What should IoT 2.0 look like?
According to a Cisco survey cited in a 2021 analysis, only 26% of IoT implementers claimed success for their projects. The remaining 74% represent dollars lost and a systemic failure to turn collected data into actionable insight. This condition, called DRIP (data rich and information poor), remained a defining obstacle for IoT deployments.
IoT has the power to shed light on the darkest corners of your business and, from that darkness, data can uncover countless opportunities to improve workflow processes, optimization, and ultimately drive ROI. However, like most new trends that gain initial hype for what they can ‘possibly’ do, IoT has yet to realize its true potential. Many companies and OEMs have integrated IoT into their processes and products, but are yet to realize the power that lies deep in the terabytes of data that are being collected daily.
Many of the IoT applications being deployed today are more or less focused on taking advantage of a trend rather than delivering real world solutions. Very often, these systems are difficult to justify financially. In fact, a survey by Cisco found that only 26% of IoT implementers were able to claim success for their projects. The remaining 74% of unsuccessful implementations points to dollars lost as well as a big opportunity to change how we implement IoT and use data to drive better ROI.
Seeing Apple’s success in revolutionizing consumer product UI and UX, it’s a wonder IoT developers haven’t embraced an elegant design mentality to simplify how we consume and interact with IoT data. I often ask myself and our team this question… “What if we work as an industry to move into the next phase of IoT and ensure that the data we’re collecting reaps the benefits of implementing this technology? What if we challenged the norm and forged ahead into IoT 2.0?”
If we do this and are successful, we could finally serve the right data at the right time while adopting IoT systems that reap the benefits of using the technology.
Right now, however, we are data rich and information poor. The tools and technology are there to gather massive amounts of data. Although all of this data exists in abundance, we lack the tools to analyze, optimize, and realize the true ROI that was promised when the term IoT was introduced to the world in 1999.
What needs to be done to right the IoT course and gain the insights for business process improvement? Data is useless unless it provides context and getting to this contextual state will be made easier if we begin focusing on the following…
How Should IoT Integrate with Existing Workflows?
IoT delivers value only when it fits into existing workflows rather than forcing users to build new processes around it. When a maintenance technician cannot connect equipment data to the repair steps they already follow, that data produces no value. IoT systems must meet teams where they work, or the investment cannot be justified financially.
For example, in machine maintenance, if you have the data on how to fix something or address an issue but the technician doesn’t know how to integrate with workflow, the data is not providing value. Your maintenance specialist is then forced to work around the data to get the job done.
Your subject matter experts (SMEs) also carry a trove of information about pieces of equipment and have their own internal workflow to ensure that things don’t go down and business runs as smoothly as possible. One day, however, these SMEs may leave and when they go, all of that information around maintenance and workflow leaves as well. To avoid this, business decision makers that are evaluating technology should attribute value to systems that integrate workflows with minimal augmentation to existing processes.
Why Does Vendor Lock-In Limit IoT Value?
Vendor specificity is one of the primary reasons IoT data stays siloed and unusable. When OEMs build proprietary systems designed to protect their own product mix, machines from different vendors cannot exchange data. This siloing prevents organizations from building a unified view of operations, which is a prerequisite for any meaningful IoT return on investment.
Why? OEMs are in the business of making money and pleasing their investors. They want to sell you proprietary systems, maintenance plans, upgrades, and everything else in their overall product mix. Keeping their proprietary status and vendor specificity close to the chest makes machine integration nearly impossible. To that end, we need to focus on building platforms that integrate multiple IoT sources into a single application while working with the customer to make workflows part of the overall system.
How Do Data Silos Block IoT ROI?
Data silos, whether in hardware systems or in the knowledge held by subject matter experts (SMEs), are a core barrier to IoT value. When SMEs leave an organization, their institutional knowledge about equipment maintenance and workflow leaves with them. IoT platforms must capture and integrate that knowledge while combining multiple data streams into one accessible interface.
Why Automate IoT Analysis with Machine Learning?
Many IT executives report downloading reports and running manual analysis just to extract insights from their IoT systems. This manual approach defeats the purpose of automated data collection. Pairing automation with machine learning allows IoT platforms to surface insights continuously, reducing the burden on analysts and closing the gap between raw data and actionable information.
If we can automate the delivery of real time information, why aren’t we integrating automation with machine learning so our data is more useful over time? It’s time to harness the power of machine learning in conjunction with an ongoing stream of real time data so we can easily tap into things like predictive maintenance and workforce optimization.
If we can accomplish the above, we will move away from companies being data rich and information poor to being data rich with an abundance of actionable information, ushering in a new era in IoT.
About the Author
This article was written by Angie Sticher, Co-Founder, Chief Product Officer, and Chief Operating Officer of UrsaLeo, an enterprise software company that enables users to visualize and interact with realtime operational data in a photorealistic 3D representation of their facility or equipment.
FAQ
1. What does “data rich and information poor” mean in IoT?
Data rich and information poor (DRIP) describes organizations that collect large volumes of IoT data but lack the tools or processes to convert that data into useful insights. The term originated in the 1983 business book In Search of Excellence . In an IoT context, it means sensors and connected devices generate terabytes of data daily, but without proper analysis workflows, that data produces no competitive advantage or measurable ROI.
2. Why do most IoT implementations fail to show ROI?
According to a Cisco survey, 74% of IoT implementations failed to achieve the success their organizations expected. The core reasons include data silos that prevent integration across systems, vendor-specific proprietary platforms that block machine-to-machine communication, and IoT deployments focused on trend adoption rather than solving real operational problems. Without integrating IoT data into existing business workflows, financial justification for these projects becomes nearly impossible.
3. IoT 1.0 vs IoT 2.0: what is the difference?
IoT 1.0 focused on connecting devices and collecting data, often in siloed, proprietary systems that required users to adapt their workflows to the technology. IoT 2.0, as described in this context, means platforms that integrate multiple IoT data sources into a single application, fit existing workflows with minimal disruption, use machine learning to automate analysis, and present data through intuitive interfaces that reduce barriers to adoption and training.
4. How do subject matter experts (SMEs) relate to IoT data loss?
Subject matter experts (SMEs) carry deep institutional knowledge about equipment behavior, maintenance procedures, and operational workflows. When an SME leaves an organization, that knowledge leaves with them. IoT platforms that fail to capture and integrate SME workflows into their systems create a second form of information loss alongside technical data silos. Effective IoT 2.0 solutions must make SME knowledge a persistent, searchable part of the platform.
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