The term “Big Data” increasingly refers to analyzing massive amounts of data that are both “at rest” (i.e., persisted) and “in motion” (i.e., streaming). To achieve a comprehensive understanding of business data, events and processes, and to respond to these data, events and processes in an appropriate timeframe, a solution that seamlessly addresses both is critical. Together, Operational Intelligence and persisted Big Data can provide this solution.

By: Brian Bohan, Vice President of Worldwide Sales Consulting

Granted I mostly attended the C-track presentations and discussions, which were those that were focused on advanced process implementations and examples, I have to say that at times I thought I was at an analytics and/or social media and collaboration show. All the new and exciting stuff is in these areas.

Utilities are deploying smart grid and smart meter capabilities to provide more information about the state of their distribution networks and the consumption patterns of their customers. This new availability of data opens-up many possibilities for improved operations in the areas of service assurance, demand response management and better customer care. However, as smart grids become more interconnected, the number of access points increases along with the number of potential vulnerabilities to intrusion. Thus, as smart grids become more pervasive, the challenge becomes how to maintain the security of a dynamic and growing smart grid network.

Traditional network-centric Service Assurance solutions lack insight into customer behavior and the impact of network performance on their customers. As a result, call centers are overloaded as energy utilities attempt to resolve customer issues and meet service-level agreements (SLA) using historical data and reactive processes.

As energy consumption rises, energy producers and utilities continue to deploy Demand Response Management programs to help reduce peak period pressure on the electric utility infrastructure. Successful Demand Response Management programs have demonstrated load reduction and cost savings.

Energy utilities struggle to see a complete, end-to-end view and monitor critical customer service processes since such processes span multiple systems, such as customer service (CRM), service provisioning, billing, etc. This leaves utilities unable to take real-time corrective action before the customer experience is impacted, which can potentially result in the loss of millions of dollars due to increased customer service costs and customer churn.

Many, if not most, utilities are deploying smart grid and smart meter capabilities to provide more information about the state of their distribution networks and the consumption patterns of their customers. This new visibility opens-up many possibilities for improved operations in the areas of preemptive maintenance, demand management, real-time pricing, and better customer care. The challenge is how to take advantage of this new information to deliver bottom-line results.

In my last blog post, I discussed the problems faced by today’s contact centers, especially with regards to the use of modern technology. In this blog post, I discuss a robust solution that addresses these problems faced by today’s contact centers.

Distinct from call centers – that purely handle phone correspondence – contact centers are used by companies to manage all client contact through a variety of mediums, including phone, fax, e-mail and increasingly, online live chat.

I enjoyed a recent analogy from my VP of Worldwide Sales Consulting, Brian Bohan, comparing Operational Intelligence (OI), which focuses on analytics velocity, to Business Intelligence (BI), which is more retrospective. Let’s say that you’re crossing a busy street, would you want to know how things were an hour ago? A minute ago? Or would you want some Operational Intelligence about that car coming right around the corner as you get ready to step-out onto the crosswalk? At the same time, if you were designing that crosswalk, you’d want more Business Intelligence, and be able to understand what has happened over time, with traffic patterns and such, to be able to design a crosswalk that best addresses the needs of the pedestrian with an eye towards good traffic flow.