According to a BusinessWire report, global mobility as a service market is projected to hit the revenue of
99.8 billion dollars by 2025 at a CAGR of around 32% over the period from 2019.
Mobility Data Pipelines for IoT using
Mobility Data meant as any kind of transportation data generated by activities or transactions using digitally-enabled mobility devices such as smartphones, e-bikes, on-board vehicle IoT devices, or any other navigation systems. Mobility data has enormous importance in many fields such as improving road safety, controlling pollutions, reducing traffic congestion, and most importantly help the administrations to design better policies for smart cities.
A few key real-life use-cases of Mobility Data
- Mobility data helps retail store managers to optimize business strategies, launch promotions, location-based advertising, and allocating resources by calculating the footfall in or around their stores.
- Administrations can analyze traffic patterns and road conditions to better design infrastructure and add more transit routes in heavy traffic areas.
- Enables people to get real-time information about traffic, buses, and other public transportations to reduce their traveling time.
Mobility Data comes with its own Challenges
- The biggest challenge to securely receive and store unprecedented amounts of streaming data coming through different types of IoT devices and navigation systems.
- Next hard thing is to validate, clean, and transform unstructured data into a common format to extract valuable information out of it at run-time.
- And very important to store structured data in such a way to run geospatial ad hoc queries, real-time data analytics, and serve machine learning models to detect patterns and insights.
How crossML Helped the Customer
Being a technical partner, we have already delivered such solutions to our customers and helped them with all technical tools and services required to store, process, and analyze unprecedented amounts of mobility data so they can completely focus on other business goals.
- Designed fully managed automated data pipelines to collect unprecedented amounts of streaming data from IoT devices securely and without losing any data packet.
- In the transformation phase, we provided the serverless architecture to crawl unstructured data and convert it into a common format by using fully customizable and dynamic algorithms.
- Provided the right set of tools to store the transformed data into the data lakes or data warehouses in such a way to run ad hoc queries, data analytics, and data visualizations.
- Our Machine learning pipelines automatically connect with data lakes to train models and generate insights to help our customers to make the right future decisions.