Creating Value Rapidly from IoT Analytics

Creating business value rapidly from IoT Analytics

Creating Value from IoT Analytics depends on a number of factors. There is a temptation to consider technology to be the primary driver or factor, but in our experience at Vitria, we believe starting with organizational and business analysis is a much more productive launching point.



Among these organizational aspects, it is imperative to start by educating the critical stakeholders for a given project or domain that data is a strategic asset of the business. Moving to this mindset might seem obvious to some given all the noise about ‘Big Data,” but it should not be assumed that non-technologists and other operational managers have this mindset.

Among the critical steps at this stage is to ensure that the capture of IoT data is in a consumable form. This process usually means that various procedures to clean and index the data have been completed. Maintenance of high quality data is essential to ensure that analysis and action can be taken on an ongoing basis. Maintaining quality data can be a significant commitment, so it is often advisable to start on a targeted basis by identifying the most important use cases and focusing maintenance efforts on the data relevant to those cases.



With the right data in place and ready for use, the next critical consideration is to assemble the right team to explore the possibilities for IoT Analytics. Success in IoT – and particularly analytics with IoT – depends on getting diverse inputs on the possibilities and best use cases for focus. The most successful projects tend to involve a cross-section of individuals:

  • Business-focused managers
    • Operations management and COO
    • General managers of product and business units with P & L responsibilities
    • Marketing leaders driving their companies into new categories
  • Information Technology teams (IT)
    • Mainstream IT professionals in databases, networks, and other relevant specialties
    • Data Scientists
  • Application/Use Case Subject matter experts – often there are a few individuals within an organization who are the most knowledgeable about the specific application or use case. It is usually a good idea to include one or two of these people on the virtual team.

Overall, the critical point is to assemble get the right expertise together in a single working group to ensure that any IoT Analytics initiative is focused on the most important business requirements.



With strategic data identified and the team assembled, the next critical step in getting value from IoT Analytics is to integrate new contextual information. The two major steps above set the stage for understanding new contextual information. The combination of strategic data understanding and insights from cross-functional teams provides a powerful base for understanding new opportunities. Typically, new contextual information involves enriching IoT device data with business data from a CRM, ERP, or other line of business type of application. Often times the greatest value in IoT comes from a unique marriage of newly discovered device data married with business data in an unprecedented manner – insight made possible by teamwork.



As teams understand the power of potential new contextual data, it is next useful to understand the stages of evolution that often takes place in IoT value generation.

The process of getting value from IoT Analytics can be seen as a three stage process:

  • The first logical baseline step is to begin by capturing sensor data for a particular machine, device, or application. For example, analyzing sensor data on a machine to assess its performance or predicting maintenance needs or requirements.
  • The next logical step is to assess the impact of sensor data on existing business processes. Examples of this would include how sensor data can improve supply chain application efficiency or enhance location-based promotions for retailers.
  • The third – and the most valuable – stage in the IoT Analytics value generation process involves creating new possibilities and use cases that combine sensor data with business data to create processes and services that were not possible in the past. Examples of this could be:
    • offering a maintenance information service to customers of a manufactured product to recommend preventative maintenance to eliminate breakdowns and downtime
    • recommending new product upgrades or enhancements based on a particular type of customer’s use of machinery or equipment



Achieving value with IoT Analytics is an exciting journey. As you can see from our brief discussion above, there are some basic practices and approaches that help to get initial value quickly, while also setting the stage for achieving greater value over time.

Implementing this methodology does require some discipline and forethought on technology and implementation choices. Embarking on IoT Analytics is best done if you can choose a common platform to work your way through the stages of evolution described above. You do not want to be in a position to have to switch technology during the evolution. Maturing to the stage where new use cases and services can be developed requires some experience learning how a particular technology works and applying it to the use cases. Starting off with a single technology and platform that will deliver a consistent path for learning and growth is the fastest path to success and business value.

Vitria offers an open and flexible platform that can handle all the stages of evolution for IoT value generation. You can get started and proceed at your own pace, choosing the use cased that make the most sense for your business. And rest assured that your initial choices and experience will all be leveraged and drive value for your company over the long run.

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