- Sales, Marketing and Operations are most active early adopters of IoT today.
- Early adopters most often initiate pilots to drive revenue and gain operational efficiencies faster than anticipated.
- 32% of enterprises are investing in IoT, and 48% are planning to in 2019.
- IoT early adopters lead their industries in advanced and predictive analytics adoption.
These and many other fascinating insights are from Dresner Advisory Services’ latest report, 2018 IoT Intelligence® Market Study, in its 4th year of publication. The study concentrates on end-user interest in and demand for business intelligence in IoT. The study also examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. “While the market is still in an early stage, we believe that IoT Intelligence, the means to understand and leverage IoT data, will continue to expand as organizations mature in their collection and leverage of sensor level data,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. 70% of respondents work at North American organizations (including the United States, Canada, and Puerto Rico). EMEA accounts for about 20%, and the remainder is distributed across Asia-Pacific and Latin America. Please see pages 11, 15 through 18 of the study for specifics regarding the methodology and respondent demographics.
Key insights gained from the study include the following:
- Sales, Marketing and Operations are most active early adopters of IoT today. Looking to capitalize on IoT’s potential to gain real-time customer feedback on products’ and services’ performance, Sales and Marketing lead all departments in their prioritizing IoT’s value in the enterprises. 12% of Operations leaders say that IoT is critical to attaining their goals. Executive Management and Finance have yet to see the value that Sales, Marketing and Operations do.
- Manufacturers see IoT as the most critical to achieving their product quality, production scheduling and supply chain orchestration goals. Insurance industry leaders also view IoT as critical to operations as their business models are now concentrating on automating inventory and safety management. Insurance firms also track vehicles in shipping and logistics fleets to gain greater visibility into how route operations can be optimized at the lowest possible risk of accidents. Financial Services and Healthcare are the next most interested in IoT with Higher Education and Business Services assign the lowest levels of importance by industry.
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- Investment in IoT analytics, application development and defining accurate, reliable metrics to guide development is the most critical aspect of IoT adoption today. Investments in the data supply chain including data capture, movement, data prep, and management is the second-most critical area followed by investments in IoT infrastructure. Analytics, application development, and accurate, reliable metrics guiding DevOps are consistent with the study’s finding that early adopters have an excellent track record adopting and applying advanced and predictive analytics to challenging logistical, operations, sales, and marketing problems.
- IoT early adopters or advocates prioritize dashboards, reporting, IoT use cases that provide data streams integral to analytics, advanced visualization, and data mining. IoT early adopters and the broader respondent base differ most in the prioritization of IT analytics, location intelligence, integration with operational processes, in-memory analysis, open source software, and edge computing. The data reflects how IoT early adopters quickly become more conversant in emerging technologies with the goal of achieving exponential scale across analytics and IoT platforms.
- The criticality of advanced and predictive analytics to all leaders surveyed is at an all-time high. Attaining a (weighted-mean) importance score of 3.6 on a 5.0 scale, advanced and predictive analytics is today considered “critical” or “very important” to a majority of respondents. Despite a mild decline in 2017, importance sentiment (the perceived criticality of advanced and predictive analytics) is on an uptrend across the five years of our study. Mastery of advanced and predictive analytics is a leading indicator of IoT adoption, indicating the potential for more analytics pilots and in-production IoT projects next year.
- The most valuable features for advanced and predictive analytics apps include support for a range of regression models, hierarchical clustering, descriptive statistics, and recommendation engine support. Model management is important to more than 90% of respondents, further indicating IoT analytics scale is a goal many are pursuing. Geospatial analysis (highly associated with mapping, populations, demographics, and other web-generated data), Bayesian methods, and automatic feature selection is the next most required series of features.
- Access to advanced analytics for predictive and temporal analysis is the most important usability benefit to IoT adopters today. Second is support for easy iteration, and third is a simple process for continuous modification of models. The study evaluated a detailed set of nine usability benefits that support advanced and predictive activities and processes. All nine benefits are important to respondents, with the last one of a specialist not being required important to a majority of them at 70%.
