This is the fourth article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide a discussion of IoT analytics value drivers and return-on-investment (RIO) for the enterprise. Previous parts to this special feature:
Part 1: Internet of Things – An Overview
Part 2: Marriage of IoT Analytics and the Cloud
Part 3: The Rise of IoT Analytics
IoT Analytics Value Drivers and ROI for the Enterprise
The return-on-investment (ROI) for IoT is dependent on having the right analytics solution in place to ensure ROI is achieved. IoT is becoming more about understanding how to analyze your data and what you do with it, as well as understanding the data storage implications and not just focus on how many devices are connected to your networks.
Across all industries, consider the following short-list of 5 high-level value drivers for IoT, realizing that specific industries may have different priorities, e.g. the drivers for retail may be different from those for oil & gas:
- Lower operational costs
- Create new revenue streams by encouraging product and/or service improvement and innovation
- Decrease defects by monitoring, analyzing and acting on physical-world situations remotely and autonomously
- Increase automation which increases production
- Refocus personnel on tasks requiring human involvement
Harvesting all of the data that IoT generates is only half of the equation, the other part is making the insight derived from the data actionable – this is where real value lies. Traditionally, companies have used methods of data science to look for trends and opportunities. In the world of IoT, searching for pieces of information in petabyte sized databases is a daunting task. To help extract value quickly and effectively, companies are turning to machine learning technologies. Combining stores of historical data with the type of real-time data available through connected devices gives decision-makers the capability of setting impactful course corrections.
Cleaning Data On-the-Fly
One important yet often unintended value in deploying IoT analytics applications is cleaning data on-the-fly. Bringing the data cleaning machinery across to the real-time system proves to be a significant value creator. Doing data integration, prep, and cleaning in real-time offers wide-ranging ROI. With data-at-rest, i.e. the historical data, finding insights typically requires a lot of data clean-up to get it ready for the analytics stage. Often 80% of the work is centered on cleaning the data whereas the balance of effort involves finding the rules and models. Once you take those models across to the real-time system, you realize you have to clean up the data before you can apply those rules and models. Taking the next step forward, now that you’re cleaning up the data in the real-time streaming system you can write that data back to the at-rest database, a process which adds significant value. There’s also value in anomaly detection for data-at-rest and taking those insights and applying them to create actions and interventions on data-in-motion.
Extending Equipment Life
On the equipment side, there is vast ROI in extending equipment life, keeping systems running, and optimizing preventive maintenance. The very nature of IoT with connected networks of devices leads to multiple incremental amounts of ROI – when you take the pieces across the whole network it really adds up. These kinds of ROI calculations often are surprising in their strength. As an example of this effect, consider Occidental Petroleum (a case study is provided later in this guide) that maintains around 3,000 devices on a connected network – you just have to increase the uptime of the equipment an incremental amount and it adds up. The devices are running 24/7 and If you can keep them running 24/7 all year, while avoiding failures and shutdowns and you multiple that by 3,000, you get a big number. So with such predictive maintenance examples, significant ROI calculations arise – $100s of millions per year across a reasonable sized connected network of IoT. There also is an observed phenomenon some call the “Halo effect” where once businesses adopt IoT, we begin to see new lines of business opening up. As an example from the insurance industry, we’re seeing products like connected car insurance policies. These companies don’t have that many people on those types of policies at this time but its growing. It’s a healthy new line of business.
There are also ROI considerations in terms of maintenance, preventative maintenance and also optimal maintenance. As another example, consider a large farm tractor that has a $250,000 engine and its maintenance can run in the $1000s of dollars. Imagine over a 3 year period having to perform maintenance 5 times instead of 6 times, so you get that 6th maintenance back. Now imagine you have an IoT application that is predictive for when that engine is going to fail – using vibration and temperature sensors. You can start to see the ROI just in terms of maintenance, preventative maintenance and also optimal maintenance. The goal is to maintain the asset before it fails, but not to over maintain it because there is a cost to maintain it. Considering a wide variety of assets in any industry has $ billions in implications – using an optimization/preventative maintenance IoT application.
As yet another example of IoT analytics ROI, consider a production line that was previously labor intensive. With IoT, sensors can receive orders, initiate fabrication, sign off work orders, and even package products, all with IoT and with little human interaction.
IoT Paradigm Shift
Often the value drivers and resulting ROI for IoT amount to an important paradigm shift. Consider the energy grid sector. Companies traditionally have sold the equipment but now they’re selling energy delivered to the grid. This shift has totally changed their business model because now that they can monitor the equipment and guarantee a certain amount of up-time based on the data, they can now just guarantee the delivery of this energy to the grid for the customer; they sell a service instead of a piece of equipment. Further, they negotiate with the amount of up-time in the equipment as well as how much they can guarantee, and they start getting into revenue sharing deals. The business landscape is changing based on the ability to monitor and maintain the equipment – “energy as a service.”
Another paradigm shift is toward omni-channel customer engagement involving mobile devices, online customer sessions, and in-store experiences. These forms of engagement are all digitized now with the back-end creating significant value. When you have the ability to ship product from different distribution centers, you’ve got real-time inventory, you can see where the product is in the supply chain node with stores and distribution centers, you can get the product to the customer at the lowest price, you can optimize pricing based on demand, and you can determine on-shelf availability of products as they go out.
Contributed by Daniel D. Gutierrez, Managing Editor of insideBIGDATA. In addition to being a tech journalist, Daniel also is a practicing data scientist, author, educator and sits on a number of advisory boards for various start-up companies.
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