How Enterprises Can Extract Meaningful Insights from Unstructured Data

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In this special guest feature, Eran Shlomo, Co-Founder and CEO of Dataloop, asks why unstructured data is so critical to building better AI – and how can organizations optimize their management of this data? Eran has held key positions in Intel’s processor division in the 13 recent years and has global expertise in computing and information processing architecture, and was responsible, among other things, for the development and implementation of machine-learning technologies in every Intel lab in the world. In addition, in recent years Eran was the technological leader of the cooperation program with Intel Israel startups (IPP), and was part of its founders. Eran also has rich entrepreneurial experience, and Dataloop is his third start-up. Eran holds a BSc degree in Computer Engineering from the Faculty of Engineering at the Technion.

As enterprises increasingly turn to artificial intelligence to unlock new business value and streamline their operations, the global AI market is slated to surge from $4.07 billion in 2016 to $169.4 billion by 2025.

But even as more organizations deploy AI, a staggering 96% of enterprises are struggling with data management, with many companies encountering issues with the overall quality and labeling of their data. Given that AI models are only as good as the data upon which they’re built, this is a serious challenge confronting both the fast-growing AI market and the diverse array of industries hoping to capitalize on AI technology.

For businesses to extract meaningful insights from AI, there must be a constant data feedback loop, with AI continuously improved while it is in production as it is exposed to new data and new edge cases and anomalies are revealed. In simple terms, AI systems  work well given past data but there is a need to constantly update the system with future data in mind.

Why is unstructured data so critical to building better AI – and how can organizations optimize their management of this data?

Here is how combining the best of human intelligence and cutting-edge technology can help enterprises clear their biggest data-related hurdles.

Understanding Unstructured Data

The sheer proliferation of digital services and applications has generated reams of unstructured data, which now accounts for up to 90% of all digital data. Harnessing this data to build cohesive, unified data sets is imperative for enterprises to gain a more accurate understanding of all the business information at their disposal – but as many organizations have found, this is easier said than done.

In contrast to structured data, unstructured data based on examining individual pieces of data to identify key features and patterns. Manually performing this task is highly time-consuming and resource-intensive – which is why AI tools are vital for helping enterprises properly manage their unstructured data and derive real value from it.

In the retail industry, for example, where more businesses are beginning to implement cashier-less checkout and AI-based solutions for monitoring product recognition and inventory management, stores need dynamic tools that enable their AI systems to keep pace with new product categories as well as new consumer behaviors like mask-wearing.

Or consider the healthcare field. Clinics and other medical facilities store vast amounts of patient data that can prove highly potent in improving detection and diagnostic tools for a variety of conditions. Computer vision technology can provide clinicians with more granular, accurate details on patient scans, for example, but the models undergirding these solutions must be sensitive to a range of relevant factors, including demographic and geographic characteristics.

While getting the most out of AI requires meticulous groundwork and analysis of disparate data sources, the good news is that there are tools available for helping organizations create order out of their unstructured data.

Optimizing Unstructured Data

Among the AI-based solutions for making sense of unstructured data are pattern recognition algorithms, which leverage machine learning to categorize unstructured data. For instance, these algorithms can quickly tag and categorize large quantities of images, a process that would take many hours if performed manually.

While full automation analysis is the ideal solution it is still beyond the reach of today’s AI, therefore a hybrid approach  to management and training is required to break down silos and build data hubs that store, unify, and deliver data effectively with high level of automation.  Fusing machine and human processes yield better AI models, allowing human moderators to correct for biases and prevent the degradation of models. Accordingly, businesses should seek out tools that combine the best of human and machine capabilities. Relying exclusively on humans is cumbersome and inefficient; putting all the onus on machines, meanwhile, means that models are effectively on auto-pilot, without sufficient input and expertise from the end user.

Overwhelming as unstructured data can seem, AI and humans working together can streamline this data and make it much easier to extract insights across multiple applications.

AI is poised to help generate billions in value across industries – and if it is to be as impactful as possible, it must combine both sophisticated technology and domain-specific expertise. Working together, humans and machines can transform unstructured data into vital intelligence, delivered as domain specific expert system.

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