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Unstructured Data: Elevating Your Business With One of Its Most Valuable Hidden Resources

Jitin Agarwal

VP, Enterprise Products, EPAM US
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  • Financial Services
  • Retail & Distribution
  • Life Sciences & Healthcare

When thinking about company data, picture an iceberg. Above the water, you only see the tip of the mass. This represents structured data, which typically accounts for 20 percent of all enterprise data. Underneath the surface lies the bulk of the berg, and one of a company’s most underutilized resources – unstructured data, usually amounting to 80 percent. Generally, organizations focus on mining structured data, as it is readily visible and accessible. On the other hand, unstructured data is a bit trickier.

While it ought to be a priority for organizations, unstructured data by nature is incredibly diverse, difficult to search and navigate, or even challenging to make sense of. The answer to this problem has existed for some time now, but is becoming more acute as the scale and type of unstructured content grows in enterprises today. Text analytics and sentiment analysis tools offer a one-two punch, enabling organizations to assign depth, meaning and intelligence to their unstructured data. With that, there are several applications that speak volumes to the advantages of text analytics and sentiment analysis tools.

One notable way that companies apply these technologies to their business strategy includes taking a hybrid approach to Enterprise search. As text analytics and sentiment analysis technologies continue advancing in capability, many companies are looking to gather and dissect a combination of relevant structured and unstructured data, otherwise known as big data. By converting structured data queries so that they can run against unstructured data sets, companies can unearth powerful insights usually reserved for the most sophisticated big data tools.

As an example, the amount of data – both structured and unstructured – within research can be daunting to manage. On top of producing loads of proprietary data, R&D teams rely on publically available research articles, presentations, and more to save on time and costs. Enterprise search tools allow these teams to leverage text analytics and access this information in one place, anytime and anywhere, adding business value to the entire R&D lifecycle.

Another distinct use case spans across many industries and addresses a major headache. In an ever-changing and highly complex regulatory regime, organizations can benefit from utilizing these tools to achieve and maintain compliance. While compliance can seem like a costly endeavor, non-compliance can cost far more. According to a study, “meeting compliance standards costs around $5.47 million for a company, while non-compliance costs including fines, business disruption and losses in productivity and revenue” comes to nearly $15 million.

With these numbers in mind, the ROI on regulatory compliance monitoring through text analytics can be huge! Companies already have to juggle many moving parts to effectively mitigate risk and manage operations within the current environment, so why not reduce the effort to comply by implementing text analytics software? In particular, artificial intelligence tools relying on Natural Language Processing (NLP) technology used alongside text analytics software can sort through unstructured data, such as customer-facing emails and PDF documents, and interpret human language for compliance screenings.

A final example involves improving customer experience. In the age of personalization and experience, the customer is king. But how can organizations ensure clarity around consumers’ priorities and behaviors? This challenge is especially acute with all of the social media platforms prevalent today. For example, it’s not enough to just be aware of social communications occurring in the market. Broad-based social listening and monitoring tools are fairly ubiquitous today, so the market is moving toward more advanced social media capabilities. Let’s call it social listening 2.0.

By utilizing sentiment analysis to determine positive or negative intent and leveraging text analytics to serve up succinct insights, organizations can remove the tedious, manual work from everyday social media monitoring processes and enable their organization to rapidly respond to evolving social media trends. Together, these tools comprise the full suite of social listening 2.0 and will play a huge role in transforming customer experiences. Ultimately, deploying Social Listening 2.0 to not only monitor customer sentiment online but proactively respond to it as well, will lead companies to new levels of market success and delighted customers.

It truly is a matter of survival of the fittest, regardless of industry. Even companies with 100-year-old histories are subject to fail if they’re not properly equipped with the right technology. To withstand and emerge successful in an increasingly competitive landscape, businesses must be prepared to turn their unstructured data into actionable insights. Once enterprises unlock this untapped data, a deluge of opportunities will flood their organization. Text analytics and sentiment analysis will enable organizations to filter and parse through content at scale. In a broader sense, companies that are armed with a better, firmer understanding of market directions will be well-positioned when it comes to identifying and adapting to any emerging market disruptions. 


Jitin Agarwal serves as VP, Enterprise Products at EPAM. With 20+ years of experience working in and leading ventures, Mr. Agarwal has amassed a wealth of expertise across the entire lifecycle of a venture. He’s currently spearheading the product and version 7.0 launch of InfoNgen, a text analytics and sentiment analysis tool.

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