When Data Collection Delivers Too Much of a Good Thing 

When Data Collection Delivers Too Much of a Good Thing

Keen AI’s new KAI platform uses Artificial Intelligence (AI) and machine learning to condense, store and compare vast amounts of visual data; delivering fast analysis and valuable trend prediction for large scale asset networks. 

Many companies are awash with visual data; some they actively gather; much else arrives as a by-product of other operations.

Readily available technology is increasingly used to collect as much visual data as possible but unfortunately, there is a global shortage of skilled engineers with the knowledge to review and interpret this data, not to mention turning it into actionable information. 

Improved ability to collect information may only exacerbate the situation, as typically, more and more data ends up being passed through the same number of people. Not surprisingly, the result is often a larger backlog of data to review – and overworked staff. 

Amjad Karim of Keen AI said, “The problem with managing this volume of information tends to be twofold. Firstly, how do you process all this data, when the capacity for human analysis tends to be limited, slow and highly expensive? 

“Secondly, how do you store and arrange this volume of data in a way that lets us learn from it; utilising its full power to reveal or predict changing conditions over time? Very often, this simply means that companies don’t maximise the return on data they so expensively collect.

“Clearly, collection is a good thing but when faced with vast volumes of data, organisations need to be more selective about where they deploy their attention”.  

Keen AI’s ground-breaking work with AI and machine learning provides a technical solution to that practical need for a selective attention mechanism. 

The company was initially born through working on risk assessment for National Grid in 2018; carrying out condition assessment of overhead line assets.  This required the team to develop a reliable model to identify components from video footage. This also reduced the condition assessment process time by 66% by filtering out images where there isn’t anything of interest and drawing attention to potential defects.

The KAI platform for National Grid was initially deployed in just two months.  Mark Simmons, Monitoring Team Leader at National Grid added, “Any fears that developing the system would be heavy on either cost or reliance on our internal computing teams were quickly swept away. 

“The KAI system was a game-changer in assessing risk and improving the speed of assessment but it is possibly the machine-learning element which is the most exciting; we are able to get a digital overview of the network in a way that has never before been possible.” 

Other applications range across many areas; including maintenance efficiency, asset monitoring and maintenance, plus ecological study. The KAI platform works to cost-effectively identify and analyse patterns, and to identify ongoing patterns of potential defects. 

Large scale asset networks require huge amounts of oversight to maintain optimal safety and service levels. By using AI, the amount of data that can be processed on a continuous basis increases exponentially, while costs are simultaneously reduced.

Karim said, “In the real world, the main role for those involved in AI and machine learning is to solve highly specific issues in a more efficient and cost-effective way. This way of working is also highly aligned with regulators’ current digitisation agenda; to use and store data more efficiently.”  

The company is already working with several large energy utilities as well as delivering ecological survey analysis for transport and logistics.  

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