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Www.statestreet.com/wps/wcm/connect/f9b92f00401ff011905ebb02fd03b00e/Insurance+Data+Release,+June+26,+2013.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=f9b92f00401ff011905ebb02fd03b00e. How Semantic Web Technology Makes Data Integration Reporting Easier. Semantic Web technology makes data integration more flexible, reducing a lot of the work previously required for new integration and for revamping databases, according to Lee Feigenbaum, vice president of marketing and technology for Cambridge Semantics. He also co-chairs the W3C SPARQL working group, which is an RDF query language and considered a key technology for the Semantic Web. He also explains who some of the main players are in this space, and why you should buy rather than build. In part one of this interview, he explained the differences in semantic technologies and what’s changed in the past year.

Lawson: How does it change what you're able to do when you talk about BI and master data management? Feigenbaum: There are a couple of key things that are going on here. As a precursor, generally speaking, this is an overlay technology and that’s super important to adoption. The benefit of doing that is it gives you a very flexible data integration layer. Feigenbaum: Oh, absolutely. Interview with David Saul of State Street Corp on semantic technology www.revelytix.com. SearchCIO TechTarget recently published a two part conversation with David Saul, Chief Scientist at State Street Corp. In this interview David hits on all the key enterprise objectives: 1 - Automating to reduce the cost of data retrieval 2 - Improved corporate agility in introducing new products or services 3 - Compliance with Regulatory and customer requirements What is different is how David is thinking in terms of meeting these objectives.

We might argue one reference, i.e., ...the need for "data scientists" to help bridge business and IT to create semantic layouts. Creating ontologies and queries for assembing new data sets does not require a data scientist. All in, if you have a smidgen of interest in streamlining enterprise data management, you don't want to miss this interview. Part 1 Part 2 "...Think of it more as an overlay technology on the existing amounts of both structured and unstructured data that we have stored in files and databases. State Street's Chief Scientist on How to Tame Big Data Using Semantics. Semantic databases are the next frontier in managing big data, says State Street's David Saul. Financial institutions are accumulating data at a rapid pace. Between massive amounts of internal information and an ever-growing pool of unstructured data to deal with, banks' data management and storage capabilities are being stretched thin.

But relief may come in the form of semantic databases, which could be the next evolution in how banks manage big data, says David Saul, Chief Scientist for Boston-based State Street Corp. The semantic data model associates a meaning to each piece of data to allow for better evaluation and analysis, Saul notes, adding that given their ability to analyze relationships, semantic databases are particularly well-suited for the financial services industry. "Our most important asset is the data we own and the data we act as a custodian for," he says. Using a semantic database, each piece of data has a meaning associated with it, says Saul. More Insights. How to Be Ready for Big Data. CIO — Big Data is all the rage these days, and more than a few organizations are at least wondering what sort of business intelligence they could derive from all the information at their disposal.

But while awareness of Big Data is growing, only a few organizations—like Google or Facebook-are really in position to capitalize on it now. However, the time is coming and organizations that expect to leverage Big Data will not only have to understand the intricacies of foundational technologies like Apache Hadoop, they'll need the infrastructure to help them make sense of the data and secure it. In the next three to five years, we will see a widening gap between companies that understand and exploit Big Data and companies that are aware of it but don't know what to do about it, says Kalyan Viswanathan, global head of information management with Tata Consultancy Services' (TCS) global consulting group. "Today, most companies are aware of Big Data," he says.

"There's a lot written about it. Enquête : Le Big Data n'explose pas dans l'assurance. Le Big Data a encore beaucoup de mal à faire partie des priorités de l’assurance. D’après une enquête Optimind winter, 31% des sociétés ont des travaux en cours. Pour l’ensemble des acteurs de l’assurance, pourtant concernés en priorité par le traitement de données de masse et leur caractère prédictif, le Big Data* reste encore un axe stratégique lointain. Une enquête d’Optimind winter menée auprès de 48 sociétés distinctes montre que, si le Big Data commence à être plutôt bien connu et identifié, les opérateurs ne l’ont pas intégré dans leur l’activité et manquent de repères. Selon ce sondage, jusqu’à 31% des répondants ne savent pas comment se situer sur le sujet par rapport aux concurrents.

“Cela semble signifier soit qu’ils n’ont pas démarré, soit qu’ils ne font pas de veille concurrentielle, soit encore que l’absence de mesure claire sur l’adoption du Big Data ne leur permet pas de se positionner aisément“, interprète la société d’actuariat et de gestion des risques. Making Advanced Analytics Work for You. Artwork: Tamar Cohen, The Big Quick, 2010, silk screen collage on vintage book pages, 40" x 50" Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data. They also see that big data is attracting serious investment from technology leaders such as IBM and Hewlett-Packard. Meanwhile, the tide of private-equity and venture-capital investments in big data continues to swell.

The trend is generating plenty of hype, but we believe that senior leaders are right to pay attention. Even so, our experience reveals that most companies are unsure how to proceed. Many CEOs, too, recall their experiences with customer relationship management in the mid-1990s, when new CRM software products often prompted great enthusiasm. Given this history, we empathize with executives who are cautious about big data. 1. Making data analytics work: Three key challenges. By now, most companies recognize that they have opportunities to use data and analytics to raise productivity, improve decision making, and gain competitive advantage. “Analytics will define the difference between the losers and winners going forward,” says Tim McGuire, a McKinsey director. Video Building a data-driven organization Matt Ariker, COO, McKinsey Consumer Marketing Analytics Center Play video But actually mapping out an analytics plan is complicated.

You have to set a strategy; draw a detailed road map for investing in assets such as technology, tools, and data sets; and tackle the intrinsic challenges of securing commitment, reinventing processes, and changing organizational behavior. Transforming data Matthias Roggendorf, McKinsey senior expert “Big data: What’s your plan?” Big data: What’s your plan? The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt. The tally of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition.

The promised land of new data-driven businesses, greater transparency into how operations actually work, better predictions, and faster testing is alluring indeed. But that doesn’t make it any easier to get from here to there. The required investment, measured both in money and management commitment, can be large. CIOs stress the need to remake data architectures and applications totally. The answer, simply put, is to develop a plan. There’s a compelling parallel here with the management history around strategic planning. What’s in a plan? Any successful plan will focus on three core elements. Data Tools Exhibit. Four innovative ways Asian banks can create actionable insights from customer data. Service is among the top attractions for consumers to Asian retail banks, ranking above products and convenience—but overall satisfaction is not high. By differentiating against competitors with targeted customer service, banks stand to gain more market share, both through expanding their customer base and deepening relationships with existing customers.

Yet with a finite amount of resources to deploy, banks need to find ways to align service and sales with customer needs and priorities. The data advantage Many customers value branch convenience as an aspect of service, for example, but only a limited number of banks will be in a position to capture that opportunity. Banks can, however, take advantage of many other opportunities to capture more value through better and more focused customer-portfolio management. Leveraging granular customer data can help banks do the following: Practical insights on four levels Banks can use their data to create actionable insights in four areas. 1. Exhibit. Big data in the banking sector. Regulators are demanding greater transparency, customers want a more relevant and personalized experience, and CEOs are looking for sustainable growth opportunities.

A common thread in all of these issues is data. Big data. What is big data? Forrester puts it succinctly in saying “big data encompasses techniques and technologies that make capturing value from data at an extreme scale economical”. Big data is all about liberating information from variable sources and formats, which is large in volume and broad in variety, in order to become more efficient and gain valuable insight to maximize opportunities. Technology silos and ever-expanding amounts of data make effective data management a massive challenge.

Deriving value from big data Banks need to move from analysis to action. Big data initiatives are likely to grow within the banking sector, as they seek better, more cost-effective ways to derive meaning from big data. What are your thoughts on big data in the banking sector?