NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

Digitization of products and services is changing industry supply chains, markets and jobs. How business companies and the providers of IT services and systems respond is increasingly driven by what digital ecosystems and roles will you play? How do you plan and optimize your digital options? Your brand market strategy and operating performance are affected by digital channels. Existing competitors may be moving to new digital models, new entrants in products and services and the IT enablers of mobility, connected sensors, social, cloud and big data may be creating step change performance.

The explosion of data and the drive for a multiplicity of customer driven experience and choice is creating an increasingly price competitive digital footprint to be mindful of.
Can digital ecosystems be “designed” if many of the actors, online social behaviors and large cloud platforms and networks appear to drive their own agendas?

What characteristics are important to establishing a competitive product and service in a digitalized marketplace and industry?
How is the balance made between investing in your own digital footprint and partnering or consuming often Co-competing digital platform offerings?

This session will explore these themes
· Definitions of what are your digital ecosystems

· How to design digital workspaces in your digital ecosystem

· Managing change in your organization to align and exploit your digital ecosystem presence

· What are the liminality of these digital ecosystem

See Mark's forthcoming book – “Digital Ecosystems”. For more details see his blog http://www.markskilton.com for announcements

Cognitive computing is a way of processing data that is neither linear nor deterministic. It uses the ideas behind neuroscience and psychology to augment human reasoning with better pattern matching while determining the optimal information a person needs to make decisions. Cognitive computing is different than other forms of software. Instead of shepherding data through pre-determined pathways, it finds the previously unknown paths and patterns through the data. This is ultimately a more scalable model than relying on experts to synthesize data since there are too few experts of any sort available at any one time. Cognitive computing doesn’t try to fit data into an existing model; it looks at the data and figures out what the model is first.

This webinar provides an overview this new field of computing. It describes what it is, why it has value, and how it applies to the current business climate.

Large organizations manage many lines of business that can lead to difficulty in efficiently allocating resources. Governments often maintain extensive amounts of data but seldom leverage the value in the data to improve operational efficiency. Discover how the Office of the Utah State Auditor now leverages data resources across thousands of local governments, dozens of state agencies and universities to uncover fraud, waste, and the inefficient allocation of scarce resources.

Cloud based services are gaining traction in the healthcare community. The combination of reliability, cost effectiveness and security has prompted many healthcare organizations to move data to the cloud for storage and analysis. New cloud opportunities are arising in promoting collaboration among care givers and in analyzing large data sets (eg genomics). This talk will review the possibilities and challenges of moving healthcare data and analysis to the cloud.

Business intelligence tools were born to query and report. But now analysts and business users don't just want dashboards, they want to dive deep into ad hoc analyses, to explore dozens of hypotheses in minutes. The BI industry is responding by tacking on better visualization and calling it analysis.

But visualization and analysis are very different. If they weren't, why do most analysts prefer to query data with BI tools, then do their actual analysis in Excel (or statistical tools)? Or why do Tableau's help documents literally suggest you pull out a calculator if you'd like to run a correlation?

For many companies, this misunderstood distinction is the final barrier to reaching the promised land of data for all. We'll explore the distinction, as well as the growing divide between exploratory analysis tools and predictive analysis tools. We'll also talk about the reasons that cloud-based analysis tools will leave the rest further and further behind.

This is a continuation of our 2-part series on Big Data Visibility with Network Packet Brokers (NPBs).

Big data techniques and technologies can be powerful tools for scaling network monitoring and forensics. They can also facilitate new use cases for network data, potentially beyond the scope of Operations.

Gordon Beith, Director of Product Management at VSS Monitoring, will discuss practical considerations for migrating to a Big Data Visibility Architecture, including:
• Accommodating network volume, velocity and variety using sophisticated hardware preprocessing and APIs
• Metadata versus flow statistics versus full packet capture – considerations and use cases for each
• Open versus proprietary formats for storage
• Pros and cons of integrated capture/storage/analysis solutions versus separate capture/ storage solutions coupled with virtualized analysis probes
• Addressing retrieval in an “open” forensics model
• Leveraging a distributed computing framework for processing large-scale data stores

This is part 1 of our 2-part series on Big Data Visibility with Network Packet Brokers (NPBs).

Even as network data has exploded in volume, velocity and variety, network monitoring solutions have been behind the curve in adopting new technologies and approaches to cost-effectively scale and accommodate a widening virtualization trend. Customers are demanding greater freedom in how applications are deployed and are moving to a consolidated, shared model of data using big data frameworks, such as Hadoop, which enable large-scale processing and retrieval for multiple stakeholders.

Join Andrew R. Harding, VP of Product Line Management at VSS Monitoring, as he discusses:
- Big data and its implications for network monitoring and forensics
- Why network monitoring solutions are lagging from a virtualization standpoint and why this is a problem for network owners
- How certain traditional network monitoring functions will eventually be offloaded to adjacent technologies
- How Network Packet Brokers can accelerate the adoption of virtualized probes, “open” storage, and big data technologies within network management / monitoring
• How a Big Data Visibility architecture can enable network data to become part of the “big data store,” allowing it to integrate with the rest of enterprise data

Presented by the experts with the facts.

The Inside Story of the Discovery, the Timeline and Solutions to Protect Your Organization. Finally, All of Your Questions Answered.