Many business leaders have adapted to a “data-driven” approach to business decision-making.
But to fully leverage the value contained in data, it needs to be analyzed and broken down into granular actionable insights. This is where the integration of AI into Big Data transforms the field of analytics by offering a level of speed, scale, and granularity that isn’t humanly possible.
DigiconAsia checked in with Zainudin Nordin, Executive Director, CUE Southeast Asia, for insights into developments in the integration of AI, video analytics and Big Data.
How is AI helping organizations seeking to be truly data-driven in their business decisions?
Zainudin Nordin: Today AI is already helping organizations become more data-driven in several ways:
Data collection and analysis: AI can help organizations collect and analyze large amounts of data from a variety of sources, such as social media, sensors, and transactional systems. This can provide insights that would be difficult or impossible for humans to uncover on their own.
Predictive analytics: AI can help organizations make better predictions about future outcomes based on historical data. For example, a retailer might use AI to predict sales patterns and adjust inventory accordingly, or a healthcare organization might use AI to predict patient outcomes and adjust treatment plans accordingly.
Decision support: AI can help organizations make better decisions by providing real-time, data-driven recommendations. For example, an AI system might suggest the most effective marketing strategies based on past performance, or recommend the best course of action for a customer service representative to take when interacting with a particular customer based on live video data inputs.
Automation: AI can help organizations automate routine tasks, freeing up time for employees to focus on more strategic and creative work. For example, an AI system might be used to handle routine data entry tasks or to handle simple customer service inquiries.
What is needed for AI to be able to discover outliers and missing values?
Zainudin Nordin: In order for AI to be able to discover outliers and missing values in data, there are a few key factors that need to be in place:
Quality data: The AI system needs to be trained on high-quality data that is representative of the real-world situations it will encounter. This means that the data should be accurate, complete, and free from errors or biases.
Outlier detection algorithms: The AI system should be equipped with algorithms that are specifically designed to identify outliers and missing values in the data. These algorithms might include statistical techniques such as box plots, histograms, and scatter plots, as well as machine learning techniques such as clustering and anomaly detection.
Data visualization tools: Visualization tools can help the AI system identify patterns and trends in the data, which can be useful for detecting outliers and missing values. For example, a scatter plot might show that a particular data point is significantly different from all of the others, which could indicate an outlier.
Data cleansing tools: In order to ensure that the data is of high quality, the AI system may also be equipped with tools to clean and pre-process the data, such as tools to handle missing values, handle errors or inconsistencies, and remove duplicates.
Overall, the ability of AI to discover outliers and missing values in data depends on the quality and completeness of the data, as well as the algorithms and tools that are used to analyze it.
As video datasets increase, how does AI help especially in surveillance and security applications?
Zainudin Nordin: As video datasets continue to grow, AI can be used to help with a wide range of business applications. Here are a few examples of how AI can be used in this context:
Object detection and tracking: AI can be used to automatically detect and track objects in video footage, such as people, vehicles, and packages. This can be useful for identifying behaviour patterns, optimization opportunities as well as security applications across Retail, Healthcare, Manufacturing and live events.
Activity recognition: AI can be used to recognize specific activities in video footage, such as walking, running, lifting, picking up or climbing. This can be particularly useful in Retail environments to better understand customer behaviour as well as shopping and buying patterns.
Video summarization: AI can be used to analyze large amounts of video footage and generate summaries that highlight key events or activities. This can be useful for quickly reviewing large amounts of footage and identifying any areas of opportunity for businesses.
What are some other key applications of video AI customizable to address business challenges?
Zainudin Nordin: There are many key applications of video AI that can be customized to address specific business challenges. Today I will focus on a few examples which we are currently delivering for our clients and partners:
Marketing and advertising: Video AI can be used to analyze customer behavior and preferences, and to personalize marketing and advertising efforts accordingly. For example, an AI system might be used to analyze how customers interact with video ads, and to optimize the content and delivery of future ads based on this analysis.
Customer service: Video AI can be used to improve customer service by analyzing customer interactions and providing real-time recommendations to customer service representatives. For example, an AI system might be used to analyze customer tone and body language, and to suggest appropriate responses or actions for the representative to take.
Training and development: Video AI can be used to create personalized training and development programs by analyzing employee performance and providing personalized feedback and recommendations. For example, an AI system might be used to analyze employee interactions with customers and provide recommendations for improvement.
Supply chain management: Video AI can be used to improve supply chain efficiency by analyzing the movement of goods and materials through the supply chain, and identifying bottlenecks or inefficiencies that can be addressed.
Quality control: Video AI can be used to improve quality control by analyzing product defects and identifying trends or patterns that can be addressed.
