The Internet of Things, or IoT, usually refers to the proliferation of sensors and other types of data collection devices that are networked so data can be automatically collected. A fitbit is a good example – you wear a small device that continuously collects location, steps, and activity levels and submits that data to the cloud. Once the data is in the cloud, analytics are used to answer higher level questions such as how fit you are and how close you are to your health goals.
It is predicted that there will be an explosion of these networked data collection devices, and that this capability will start being incorporated into everyday things like cars and appliances. Who will the net winners be as this trend plays out? The most obvious answer is the people who supply the sensors themselves. However, I believe this is likely to be a rapidly commoditized hardware business over time. Another winning group could be the vendors providing networking services for these devices – folks like ATT and Verizon. There are going to be many more of these devices than there are PCs, tablets, or phones, and they all need wireless networking. And last but not least, the cloud providers that take this machine-generated data and use it to help answer interesting questions will emerge victorious.
In my opinion, that last category has the highest value and the most possibility. Much of the discussion today about the IoT centers on personal analytics like the fitbit. But we are just at the very beginning of a data-driven revolution of how enterprises use automatically-generated data to analyze and better understand their customers, business processes, and workforce productivity. Existing enterprise analytic efforts have largely centered around human-generated data such as CRM and finance information. Businesses have created highly-structured processes around the creation and reporting of this data to provide executives with an understanding of the performance of their business. These practices will continue, but over time the existing analytics will be augmented with new types of data – much of it sensor or machine-generated – and big data cloud analytics-based approaches will be used to understand and answer questions based on these new, large data sets.
To highlight an example closer to what we do here at Thinking Phones, today we use data generated by end user UC activity on our platform (e.g. calls, messages, presence) to help executives understand workforce productivity within their enterprises. This practice has a long history within the contact center, but one of the great opportunities of cloud UC platforms is that the same approach can now be extended to all enterprise users since they are on the same platform.
Furthermore, UC data can be combined with other cloud-based data to create new insights. I’ve written previously about the power of combining UC with CRM data to better understand inside sales performance. Our next evolution of this idea is to combine UC and CRM data with location and sensor-based activity data to understand the performance of outside sales teams. We are looking for characteristic data patterns and trends exhibited by top performing outside sales reps. Maybe we will find that the most productive reps are the most active ones with the highest number of onsite customer visits (e.g. physical location tied to Salesforce account location data). This data can help us predict which reps will be most productive in the future.
The key value that the IoT will bring to enterprises is a cost-effective way to instrument business processes and collect machine-generated data without the need for human interaction. Applying cloud analytics to this collected data is going to yield a treasure trove of insight and optimization opportunities.