Top 3 Growth opportunities in the Artificial Intelligence & Analytics Industry for 2021

Demand for location data is being driven by a wave of context-aware applications
Leveraging contextual data with respect to location is enabling enterprises to develop new business models, drive operational efficiencies, and enhance customer experience.

Context-aware applications enrich the customer experience for a range of applications in social networking, navigation, AR-based games, among others, by leveraging real-time data about the user’s surroundings (location, time, weather, etc.)

This growing need for contextual information is leading to a rapid expansion of the collection and monetization of location data. Location-based Services (LBS) are growing rapidly and bundled as part of business applications. The underpinning layer is that of location-based analytics which leverages geographic data and information across business use cases.

Location-based analytics and services will witness adoption across industry verticals

• The transportation & logistics vertical exhibits a strong demand for asset tracking & monitoring as well as navigation services from land transport operators and the transport and logistics functions of petroleum, FMCG and retail firms.
• Governments across the region are aggressively pursuing open data policies around location data to support enterprise LBS uses cases. A growing number of start-ups and SMEs are basing their business models on combining this free and open location data with other propriety sources of data to create customized context-aware applications and services for a range of niche use cases.
• Governments are also expected to continue a high level of spend on location intelligence to support a large and diverse portfolio of location- based services for public use, such as parking and traffic management solutions.

Edge analytics create new opportunities and use cases
Organizations are recognizing the benefits of data analytics at the edge, given the ability to process real-time data with lower latency and bandwidth requirements.

Ability to perform analytical computing at a sensor, network switch level is definitely a game changer and expected to find immense application as IoT deployments increase.

Leading global technology and IT services vendors have introduced edge analytics services enabling customers to scale up use of analytics solutions

Edge analytics is expected to create a host of opportunities, both for enterprises deploying IoT as well as the ICT ecosystem

Edge analytics will not replace cloud analytics

• While edge analytics addresses one of the main reasons why enterprises are investing in IoT (to enable real-time customer data analytics) and is expected to witness significant adoption, several challenges will persist concerning deployments.
• Edge analytics may not be optimal in cases where raw input/data points are required for storage or the requirement is for processing complex data algorithms.
• It is thus for multiple reasons that cloud-based analytics will continue to be critical as large volumes of historical data will be essential to drive the true value of analytics. Edge analytics will augment cloud analytics and integration of the unstructured data arising from sensors will be a key growth opportunity.

Video analytics ecosystem matures
The application, hardware and hosting ecosystem of the video analytics market is evolving. A new breed of video-based applications embed such functions as rapid people-counting, traffic heat maps, video- based quality control, affective state analysis, video-based checkout systems and augmented signage.

Cloud computing deployments are also helping users manage, process and store video data securely, eliminating the need to invest in expensive infrastructure. Service providers are extending video analytics-as-a- service offerings for end-users to seamlessly integrate video analytics tools with their installed video hardware.

Growing demand for video analytics solutions beyond surveillance use-cases

• Video analytics has been adopted for surveillance, security and safety by enterprise and the public sector. In addition, adoption is increasing in other industries, such as retail for customer analysis.
• Innovative use cases, such as for employee recruitment, are in a relatively nascent stage but gaining traction and are expected to gain mainstream adoption in years to come.
• Deep learning and machine learning algorithms to analyze data are further expected to drive new use cases. Automated solutions, with artificial intelligence, are capable of analyzing the large amounts of data that videos generate. Use cases include facial recognition and related applications across industries.

Frost & Sullivan