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Cognitive Business: When Cloud and Cognitive Computing Merge

July 21, 2016 / G C Network /

Cloud computing has taken over the business world! With almost maniacal focus, single proprietors and Board Directors of the world’s largest conglomerates see this new model as a “must do”. This rapid shift is, in fact, accelerating. As Jeff Bertolucci observes in “The Shift to Cloud Services Is Happening Faster Than Expected”:
“According to the sixth annual Uptime Institute Data Center Industry Survey, which examines the big-picture trends shaping IT infrastructure delivery and strategy, the move to cloud services is accelerating. The Uptime Institute’s February 2016 poll of more than 1,000 data center and IT professionals predicts that an even faster shift to the cloud will occur over the next four years, reports ZDNet.” 
Another maybe even more important trend, that is actually being driven by cloud computing, is the rapid expansion of cognitive computing. In this arena, IBM’s Watson, famously known for defeating Jeopardy gameshow champions Ken Jennings and Brad Rutter, has quickly established itself as a commercial cognitive computing powerhouse. Contemporary reports of the Jeopardy contest from the New York Times cited this victory as IBM’s “…proof that the company has taken a big step toward a world in which intelligent machines will understand and respond to humans, and perhaps inevitably, replace some of them”. Although we are not yet at the human replacement stage, the merger of cloud and cognitive computing is rocking the business status quo.
Coined as “Cognitive Business” this trend can deliver quantum level improvement to just about any industry vertical. Examples include:

  • 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!

If your business wants to take advantage of this important transition, the time to take action is now. Your initial steps should include:

  1. 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.
  2. 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.
  3. 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.
  4. 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.  
  5. 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
Cloud Computing
Cognitive Computing
Cyber security
Highly automated and economical infrastructure platforms that encompass the implementation and enforcement of “brutal standardization” – IaaS
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
Marketing
Ubiquitous access to compute, storage and networking services independent of device
Delivery of social media analytics that promise the coordinated delivery of context driven information and commercial offers across multiple channels to targeted individuals
Process Industries
Connectivity to over 6.4 billion sensors collecting and storing real-time data
Analytics and cognitive computing programs that provide actionable insight from real-time and historic data
All industry verticals
Transform from the delivery of labor hours and physical goods to the delivery of information and services (Uber, Air B&B, Travelocity, etc)
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)
Consulting / Analytics
Voice-driven command and control
Applications that understand natural language and generate personalized insights that learn with every user interaction
Consulting
Economic and secure collection, transport, processing and storage of massive amounts of structured and unstructured data
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.”
Distribution / Publishing / Content Management
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.
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.
Education / Research
Global SaaS/PaaS business models and platforms
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.
Healthcare / Public Safety
Global SaaS/PaaS business models and platform
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.
Figure 2– Cloud and Cognitive Computing Merge to drive business value in multiple industry verticals

( This post was brought to you by IBM Global Technology Services. For more content like this, visit Point B and Beyond.)

Cloud Musings

( Thank you. If you enjoyed this article, get free updates by email or RSS – © Copyright Kevin L. Jackson 2015)

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