In the MarketsandMarkets taxonomy, we define IoT Analytics market as – IoT analytics is a collection of technologies & solutions, tools, policies, platforms, guidelines approaches, and set of professional services. IoT analytics products and solutions are defined and judged by possessing the capability to reduce the time, cost and required expertise to develop analytics-rich IoT applications. The solutions are primarily responsible for collecting, integrating, cleansing, and filtering data from IoT sensors and devices. The solutions then apply model-based and data-driven prediction analytics, as well as optimization and simulation on the collected data to generate useful information.

• The global IoT analytics market is estimated to be $4,857.2 million in 2015 and is expected to reach $16,353.5 million by 2020 at a CAGR of 27.48% from 2015 to 2020.
• The increasing penetration of smart connected devices and predictive analytics are the key driving factors of this market.
• The absence of efficient real-time analytics platforms and data security are restraining the growth of the IoT analytics market.
• The development of new technologies such as PaaS and edge analytics is expected to provide a huge opportunity for the growth of the IoT analytics market.
• The key challenge faced by this market is the filtration of huge amount of heterogeneous data.

One of the main drivers of this market is- In the current scenario as more and more devices are getting connected to each other to form the IoT, predictive analytics has gained a prominent role in the background. It has become an underlying part of smart decision-making which is independent on human users. For example, a self-driven car uses the Global Positioning System (GPS) to provide the shortest possible route from one place to another. The GPS uses predictive analytics algorithm for controlling the car and choosing the shortest path. Predictive analytics uses data, statistical algorithms, and M2M learning to identify the probability of future outcomes using historical data. Predictive analytics is an outcome of IoT analytics because it uses same IoT data as an input and refine it to generate inferences and predictions.. IoT analytics has the capability to process the data generated through different things connected to the internet to develop real-time dashboards mapped on various parameters. IoT analytics solutions enables the users to take important business decisions by analyzing the current data and predicting future outcomes based on the previous data available. Thus, predictive analytics for business is acting as a huge driving factor for IoT analytics market.

One of the main restraints in this market is- The biggest problem with the IoT analytics market is the lack of efficiency of real-time algorithms. Real-time algorithms are intended to serve real-time applications and process data as it comes in without any delays. The IoT analytics being a new domain does not have robust algorithms and platforms to handle the ever increasing size of IoT data. The rate of generation of data is very high and the rate of data analysis is not as quick as required. As a result of which, often a new algorithm’s or platform’s life is short-lived and it gets obsolete quickly thus leading to failure of system to handle the heavy traffic of data generated from IoT enabled devices and sensors. This hampers the efficiency of entire system and is a major cause of concern for many companies because it ultimately results in loss of revenues for them. This is an acute problem and will solve gradually when the market will grow and technology will evolve in subsequent years.