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IoT data intelligence: creating the ‘always-aware’ enterprise

October 05, 2020 - 1111 views

IoT data intelligence: creating the ‘always-aware’ enterprise

Here are the five levers for using IoT data analytics to advance decision making capabilities and create new sources of recurring revenue.

As the world adapts to a new normal and braces for the next normal, businesses are increasingly dependent on all things digital. Pre-pandemic, organizations crafted Internet of Things (IoT) strategies in hopes of creating intelligent, data-driven products, operations and services — few of which reached widespread use. In the face of today’s challenges, businesses are more sharply focused on executing full-fledged IoT implementations. But it hasn’t been easy.

Recently, we commissioned Forrester Consulting to assess how companies can escape pilot purgatory and reliably scale their IoT implementations to truly add organizational value.

One of the more intriguing findings in the resulting report is what Forrester calls the five key levers for quickly moving IoT pilots to scaled initiatives. The analysis was based on the impact of each lever to quickly move IoT initiatives past the pilot phase through effective executive team commitment, as well as support the necessary planning, technology, skills and execution of IoT initiatives.

Forrester ranked these levers from most to least impactful, using factor and regression analysis of the aggregate survey responses. In order, these five levers include:

  1. Organizational enablement
  2. Infrastructure/technology
  3. Integration
  4. Strategy
  5. Data/analytics

Here’s the dichotomy: While the data/analytics lever was assessed as least impactful, this is precisely where the greatest value is to be found. On a closer look, though, this is unsurprising, even inevitable. Since data/analytics requires the greatest level of IoT maturity, its impact is only starting to be felt. Once businesses get the other four foundational pieces in place, they can forge a productive link between analytics and massive IoT datasets.

Data challenges and opportunities

When companies move beyond collecting data to mining relevant and timely data in operations and services, they can understand not only what has happened and why it happened but also what´s likely to happen next. This enables them to make the best possible decisions quickly and with the least risk. To become this type of “always-aware” enterprise, businesses require usable data from all relevant sources.

Similarly, with the advent of the smart-product economy, businesses must be able to not just collect data but also manage its scale and security. In both the consumer and industrial contexts, a company’s data management strategy will dictate the success of its connected ecosystem.

This dimension refers to “servitization” opportunities and new business models built on smart, connected products, in which businesses create knowledge-based service offerings around their existing product portfolio. An example is a global manufacturer of pumps that we helped recast itself as a provider of data on water delivery (see below).

Servitization is here to stay, as forward-looking companies seek new business models to create opportunities for recurring revenues, with relatively low capital expenditures, to contend with shrinking margins for unintelligent physical products.

Developing the capabilities for always-aware insights 

The following examples highlight the impact of IoT data analytics on creating always-aware operations, products, operations and services:

  • A manufacturer of water pumps for industrial and consumer applications had a problem: Its products were so reliable and long-lasting that the company was losing sales. This company, the first in its industry to integrate electronics and remote monitoring, sought to transform from a maker of pumps to a leader in the sustainable delivery of water and intelligence. We helped the business construct an IoT platform built on Microsoft Azure that will ultimately enable the enterprise to gather structured and unstructured data from 16 million pumps annually, using algorithms to perform analytics at the edge while sharing meaningful data securely via the cloud.
  • A manufacturer of consumer products sought to overhaul its global operations. Few of its assets were instrumented with sensors that could support data analytics, and the company lacked visibility into even fundamental production metrics. It also faced limitations on communicating and coordinating between facilities. We worked with the company to implement an industrial IoT (IIoT) data platform to collect data from sensor-equipped equipment and systems, and create dashboards for managers to monitor production machinery in its more than 100 manufacturing operations. Anchoring its capabilities is a hub facility in which modern industrial process management software validates real-world IIoT solutions before pushing them to its global operations — promoting product quality, improving productivity and creating new efficiencies. This IoT nerve center allows the organization to explore, showcase and test processes, and to pilot and implement pragmatic, real-world solutions that drive return on investment and enable real-time troubleshooting.

Assessing next steps

It’s important not to underestimate the difficulty of generating actionable insights from IoT data. In the Forrester study, businesses (which were categorized into Novice, Aware and Committed categories depending on the scalability of their IoT endeavors) were asked about their top challenges around data analytics when scaling IoT. Based on each category, we’ve developed recommended next steps for becoming an always-aware enterprise.

  • IoT Novices should ensure they have a clear objective and a way to distribute IoT data to key users. These organizations need to identify strong internal opportunities for their IoT data, as well as a monetization strategy with potential third parties. They also need to formulate a strategy for capturing and archiving data to enable use cases offering near- and longer-term value.
  • IoT-Aware businesses should focus on identifying structured, unstructured and analytics requirements with stakeholders while also solidifying and aligning their IoT strategy both internally and with third parties. These organizations also need to formalize the emerging third-party ecosystem with an eye toward monetization.
  • IoT-Committed organizations can move into aggressively expanding the monetization of their IoT data. To that end, they should develop a products-and-services roadmap to capitalize on opportunities and expand their roster of third-party partnerships.

While data/analytics may not have emerged as the most important lever for scaling IoT today, it’s an imperative factor for deriving value from IoT deployments. While the challenges are by no means easy to overcome, businesses seeking to maximize their use of IoT data analytics can gain insights from more mature organizations, especially to accelerate their modern decision-making capabilities.

Vivek Diwanji, Senior Director, IoT and Engineering Services and Shivanajay Marwaha Associate Director, IoT Strategy and Advisory, contributed to this blog.

Digital Business & Technology AI, artificial intelligence, data analytics, Industrial IoT, Internet of Things, iot

Randal Kenworthy

Randal Kenworthy is a Vice President in Cognizant's IoT and Engineering Services group. He has over 25 years of experience in...

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