How to Manage and Optimize Unstructured Data

Organizations optimize unstructured data by automating collection and centralizing storage. Applying artificial intelligence to analyze formats like video and text transforms hidden information into actionable insights. This approach improves safety, reduces risks, and streamlines operations.

In this article:

• Centralizing information in a data lake and applying natural language processing helps teams extract value from text-heavy sources.
• Access the full whitepaper linked at the bottom of this page to explore comprehensive strategies for addressing digitalization challenges.
• Protex AI software integrates directly with existing CCTV infrastructure to provide continuous autonomous risk detection.
• Automation tools like optical character recognition extract details from forms to simplify manual workflows.
• Analyzing unstructured video footage empowers safety professionals to make proactive decisions that reduce workplace incidents.

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Why Unstructured Data Is a Growing Challenge for Insurers

The surge in available data due to digitalization poses a challenge for organizations. A Cloud Security Alliance study cites Gartner estimates that unstructured data accounts for 70% to 90% of enterprise data, while also finding persistent visibility and governance gaps.

Companies grapple with organizing data from diverse sources, including online portals, social media, eApps, and emails. Insurers, too, face these challenges but stand to gain significantly from organized data.

Efficient organization frees up resources, enhances security, and provides insights for optimizing product design, marketing, sales, and underwriting.

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How Does Automating Data Collection Improve Data Quality?

Effective data collection strategies continually analyze available data sources and identify inefficiencies. Automation allows a consistent structure to be applied to the captured data.

This provides insurers with a wealth of data primed to identify and solve business challenges, including customer satisfaction, safety risk reduction, simplified underwriting, and more.

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Data Quality at the Point of Capture

Ensuring data quality at the point of capture is equally critical, without proper preprocessing and curation, even the most sophisticated automation tools can produce unreliable outputs.

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Automation Tools Worth Considering

Many manual processes can instead be handled by automation tools, including:

• OCR technology (Optical Character Recognition) to extract personal data from forms
• CRM (Customer Relationship Management) systems seamlessly connecting and capturing data from web forms and customer communications
• AI tools to collect and analyze data from photo, video, and audio formats, with video content analysis helping teams structure recorded footage
• Natural language processing (NLP) to extract meaning from text-heavy sources such as emails, social media posts, and customer communications through techniques like entity extraction and sentiment analysis

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What Does Centralized Data Organization Actually Require?

While efficient data collection is important, data centralization is imperative to reap the full benefits. The reality, however, for most insurers is that their data is spread across multiple systems, in multiple formats, making it difficult to pull aggregated insights from all available data sources.

Consolidating these assets into a data lake or centralized repository helps break down data silos and makes information retrieval far more manageable. A well-maintained data catalog further supports this effort, enabling teams to discover, index, and manage data assets through consistent metadata management.

Underpinning all of this is a sound data governance framework, one that enforces access controls, maintains data lineage, and ensures ongoing regulatory compliance.

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Cloud Solutions and Their Trade-offs

There have been technological advancements in this space; however, each comes with its own obstacles. Cloud solutions have helped tackle unstructured data challenges, but can also create new data security concerns.

Data integration solutions are helping, but they can struggle to effectively structure data across multiple systems. Until a comprehensive solution is found, insurers need to take a more focused approach and instead concentrate on improving a specific aspect of their data landscape.

With the right mix of tools, insurers can begin to more efficiently and effectively solve specific business challenges.

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Protex AI and Marks and Spencer – A Real-World Case Study

Manufacturing facilities and warehouses often have CCTV cameras in place for real-time human monitoring. Among safety professionals, it's recognized that the utilization of CCTV footage to gain insight into unsafe worker behaviors is low.

Protex AI, a workplace safety software company based in Dublin, Ireland, saw the collected video footage as an underutilized resource. Using artificial intelligence (AI) to analyze unstructured CCTV footage, Protex AI's privacy-preserving platform plugs into existing CCTV infrastructure and uses its computer vision technologies to capture unsafe incidents autonomously.

Having 24/7 risk detection enables safety professionals to gain greater visibility of risk across their facilities, so they can make proactive, data-informed safety decisions.

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Reporting, Dashboards, and Data Visualization

Protex AI's in-depth reporting and EHS analytics functionality, including storyboards and operational dashboards, allows clients to gain insights into trends of unsafe behaviors and collaborate on learnings to instigate effective corrective actions and training programs.

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Incident Reduction and Safety Outcomes

In one installation, Protex AI's client, Marks and Spencer, a major British multinational retailer with over 80,000 employees globally, saw an 80% reduction in incidents within the first 10 weeks of deployment and a positive shift in employees' perception of their company's care for their safety.

Much of this reduction was attributed to the efforts of their safety team to quickly identify areas of risk in their distribution centers and implement impactful safety programs amongst their workers. Within the U.S., employers spend nearly 1% of their monthly payroll on workers' comp benefits, a significant cost much attributed to the safety risks present in many workplaces.

Protex AI is an example of a tool that insurers can leverage in partnership with employers to better identify and reduce such risks. Historically, unstructured video data was difficult to effectively monitor, but with Protex AI, insurers can now see the full workplace risk picture.

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Join the Digitalization Conversation

Explore the full whitepaper for comprehensive insights into addressing challenges in insurance digitalization.

As part of the Insuretechs Collective, Protex AI shares practical ways to turn insurance digitalization challenges into clearer safety decisions.

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