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Why the Cloud? Processing, Exploitation and Dissemination
So why is the intelligence community so interested in cloud computing? Three letters: PED (Processing, Exploitation, Dissemination). Take these two real life examples from the publishing industry. Jim Staten of…
World Summit of Cloud Computing: “Enterprise Cloud Computing” work group
To leverage attendees of the World Summit of Cloud Computing, a kick-off meeting of the “Enterprise Cloud Computing” work group will be held near Tel Aviv, Israel on December 3,…
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In his post “Missing in the Cloud: package management“, Dave Rosenberg highlights a critical issue in the adoption of cloud computing by government agencies. “I dare say that a standard…
PlugIntoTheCloud.com
Information Week has just launched PlugIntoTheCloud.com as their cloud computing destination. In his Non Linear Thinking blog, Bill Martin calls it a movement aimed at “providing a source and forum…
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From Gartner “Why a little cloud hype might be useful“: “It’s too simplistic to say cloud hype is bad . If we are technically expert is might irritate us with…
Stop the FUD (Fear, Uncertainty and Doubt) !!
Dan Morrill! Count me in !! In his excellent article, “Cloud Computing is Scary – But the FUD Has to Stop“, Dan makes some excellent points: It is time to…
IBM, Microsoft and Google
On October 6th, IBM launched their cloud services initiative. This is a: “[C]ompany-wide initiative that extends its traditional software delivery model toward a mix of on-premise and cloud computing applications…
Government in the Cloud
Back in mid-September, there was quite a thread in the Google Cloud Computing Group on the use of cloud computing by the federal government. Some of the interesting comments were:…
CloudCamp Partners With SOA-R !!
I’m proud to announce that the final SOA-R Cloud Computing Education Event will be held in collaboration with CloudCamp. Now dubbed CloudCamp:Federal, the event will be held as an “unconference” to help…
Federal Cloud Computing Wiki
With the fast growing interest in cloud computing, the Federal Government community has established a Federal Cloud Computing Wiki. This wiki is managed by Dr. Brand Niemann, Senior Enterprise Architect…
- Using highly automated and economic cloud infrastructure to deliver proactive and predictive monitoring and threat interception in cybersecurity;
- Leveraging cloud computing device independence to enable real-time social media analytics that coordinate delivery of context driven information and commercial offers across multiple marketing channels;
- Establishing connectivity across over 6.4 billion sensors so that analytics and cognitive computing programs can provide actionable insight from real-time and historic data; and
- Hybrid Cloud data architectures that use cognitive computing capabilities to maintain content traceability and lifecycle management to enable the auditable management of licenses, terms of use, and changes to third-party data.
Cognitive systems understand by interaction, reason by generating recommendations and hypotheses, and learn from human experts and data. Since they never stop learning they also never stop providing business value. With this blending of cloud infrastructure and cognitive applications, the impossible can suddenly become easy!
- Develop a cognitive strategy by deciding which of your products, services, processes and operations should be infused with cognition. Your strategy should include identifying your organizations data needs and picking the experts to train the cognitive system.
- Collect and curate the data that is most useful to driving your business model. This step will help in creating an organizational foundation of data and analytics.
- Use cloud services that are designed specifically for your industry vertical. Such services will incorporate the application programming interface (API) building blocks necessary to power your future cognitive products and services.
- Acquire hybrid cloud service broker expertise and develop a hybrid infrastructure transition plan that combines your current IT systems with private and/or public clouds. This combination will serve as a backbone of your cognitive business.
- Establish and build-in a data-centric security model from the start. This focus will give you the ability to secure every transaction, piece of data and interaction as cognitive systems make their way into the Internet-of-Things (IoT). Secure systems ensure trust in the entire system and ultimately, the organization’s reputation.
Industry
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Cloud Computing
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Cognitive Computing
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Cyber security
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Highly automated and economical infrastructure platforms that encompass the implementation and enforcement of “brutal standardization” – IaaS
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Enables the advancement of operational cyber security is from threat detection via signature-based identification to proactive and predictive monitoring and threat interception powered by analyzing user behavior
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Marketing
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Ubiquitous access to compute, storage and networking services independent of device
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Delivery of social media analytics that promise the coordinated delivery of context driven information and commercial offers across multiple channels to targeted individuals
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Process Industries
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Connectivity to over 6.4 billion sensors collecting and storing real-time data
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Analytics and cognitive computing programs that provide actionable insight from real-time and historic data
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All industry verticals
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Transform from the delivery of labor hours and physical goods to the delivery of information and services (Uber, Air B&B, Travelocity, etc)
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In the case of IBM Watson, the consumption and processing of billions of API calls per month across 80,000 programs developed by 500 companies in 36 countries (https://www.tomsitpro.com/articles/ibm-cloud-hyrid-storage-watson,1-3158.html)
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Consulting / Analytics
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Voice-driven command and control
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Applications that understand natural language and generate personalized insights that learn with every user interaction
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Consulting
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Economic and secure collection, transport, processing and storage of massive amounts of structured and unstructured data
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Ability to pull non-obvious insights out of massive amounts of multi-structured data through the discovery of patterns and relationships. This enables the economic use of dark data, described as “information assets that organizations collect, process and store in the course of their regular business activity, but generally fail to use for other purposes.”
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Distribution / Publishing / Content Management
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Hybrid Cloud data architectures that ensures that, when multiple data zones are in use, compute power is moved to the data, rather than compute workloads being moved in a way that could violate institutional policies, regulatory guidelines or governmental laws around data location.
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Data Fabric technology that maintains the traceability and lifecycle of content enabling the auditable management of licenses, terms of use, and changes to third-party data.
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Education / Research
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Global SaaS/PaaS business models and platforms
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Creation of Analytic Fabrics that combine and orchestrate different analytics engines that deliver an ability to create composite or cognitive insights across first-part and third-party data. This can also be used to combine natural language queries with structured data analytics.
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Healthcare / Public Safety
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Global SaaS/PaaS business models and platform
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Cognitive Graphs that can represent entities, relationships, and attributes in a probabilistic way, not just a deterministic way, so that users can do inferencing and generate hypothesis. This also delivers an ability to normalize many different data types as well as learn from data over time. With this capability, when something changes somewhere in the graph that may affect something elsewhere in the graph, every specific change is recognized at every point it touches.
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( This post was brought to you by IBM Global Technology Services. For more content like this, visit Point B and Beyond.)
( Thank you. If you enjoyed this article, get free updates by email or RSS – © Copyright Kevin L. Jackson 2015)
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