The emerging Internet of Things (IoT) extends digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation enables devices designed for it to generate and transmit data about their operations; analytics using this data can facilitate monitoring and a range of automatic functions.vr_oi_goals_of_using_operational_intelligence_updated

To perform these functions IoT requires what Ventana Research calls Operational Intelligence (OI), a discipline that has evolved from the capture and analysis of instrumentation, networking and machine-to-machine interactions of many types. We define operational intelligence as a set of event-centered information and analytic processes operating across an organization that enable people to use that event information to take effective actions and make optimal decisions. Our benchmark research into Operational Intelligence shows that organizations most often want to use such event-centric architectures for defining metrics (37%) and assigning thresholds for alerts (35%) and for more action-oriented processes of sending notifications to users (33%) and linking events to activities (27%).

In many industries, organizations can gain competitive advantage if they can reduce the elapsed time between an event occurring and actions taken or decisions made in response to it. Existing business intelligence (BI) tools provide useful analysis of and reporting on data drawn from previously recorded transactions, but to improve competitiveness and maximize efficiencies organizations are concluding that employees and processes – in IT, business operations and front-line customer sales, service and support – also need to be able to detect and respond to events as they happen. Our research into big data integration shows that nearly one in four companies currently integrate data into big data stores in real time. The challenge is to go further and act upon both the data that is stored and the data that is streaming in a timely manner.

The evolution of operational intelligence, especially in conjunction with IoT, is encouraging companies to revisit their priorities and spending for information technology and application management. However, sorting out the range of options poses a challenge for both business and IT leaders. Some see potential value in expanding their network infrastructure to support OI. Others are implementing event processing (EP) systems that employ new technology to detect meaningful patterns, anomalies and relationships among events. Increasingly, organizations are using dashboards, visualization and modeling to notify nontechnical people of events and enable them to understand their significance and take appropriate and immediate action.

As with any innovation, using OI for IoT may require substantial changes. These are among the challenges organizations face as they consider adopting operational intelligence:

  • They find it difficult to evaluate the business value of enabling real-time sensing of data and event streams using identification tags, agents and other systems embedded not only in physical locations like warehouses but also in business processes, networks, mobile devices, data appliances and other technologies.
  • They lack an IT architecture that can support and integrate these systems as the volume and frequency of information increase.
  • They are uncertain how to set reasonable business and IT expectations, priorities and implementation plans for important technologies that may conflict or overlap. These can include business intelligence, event processing, business process management, rules management, network upgrades and new or modified applications and databases.
  • They don’t understand how to create a personalized user experience that enables nontechnical employees in different roles to monitor data or event streams, identify significant changes, quickly understand the correlation between events and develop a context in which to determine the right decisions or actions to take.

Ventana Research has announced new benchmark research on The Internet of Things and Operational Intelligence that will identify trends and best practices associated with this technology and these processes. It will explore organizations’ experiences with initiatives related to events and data and with attempts to align IT projects, resources and spending with new business objectives that demand real-time intelligence and event-driven architectures. The research will investigate how organizations are increasing their responsiveness to events by rebalancing the roles of networks, applications and databases to reduce latency; it also will explore ways in which they are using sensor data and alerts to anticipate problematic events. We will benchmark the performance of organizations’ implementations, including IoT, event stream processing, event and activity monitoring, alerting, event modeling and workflow, and process and rules management.

As operational intelligence evolves as the core of IoT platforms, it is an important time to take a closer look at this emerging opportunity and challenge. For those interested in learning more or becoming involved in this upcoming research, please let me know.


Tony Cosentino

VP and Research Director

Founded in 2000, LiveOps has evolved a unique two-sided business model. On one side is LiveOps Agents on Demand,  an Uber-like business in which home-based workers sign-up as LiveOps agents, and the company uses them to provide outsourced contact center services. This model enables LiveOps to provide flexible levels of service; customers can scale up and down as needed while the provider is able to manage agent numbers cost-effectively. The agents use the LiveOps Cloud Contact Center platform; in this way the company can test its system and use these agents’ experiences to improve the platform as used on the other side of the business. I have previously covered their focus on contact centers in LiveOps Improves the Agent Experience. LiveOps reports revenues growing on both sides and being able to expand its cloud contact center business globally.

The Cloud Contact Center platform provides interactions through voice, chat, email and social engagement and manages all these channels in the same way; it supports a single queue and routes all interactions according to the same rules. Companies thus handle interactions in a consistent way, swapping between channels if need be, which goes a long way toward ensuring that customers receive an omnichannel experience. LiveOps has designed the platform to require little support from IT. Being cloud-based it doesn’t require special on-site hardware, and the desktop removes the need for agents to use handsets. It is easy to configure, can be scaled to meet most companies’ needs, supports a distributed operation and is based on an open architecture that enables integration with other on-premises or cloud-based systems.

This emphasis is further strengthened by additional tools. One of the most important in my opinion is the LiveOps Engage agent desktop system. I have written recently about the importance of smart agent desktops in providing experiences that meet customers’ expectations. Such systems bring together information and technology that agents need – customer information, engagement history, access to other business systems such as CRM and access to multiple channels of engagement – but often is stored in separate systems. They enable the agent to focus on the customer and not the systems. LiveOps Engage has these capabilities and a few others. It allows agents to toggle between online, real-time channels such as voice to less urgent channels such as social interaction. Agents see when a customer has dropped from one channel but is available to continue the interaction on another. To support offline channels such as email, LiveOps provides templates of responses that allow the agent to plug in customer data and personalize a response depending on the context of the situation. Engage integrates with third-party CRM systems such as and Microsoft Dynamics for two-way transfers of data. The desktop is WebRTC-enabled so agents can control making and receiving phone calls from within the desktop. This combination of capabilities helps agents handle customer interactions efficiently while providing customers with the information and experiences they expect. In turn it helps companies meet key objectives and hold down costs while optimizing customer-related metrics such as customer satisfaction and net promoter score.vr_CCC_actions_to_improve_customer_interaction_updated

LiveOps Cloud Contact Center also provides support to help a company manage its contact center performance. LiveOps Recording goes beyond recording interactions for future analysis to capture agents’ use of their desktop, providing key information about the processes, systems and information agents use to handle interactions. This tool not only allows the company to review its agents’ performance but more crucially can identify best practices and offer advice on how to get agents to adopt them. LiveOps Insight supplements this analysis with broader analysis of contact center performance, with an emphasis on driving actions to improve.

In our benchmark research into the contact center in the  cloud, companies most often (73%) said that improving agent performance is the best way to improve handing of interactions, but from a technology perspective nearly two-thirds (63%) said that adopting cloud-based contact center systems is the way to move forward. LiveOps answers those intentions by providing both a platform in the cloud and interaction handling services using the platform. This dual approach has allowed it to move the platform forward and become one of the leading vendors of such systems. I recommend that companies looking to provide omnichannel customer experiences assess how LiveOps can support those efforts.


Richard J. Snow

VP & Research Director

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