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Real world examples: How AI can help you get a handle of unstructured and big data

Iron Mountain

Real world examples: How AI can help you get a handle of unstructured and big data

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Big data can be a double edged sword; it can be your biggest asset but also your biggest challenge.

The volume of data, unstructured data, siloed data, data storage costs, and navigating compliance and privacy regulations are all challenges that can come with big data.

The root of all these challenges, however, is the sheer amount of data and its unstructured nature. While you could manually format, clean and structure your datasets to a certain size, this process is not only time consuming and expensive but also creates the opportunity for human fallibility. 

This is where new and emerging technologies, like artificial intelligence (AI), can help. In fact, AI can help you get a handle on your data and extract value no matter what industry you’re in. AI can be applied to convert virtually any data that a business is gathering or storing into valuable, actionable insights. 

Every Industry Can Benefit From AI

For every type of unstructured data a business in any industry can generate, there are AI tools that can mine it and extract insights. Here are just a few examples of how various businesses in various industries have benefited from the use of AI.

Energy

In the oil and gas industry vast amounts of data are generated throughout the entire process. These data sets include geological samples, seismic images, well logs, and lithographic surveys. AI is key to accurately assessing these data sets and realizing the value of wells and the companies that operate them.

Here are some examples of how AI helped two energy companies extract value from their information:

  • An oil company was seeking to identify potential opportunities for acquisition. However, in order to do so they needed to scan and extract information from 15,000 documents relating to 500 wells. If done manually, it would have taken them several months to complete and even then there would have been no guarantee that all of the data would be evaluated. Ultimately, AI helped this company not only thoroughly evaluate all the information but did so in less time.

  • A multinational power infrastructure provider needed to identify, and classify, sensitive records detailing construction of facilities. However, the pertinent data resided somewhere across five premises. With computer vision and natural language processing the relevant information was located and could be securely stored or disposed of as appropriate.

Mortgage

The task of approving a mortgage requires the completion of a sizable amount of paperwork by both the applicant and the lender. This means the whole process can take months. When this information is collated and classified manually not only is it incredibly time consuming, but any errors can have serious financial implications. 

Automating the classification, extraction and metadata validation techniques can lead to reduced cost, faster approval times, improved customer satisfaction, and more accurate risk profiling and fraud detection. With automation, the mortgage approval process can take a fraction of the time it takes when done manually.

Pharmaceuticals

Pharmaceutical companies have to gather massive amounts of information in order to create and trial new drugs. Furthermore, there is no standard format for recording this data meaning researchers have to work across and sift through data recorded in a myriad of forms by different corporations and government bodies. This is time consuming work to say the least.

Machine learning can help pharmaceutical companies quickly classify data stored in any format and retabulate data in the required format. This makes the data ready for analysis and review quickly which then leads to improved patient outcomes as well as the development of drugs and treatments in a timely and more accurate manner.

Media & Entertainment

Protecting assets by guarding against copyright violations and other infringements of intellectual property is crucial for media companies. Since just about anyone in the world can upload and distribute content over the Internet, this is a challenging task. 

With the help of artificial intelligence (AI), any dataset, no matter how large, can be analyzed and also automatically identify copyright breaches. This, in turn, means artists and creators have a fairer chance of getting paid for their work.

Conclusion

No matter the industry, everyone can benefit from AI and machine learning. Eliminate the manual slog of sifting through data and avoid potentially costly mistakes. With AI and machine learning, data stored in just about any format can be quickly classified and retabulated into any format, making it ready for analysis and review in a timely manner.

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