“America has lost its creative edge,” Harthorne says. “How do we restore it? It’s created by startups. All recent net job growth has been created by companies under five years of age.”

Harthorne’s plan to ignite a worldwide rebirth of startups comes in three stages, modeled on the template of MassChallenge.

Gary Kennedy, a wise mentor of mine once said that the problem with business is that it constantly looks for the one thing that is the problem so it can apply one fix. He described most business problems as an arch bridge with multiple arches. Each is damaged a different percentage but to cross the bridge they all need repairing. Look for places where one solution may fix multiple problems but understand there is not one thing. This is a good idea. It is one good idea and to fix America’s youth unemployment rate will require lots of little fixes not one big idea.

Source: Mashable

Even as organizations are trying to define the role of those tasked with analyzing and managing the new phenomenon of big data, people capable of that job are already projected to be in short supply.

The move from a network-centric to a data-rich environment requires a different skill set, John Marshall, CTO of the Directorate of Intelligence J2 with the Joint Chiefs of Staff, said March 6 during a forum on big data.

There is a need to aggregate data cross structured and unstructured data repositories. The biggest threat to productivity is the lack of aggregation across different lines of businesses, said Marshall, who moderated a panel on the emerging role of the chief data officer and data scientist at the Government Big Data Forum. The forum was held in Washington, D.C., by systems integrator Carahsoft.

I could not agree with this more. SAND’s strategy is to identify the non-productive work people who analyze data are forced to perform and focus on freeing them up to focus on high value work. We can’t create more data scientists, analysts and people; we can and should do the job of software and make them more productive.

Source: Government Computer News

The ads and offers that you receive today via the Web and mobile are mostly blind to how you’re actually spending your money in the physical world. As these databases are more intelligently connected, the offers you receive will become significantly more relevant and compelling, based on where you spend your actual time and money. Note to payment network innovators: it’s critical that these programs are introduced in a way that protects consumer privacy and retains consumer trust.

I agree with Reid Hoffman, however while this vision of integration is compelling, there are many facets that need addressing. SAND delivers the underlying technology to process and manage all this data, we also have the skills to understand this data and the knowledge to turn this into actionable insight. Therein lies the challenge for many companies to get the value Reid describes. There are a dearth of skills in the market, and while companies like SAND can help turn this data into information, many companies simply don’t have the skills to do so. Like the move to online shopping, adoption will occur when all the elements are in place. Getting the data Reid describes is one thing, delivering the conversation to the consumer so they engage rather than opt out requires detailed knowledge of consumer behavior. Once they opt out, they rarely opt back in.

Source: Forbes

While Hadoop is an open-source platform, the process of developing and deploying it is far from free. Hadoop’s sheer complexity makes it necessary to build and maintain a significant infrastructure. Hadoop’s widespread and growing resonance in the marketplace is based on the genuine advantages the platform provides: the market’s highest performance and scalability at much-reduced costs compared with other approaches. Once this Hadoop infrastructure is built, it enables the extraction of far more data and information than can be obtained using Oracle infrastructure or other competing solutions.

Or, frankly, better solutions. With no caveat.

The Hadoop project provides a framework that excels at capturing semi-structured and unstructured data to help enterprises get a handle on Big Data. But simply storing data in Hadoop is not enough: companies need fast and agile data analytics in order to tap the full potential of their data. The best approach is to take data from the creation layer (where it is generated) and move it to an actionable layer (an analytic performance database, such as SAND). Using SAND’s bidirectional capabilities, users can quickly analyze data and easily drill down into Hadoop for additional data. Whereas traditional platforms cause bottlenecks by requiring data to be pre-formatted prior to analysis, SAND avoids this limitation through its Infinite Data Optimization technology, ensuring that users can instantly and easily run any query. Faster data analysis empowers the enterprise to get more value out of Big Data, leading to valuable business insights and faster ROI.

Get the SAND Hadoop white paper, and get your company real Big Data Big Insight results now.

This white paper, Leveraging the Value of Big Data for Life Sciences Companies, discusses how, using SAND Life Sciences, companies more efficiently capture, store, and analyze all of their data. Improved data analysis improves R&D cycles and identifies early indicators of success or failure in a product, enabling the company to concentrate efforts on its most promising projects. By empowering scientists through better information, SAND helps Life Sciences organizations to develop new therapies and treatments more efficiently, and bring those products to market faster, potentially saving patients’ lives.

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As technology accelerates, companies across all industries are generating more and more data—a phenomenon known as Big Data. Enterprises are racing to capture, store, and analyze all of this data in order to mine it for insights and business intelligence. With more customer data available than ever before, companies today have an unprecedented opportunity to better understand their customers’ needs and maximize profits. Advanced analytics solutions are now making it possible for companies to get a handle on Big Data and mine it for intelligence in order to offer customers a truly “Next Best Offer.”

With the wealth of information available within Big Data, today’s companies have the unprecedented opportunity to better understand their customers and deliver a Next Best Offer. The challenge is to effectively capture, store, and analyze these huge stores of data, which routinely overwhelm traditional data analysis tools, rendering them useless. To generate NBOs, a company needs a scalable solution capable of searching across unlimited categories at once. SAND provides the world’s most advanced analytic database platform to overcome these limitations, enabling organizations like Thomas Cook to maximize the value of their data and gain new insights into their clientele. With improved data analysis, companies can generate the best possible Next Best Offers for customers, delivered precisely when and where they will be the most effective. NBOs promise to revolutionize traditional sales models, helping customers to find exactly what they want when they need it. NBO is still a relatively new concept, but the companies that embrace data analysis in order to better serve their customers through NBOs will gain competitive advantage in the marketplace, increasing sales and making customers happier.

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• Microsoft mainly wants to protect Windows and Office. Microsoft is a platform company, and its main goal is to protect its highly lucrative Windows and Office monopolies, while establishing other platforms that will be hard for customers to break away from later. New functionality is “drip fed” to users of those core platforms, but new products exist to protect the core. He advised extreme caution before moving to Office 365, and said not to slip into an “all-Microsoft” mentality.

• Oracle products don’t really work well together. Oracle’s sales force is extremely aggressive about pushing a suite of products, but has much fewer integration points than SAP. In fact, integration is usually left entirely up to the customer. Oracle is also very reluctant to talk about product roadmaps for fear that future products will cannibalize existing ones. The company makes more than 90% of its profits through maintenance fees, and will do whatever it takes to keep those fees flowing in. Gaughan also expressed some surprise that so many customers keep working with Oracle despite reporting that Oracle is “the most difficult vendor to deal with.”

• IBM wants to take over your IT strategy. IBM bills itself as a thought leader, but its real business is selling consulting services. To thrive, IBM account managers try to take control of a company’s IT strategy so they can keep pushing new products. Gaughan recommends taking a collaborative or partner approach.

• SAP confuses customers with pricing. A lot of SAP customers ask Gartner for help figuring out SAP’s pricing and licensing, as SAP has unusual terms for billing data going into and out of systems. Gaughan also said that a big technology transition that was driving SAP revenue for the last few years — moving existing customers from the old R/3 system to the newer Business Suite — is almost done, which means SAP will have to be more aggressive with maintenance fees. He recommended locking in maintenance prices now.

I couldn’t have said it better myself – thank you Gartner.

Source: ITNews via BusinessInsider

This white paper, Unleashing Big Data value using SAND Infinite Optimization, discusses how SAND employs a unique approach called Infinite Optimization that avoids traditional limitations. SAND delivers a high-performance analytical database that is automatically optimized for answering any question, even unusual questions that have never before been asked. SAND Infinite Optimization technology means organizations no longer need to model or tune their data before running queries; instead, all data is fully and automatically indexed so that is ready for any and all queries that users can imagine.

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The result is that Oracle’s product line is morbidly obese. Big Data’s Mr. Creosote. For those that remember the Meaning of Life — and I don’t mean “42″, I mean the Monty Python film of the same name — you will remember Mr Creosote eating just one more wafer-thin mint too many… before exploding into a million messy pieces. Is the acquisition of Endeca Oracle’s one wafer-thin mint too many?

Already Exadata, with the Oracle database at its core (that was never designed or intended for how it is currently positioned), has seen more and more acquisitions forced into its box. Oracle is designed for OLTP, not Big Data analytics, yet Larry insists on force-feeding it more and more wafer-thin acquisitions. Endeca forces a column-oriented database into Oracle. First, I’m glad to see Oracle finally figured out column orientation is a good idea for Analytics rather than the hybrid columnar compression that was first grafted onto Oracle. Well done. We knew you’d get there someday! Second, it’s crammed in there with TimesTen to improve memory performance. We knew you’d need that too. Just how clown-car crowded is it with everything from Hadoop and Berkley DB also tied to the box?

Extracting and augmenting structure onto unstructured data and merging it with structured data is the future of Big Data analytics. A video feed of someone walking into a store has multiple versions of the truth depending on the eye of the beholder. If I am running marketing, I may care which other shopping bags someone brings in, where they browse in the store and what they ultimately buy. Merging this data with their shopping basket data can tell me much more than simply looking in their basket. If I am head of security, the same person entering the store with bags and leaving without them and doing so with regularity, when given structure and merged with inventory data, can tell me who is potentially a security risk. There is immense untapped business value in extracting structure from unstructured data and merging it with structured data.

But it requires a database designed and built to handle it, not a monster overfed on an endless supply of wafer-thin acquisitions. Personally I am waiting for the bang.