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Posts tagged ‘Data Economy’

11
Jun

NSA PRISM – The Mother of all Big Data Projects


Prism9As a data engineer and scientist, I have been following the NSA PRISM raw intelligence mining program with great interest.  The engineering complexity, breadth and scale is simply amazing compared to say credit card analytics (Fair Issac) or marketing analytics firms like Acxiom.

Some background… PRISM – “Planning Tool for Resource Integration, Synchronization, and Management” – is a top-secret data-mining “connect-the-dots” program aimed at terrorism detection and other pattern extraction authorized by federal judges working under the Foreign Intelligence Surveillance Act (FISA).  PRISM allows the U.S. intelligence community to look for patterns across multiple gateways across a wide range of digital data sources.

PRISM is unstructured big data aggregation framework — audio and video chats, phone call records, photographs, e-mails, documents,  financial transactions and transfers, internet searches, Facebook Posts, smartphone logs and connection logs – and relevant analytics that enable analysts to extract patterns. Save and analyze all of the digital breadcrumbs people don’t even know they are creating.

The whole NSA program raises an interesting debate about “Sed quis custodiet ipsos custodes.” (“But who will watch the watchers.”) Read more »

18
Mar

Data-as-a-Service (DaaS)


datamartproliferation

If the analytics team wrestles with getting access to data, how timely are the insights?

To address the question…Global CIO are shifting their strategy — “need to build data-as-a-service offering for my data” to enable the analytics users in the organization.   The more advanced CIOs are asking – “how should I build data science capabilities as a shared foundation service?”

The CIO challenge is not trivial. Successful organizations today operate within application and data eco-systems which extend across front-to-back functions (sales & marketing all the way to fulfillment and service) and well beyond their own boundaries. They must connect digitally to their suppliers, partners, distributors, resellers, regulators and customers. Each of these have their “data fabrics” and applications which were never designed to connect, so with all the data-as-a-service and big data rhetoric, the application development community being asked to “work magic” in bringing them together.

Underutilization and the complexity of managing growing data sprawl is not new. But the urgency to address this is increasing dramatically during the last several years. Data-as-a-Service (DaaS) is seen as a big opportunity in  improving IT efficiency and performance through centralization of resources. DaaS strategies have increased dramatically  in the last few years with the maturation of technologies such as data virtualization, data integration, MDM,  SOA, BPM  and Platform-as-a-service.

The questions which are accelerating the Data-as-a-Service (DaaS) trend:  How to deliver the right data to the right place at the right time? How to “virtualize” the data often trapped inside applications?  How to support changing business requirements (analytics, reporting, and performance management) in spite of ever changing data volumes and complexity.

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19
Jan

Multi-channel to Omni-channel Retail Analytics: A Big Data Use Case


MULTI-CHANNEL is simply having multiple channels through which you buy, market, sell, and fulfill.

CROSS-CHANNEL has the ability to see all of a customer’s information across all channels enables more personalized offers based on their brand relationship.

OMNICHANNEL weaves all the touchpoints of the products and services of the brand into a  seamless fabric  of all phases of the  customer’s brand experience.

Which one are you?

Let’s face it – The old uni-channel retail model is dying in some cases and changing in others.  E-commerce is driving nearly all retail growth. Digital customers want simple, consistent, and relevant experiences across all channels, touchpoints, mobile screens, smart watches and other devices.

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