As an industrial equipment or healthcare technology company, developing an IoT strategy is vital for navigating the digital transformation journey. The companies that come out on top will be those that embrace the disruption and learn to harness IoT-generated data to deliver on customer outcomes.

Although it’s easy to understand the necessity, actually deploying a successful IoT strategy comes with a collection of unique challenges for industrial equipment and healthcare technology companies. That’s why TSIA is dedicating a blog series called, “IoT Strategy for Healthcare and Industrial Equipment,” catered to addressing those unique challenges. Over the next several months, you will see blogs and quick polls relating to the following topics necessary for designing and deploying an IoT strategy:

  • Vulnerabilities of Healthcare Technologies and Industrial Equipment IoT
  • How Blockchain Can Be Used to Address Security Concerns with Industrial IoT
  • Overcoming Barriers on Edge Computing
  • IoT Analytics Strategy: What to Do With All That Data
  • Analytics Journey: Going from Descriptive to Prescriptive
  • AI and Machine Learning Technologies
  • Application of IoT to the XaaS Transformation

7 Topics You Will Learn About in The "IoT Strategy for Healthcare and Industrial Equipment" Blog Series

companies using an IoT technology platform  

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In the 2018 Global Technology Survey, only 28% of companies indicated they are currently using an IoT technology platform.

According to the 2018 Global Technology Survey, 72% of the industry is not using an IoT platform, and the 7 topics highlighted in our series will help to reduce the barriers to entry for healthcare and industrial equipment companies. We have designed the series intentionally so that one post will build off the other, corresponding to the Digital Value Chain.

digital value chain  

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The Digital Value Chain is a framework created by TSIA's vice president of field services and IoT research, Vele Galovski, outlining the processes, technologies, and operations necessary to realize valuable outcomes from digitally generated data.

Vulnerabilities of Healthcare Technologies and Industrial Equipment IoT

Before a company can successfully realize value from the wealth of information generated by IoT data, they must first understand and mitigate the risks created through interconnected devices.

For healthcare technology companies, leaving IoT-transfered data unprotected can have consequences to the very human lives they are working to improve. For industrial equipment companies, having their entire fleet connected through the internet leaves vulnerabilities for plant take over, or information interception. Existing cyber security measures for technology that protect the data at each device source are well documented, but protecting the data while it is transferred is where the vulnerability lies within IoT. New strategies and technologies need to be developed to stay secure.

How Blockchain Can Be Used to Address Security Concerns with Industrial IoT

Although blockchain was invented for the use case of cryptocurrencies, the underlying technology can be used to secure any information that is transferred across the internet. Using blockchain with IoT will reduce the risk of information being intercepted, or an entire system from being taken over, because the data sharing is anonymized and adds a layer of complexity to tracing or intercepting the information. Anyone can see that a transaction has occurred, but without the dedicated cipher for the encryption, they cannot see what was sent or where it was sent from.

This is still an emerging technology with minimum commercialization taking place, but its use cases beyond cryptocurrency have already been proven.

Overcoming Barriers of Edge Computing

The first two posts address the underlying security concerns regarding transferring information through IoT. Now comes the work of operationalizing processes and making sense of the data. Using edge computing is one important step that can help decrease time to value. We often hear from members that the main barriers to successfully harnessing edge computing are technology, skills, and psychological. We will explore those common barriers and how to overcome them futhers in this upcoming post.

IoT Analytics Strategy: What to Do with All That Data

Once fully engaged on the digital value chain and a IoT strategy is in place to generate and collect information, it’s important to design an analytics strategy for interpreting and presenting that data. In the past, TSIA’s vice president of analytics, Jeremy DalleTezze, has outlined a guide to consumption analytics, and many of these concepts are directly applicable to IoT analytics as well. This article will help you to intentionally design an analytics strategy for your organization that delivers on appropriate business outcomes.

Analytics Journey: Going from Descriptive to Prescriptive

Once an analytics strategy is in place, the analytics capabilities will mature from basic analysis (what and when) to advanced analysis (how and why) as part of the outcome services model and the Consumption Analytics Framework. This blog on the analytics will explore that journey in depth and explain how analytics capabilities must mature in conjunction with IoT development.

The journey from descriptive, to predictive, to prescriptive reporting is still developing as new technologies make it possible to access data and information and using software and platforms. When asked about data analytics insights on the 2018 Analytics Organization and Optimization survey, providing prescriptive insights is still a minority practice, but necessary to deliver on an outcome based business model.

TSIA 2018 Analytics Organization and Optimization Survey  

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Providing prescriptive recommendations is still a minority practice for analytics organizations with only 40% of companies who participated in the 2018 Analytics Organization and Optimization Survey, indicating they have this capability.

AI and Machine Learning Technologies

Using artificial intelligence (AI) and machine learning will be absolutely necessary for managing and deriving insights from the millions of data points generated by interconnected devices. This post will address AI and Machine learning technologies successfully employed by Industrial Equipment and Healthcare technology companies within analytics teams. Leveraging AI and machine learning is important for the transformation to prescriptive analytics reporting.

Successfully using AI and machine learning is a blend of people and technology, using existing resources and pre built software to fill in the gaps for technical skills and abilities. There has been a rise of the “citizen data scientist” as platforms such as BigML, LogicalGlue and DataRobot continue to emerge and develop. You can leverage these platforms with intentionality to develop your advanced analytics initiatives with your team’s existing skill sets.

Application of IoT to the XaaS Transformation

This final article in the IoT series will address how the development of an IoT strategy fits into the larger XaaS transformation taking place in the technology industry. This will help you understand the capabilities and limitation of IoT generated information, and where it can be appropriately applied to the XaaS journey.

Let TSIA Help You in Your Digital Transformation Today

We look forward to sharing our findings with you on this important topic impacting healthcare and industrial equipment companies. In the meantime, please reach out to TSIA today to learn how our proven frameworks, data-backed best practices, and expert advice can help your company successfully navigate the digital transformation journey. Thank you for reading!

 
 
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About Sarah Swanson

About Author About Sarah Swanson

Sarah Swanson is a research analyst for TSIA and is part of the company's "A-Team", which works to collect and analyzes technology and services industry data for the benefit of TSIA members. She holds a Masters in Social Science Research from University of Chicago and has worked in the analytics field for 5 years applying research methodologies and quantitative analysis to various data sources. She has a passion for using data-driven processes to improve efficiencies and optimize performance.