. Enterprise archive has grown over 200 percent from 2007 to 2009. Steve agrees that not everything needs to be stored on disk. Retention periods greater than six years dictates the need for tape.
Current systems may not meet today's requirements. Data loss and downtime costs have skyrocketed. Data Protection and Retention projects can represent a gold mine of savings, new capabilities can greatly lower costs, allowing companies to shift resources over to revenue generation.
Big Data, New Physics and Geospatial Super-Food
I would vote this the best session of the day! For all those confused on what the heck "Big Data" means, Jeff has the best explanation. Jeff Jonas is an IBM Distinguished Engineer and the Chief Scientist of Entity Analytics. He had just finished his 17th marathon on Saturday, and his fingers were bandaged.
Jeff had founded the Systems Research and Design (SR&D) company, known for creating NORA (non-obvious relationship awareness) used by Las Vegas casinos to identify fraud. SR&D was acquired by IBM back in 2005. Jeff is focused on sensemaking of streams. He feels many companies are suffering from "Enterprise Amnesia".
"The data must find the data .. and the relevance must find the user."
-- Jeff Jonas
Jeff's metaphor to Big Data is a jigsaw puzzle without the picture on the outside of the box. To demonstrate his point, he presented a pile of jigsaw puzzle pieces and asked four teenagers to put the puzzle together without the advantage of the picture on the box. What he had not told them was that he mixed four different puzzles together, removing out 10 to 20 percent of the pieces from each puzzle. He also added some duplicate pieces from a second identical puzzle, and just to make things fun, included a dozen pieces from a sixth puzzle just to mess with their heads. Within a few hours, the kids had managed to figure out that there were four puzzles, that there were duplicate pieces, and that there were some pieces that did not fit any of the four puzzles.
"You can't squeeze knowledge from a pixel."
-- Jeff Jonas
This approach favors false negatives. New observations reverse out old conceptions. As the picture emerges, this provides added focus on new information. More data can provide better predictions. "Bad" data, including misspelled words and mis-coded categories, was often discarded or corrected on the basis of "Garbage-In, Garbage Out", but can now be useful in a Big Data perspective.
Take for example the 600 billion recordings of the "location data" captured on cell phones every day. With regular triangulation of cell phone towers, the information can pinpoint you within 60 meters, add GPS and this improved to within 20 meters, and add Wi-Fi is further improved to 10 meters. While this data is "de-identified" so as not to identify individual users, the process of re-identification is relatively trivial. Jeff's system is able to predict a person will be next Thursday at 5:35pm with 87 percent accuracy.
Thus, Big Data represents an asset, accumulation of context. Real-time analytics can be a competitive advantage. These streams of data will need persistent storage and massive I/O capabilities. In one example, Jeff processed 4,200 separate sources of information and was able to identify "dead votes". These are votes cast by people that died in years prior, indicating voter fraud.
Jeff's latest project, codenamed G2, will tackle not just people, but everything from proteins to asteroids.
Normally, the worst time slot is the hour after lunch, but these presentations kept people's attention.