What is IoT Analytics SoftwareIoT is a combination of technologies, software and services, tools, policies, platforms, guidelines approaches, and a set of professional services. IoT Analytics software and solutions are defined and judged by their capabilities to reduce the overall operational time, cost, and required expertise, to develop analytics-rich AoT applications. The solutions are primarily responsible for collecting, integrating, cleansing, and filtering data from Internet of Things (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.
360Quadrants recognizes the below-listed companies as the best IoT Analytics Software -
Top 10 IoT Analytics Software in 2020:
- PTC INC
The IoT Analytics software market is expected to grow from USD 7.19 Billion in 2017 to USD 27.78 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 31.0%. Factors such as the tremendous growth of IoT data and the need for advanced analytics and automation of businesses are driving the global market. In the component segment, the IoT Analytics software segment is expected to have the larger market share during the forecast period. The demand for IoT Analytics is rising as organizations are looking for solutions to generate business-related insights and plan the next steps accordingly based on the insights gathered. Vendors provide software and solutions that assist companies with data collection and data analysis for generating meaningful insights. IoT Analytics software solutions filters the aggregated and enriched data so that it can be analyzed to provide a high throughput from multiple live input data sources. Among applications, the predictive maintenance and asset management application is expected to continue its dominance during the forecast period. Asset management integrated platforms assist users in managing physical assets and tracking equipment performance. The platforms also provide service assurance by enabling real-time alerts and providing automated corrective actions. The cloud deployment model is expected to exhibit a higher adoption compared to the on-premises deployment model. Cloud-based solutions are gaining a firm hold on the market, due to growing demands for improved service and cost-effectiveness, and the increasing needs of organizations to keep track of operational processes for maintaining productivity. The manufacturing industry vertical is expected to have the largest market share and lead the market during the forecast period. IoT Analytics can be exploited by manufacturers to create smarter products; connect and integrate with customers; streamline innovations, planning, and pre-manufacturing processes; and improve post-manufacturing support and services.
The vendors have been placed into 4 categories, based on their performance in each criterion: “visionary leaders,” “innovators,” “emerging companies,” and “dynamic differentiators.” The top 25 players have been evaluated in this section of the report. The analysis has been carried out based on specific parameters and scores have been assigned accordingly.
Vendors who fall into this section receive high scores for most of the evaluation criterion. The vendors in this section have a strong and established product portfolio, and a very strong market presence. They provide mature and reputable AoT software and services that cater to a wide range of verticals, globally. They also have strong business strategies. The companies falling in this category include IBM, Microsoft, Oracle, SAP, Cisco Systems, Dell Technologies and Google.
The vendors included in the dynamic differentiators section have the potential to broaden their product portfolio to compete with other key market players. The companies falling in this category include HPE, Amazon Web Services and General Electric.
The companies falling in the innovators section include PTC, Hitachi, Teradata, Glassbeam, AGT International, Software AG, TIBCO Software, Striim, Ericsson, and Vitria Technology. These players have a strong product portfolio and robust business strategy to achieve continued growth. These players have innovative products and potential to build strong strategies for their business growth to be at par with the visionary leaders.
Emerging companies in the AoT market include Greenwave Systems, Splunk, Salesforce.com, Information Builders, mnubo and RapidMiner. These players have the potential to build a strong product portfolio and business strategy to compete in the market with the visionary leaders and innovators.
Frequently Asked Questions
What are the opportunities in the IoT analytics market?The IoT analytics market size is expected to grow from USD 7.2 billion in 2017 to USD 27.8 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 31.0% during the forecast period. Factors such as the tremendous growth of IoT data is expected to fuel the market growth. However, the ownership and control of IoT data is expected to restrain the market growth.
What is the competitive landscape in the market?The IoT analytics solution and service vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions, to strengthen their offerings in the market. The Major players operating in the market include IBM Corporation (US), Microsoft Corporation (US), Oracle Corporation (US), SAP SE (Germany), Cisco Systems, Inc., (US), Dell Technologies, Inc. (US), Google, Inc. (US), Amazon Web Services, Inc. (US), HP Enterprise Co (US), PTC, Inc. (US), Hitachi, Ltd. (Japan), Teradata Corporation (US), Salesforce.com, Inc. (US), Greenwave Systems, Inc. (US), and mnubo, inc. (US).
What are the emerging technologies impacting the overall IoT analytics market?Predictive analytics transforms data into valuable insights, facilitating enterprises to optimize business processes, enhance process efficiencies, and increase customer satisfaction. Predictive analytics makes use of real-time data as well as historic data to come up with futuristic outcomes before the actual event takes place. The predictive analytics type is expected to have the largest share in the AoT market due to its ability to determine data and predict future outcomes and trends. The predictions that flow from predictive analytics are transformed into a set of recommended actions that will generate business value. This assists procurement professionals to view business scenario and build future operational strategies. Predictive analytics can also be used for demand forecasting using multiple regression models, which helps organizations determine demand and supply forecasts for different product categories. Such forecasts can prove beneficial while calculating the company revenue and budget growth. Companies are also focusing on providing predictive maintenance and asset maintenance using predictive analytics to understand the device repairing time, minimize downtime, and take swift corrective actions. Predictive analytics when incorporated in AoT, assists a manufacturing unit to ensure continuous operations by implementing asset maintenance or helps a government institute to shut down their servers if suspicious web traffic is detected.
What are the key use cases existing in the IoT Analytics market?The top 10 use cases in IoT Analytics market are: Predictive maintenance, Quality inspection & assurance, Manufacturing process optimization, Supply chain optimization, AI-driven cybersecurity & privacy, Automated physical security, Automated data management, Smart assistants, AI-driven research & development, Autonomous resource exploration.
What are the key trends and dynamics existing in the IoT analytics market?The IoT Analytics Market is moving towards the cloud based environment. Cloud-based solutions require less physical set-up, incur lower maintenance costs, and provide 24/7 accessibility from anytime, anywhere. Cloud-based solutions prove to be flexible, agile, and cost-effective, as compared to on-premises solutions. Cloud-based AoT helps organizations to reduce cost and IT complexity; it also improves business agility and increases collaborations by shared services. Globally, large enterprise and SMEs are shifting toward cloud-based solutions to streamline their processes easily and minimize upfront costs. Some of the prominent vendors offering cloud-based AoT software and services include IBM Corporation, Oracle Corp., AWS, and Salesforce.com, Inc.
Which are the recent developments made in IoT Analytics market?In February 2019, IBM announced access to its IBM Watson on any cloud. Also, the vendor can deploy AI software to automate business wherever the data resides. In June 2019, Cisco announced software innovations for simplified management and security of networks. Also, it introduced new artificial intelligence and machine learning capabilities that enable IT teams to function at machine speed and scale through tailored network insights.
Oracle provides an IoT platform to develop new IoT services quickly, using Oracle portfolio of products. This enables organizations to handle growing volumes of data, streamline application development and deployment, automate integration of data, and protect the data across the entire IoT value chain. Oracle provides a variety of IoT analytics solutions, including Oracle IoT Cloud Platform as a Service (PaaS) and Oracle IoT applications with built-in analytics for Key Performance Indicators (KPIs), anomaly detection, comparative analytics, and predictive analytics. Oracle also provides an IoT cloud service which is a secure and scalable platform to quickly build and deploy IoT applications, and capture and analyze IoT data.
The Cumulocity IoT from Software AG is an IoT platform which connects any device from any of the cloud platforms and can be integrated with any of the applications. The platform is very quick to start, in just ten minutes and in very simple steps. The platform offers the benefit of not requiring coding and analysis for business users and analysis can be carried out without coding or programming skills. Users require the same APIs, data and analytics models everywhere, in the cloud and on-premises.