The digitization of processes and workflows across organizations has led to a massive amount of data that needs to be managed. Today, data sources extend beyond the traditional and include rich (and potentially vital) data captured from devices and sensors spread across the entire value chain. It’s increasingly also becoming true that data from a range of CCTV systems is being folded into this data paradigm. Of course, this data must be analyzed to extract valuable insights and drive better business decisions.

The Role of Visual Analytics in Providing Actionable Intelligence

Actionable intelligence is not just a collection of data returned by a dB query; it is, in fact, data designed to make decision-making processes more effective and accurate. In simple words, actionable intelligence is the information that helps you determine the right course of action to take in a particular situation or need. 

This information (or intelligence) that can be instantly acted upon serves well to:

• Make data-driven decisions coinciding with business flow exigencies and deadlines.
• Respond to changes appropriately and accurately, leading to optimized operations, processes, workflows, and threat management.
• Ensure personnel safety on the premises and prevent operational anomalies from escalating.
• Drive predictive maintenance and help optimize maintenance schedules.

Indeed, the value of having this kind of information is immense. But how to derive this information in the first place? And then, how to act on it? That’s precisely where visual analytics comes into play.

The analysis of visual data (sourcing from sensors and cameras installed across the facility) can help identify real-time patterns, anomalies, and issues that might influence operations within an establishment. It can then drive action plans for rectification, optimization, maintenance, and predictions. 

For example, in a quick-service restaurant (QSR) or retail store, visual analytics can help in tracking footfall, discovering hotspots, personalizing offers using facial recognition, understanding the employee behavior, etc. Such holistic, real-time visibility can help owners make decisions that can lead to more effective service delivery, reduced wait times, and improved customer satisfaction. Likewise, in an electronics assembly line, a visual analytics platform can be used to detect unauthorized handling of components or defected items and immediately alert the concerned personnel.

Challenges that Come to the Fore

While the concept of visual analytics is enticing, there are a number of hurdles that need to be overcome before the practice finds its full share in the domain of intelligence and optimization.

First off, Visual Analytics Needs Enterprise-Wide Integration

Often, not all data across an enterprise is within the framework of a single view; hence the possibility of having data silos could impede the timely delivery and accuracy of the information that is being reported. It’s important that the analytics initiatives are well tied to enterprise systems such as ERP, CRM, etc. 

Visual Analytics Must Drive Proactive Resolution

While we have cameras everywhere, and they can be used to derive a whole range of insights, the end goal of visual analytics is the creation of meaningful action plans that can be used for the proactive formulation of time-bound and effective resolutions to any issues. But that’s challenging to realize, especially when most of the establishments rely on post-mortem analysis of visuals to unearth the root of the problems that have escalated. 

Visual Analytics Must Empower Automation

The entire process of surveilling people for their safety, non-intrusively monitoring the operations, inspecting the products, and identifying any gaps and anomalies with respect to requisite quality metrics and adherence to standards, has to be automated. Modern-day facilities demand the leap from digitization to autonomous anything, and rightly so. 

All in all, visual analytics should help with the facilitation of actionable intelligence that can be leveraged to realize streamlined operations. Such operations can be a breeding ground for fostering innovation, which can translate into a competitive advantage for the business. They shouldn’t have to rely on post-mortem analysis and make a plan of action in the aftermath of an incident. But how’s that possible? 

Enter KamerAI – an intelligent, computer-vision (CV) based analytics platform.   

Realizing the Potential of Visual Analytics with a Computer Vision Platform

For visual analytics to drive insightful action, it must be powered by a robust CV platform that can help make sense of all the visual data being captured live across the enterprise. That’s what KamerAI empowers businesses to do by enabling them to employ computer vision technology powered by AI, deep learning, and artificial neural networks. For example, KamerAI helps in:

• Extracting and analyzing barcodes to evaluate products and assets
• Employing robust facial recognition technology to automate timekeeping
• Driving hard hat detection from CCTV data to advance personnel safety and ensure adherence to policies and compliances
• Monitoring how the equipment, including heavy machines, is aligned and handled across the work-floor 
• Identifying any unattended or inactive assets on the premises
• Ensuring real-time analysis of visual data captured from CCTV and alerting the security personnel on the possible perimeter breach

Such capabilities make KamerAI a powerful platform to be used across retail establishments, manufacturing facilities, logistics hubs, warehouses, and more. 

Interested in learning more? Look through these demo videos exhibiting KamerAI’s viability. Reach out to us to schedule a personalized demo!