Frequently Asked Questions
Questions and answers about IoT analytics
IoT data is distributed and disparate in nature. This means that to enable real-time IoT data streaming and analytics, you must have an efficient method of data ingestion or integration. The ingestion step usually involves automated data collection across different data sources, aggregating everything into a data warehouse or data lake. Depending on the nature of the data collected, the design of the ingestion process and storage will vary widely.
In data transformation, you merge or join the collected data as needed and run any additional operations to get it into the optimal format.
Gartner has predicted that by early in the 2020s, that more than half of major new business entities will incorporate some elements of IoT in their systems. The complexity of the vast volumes of data generated through these IoT systems creates a need for deep data analytics tools and skillsets. However, IoT data tends to be far messier than common business data, incorporating large streaming volumes of data that are often geospatial in nature. With such complex data, gleaning any insights or understanding from it will require an IoT specific analytics solution.
IoT analytics can be used to simplify that enormous volume of data into actionable insights and understandable dashboards.