Utilizing Cutting-Edge Unstructured Data Analytics to Solve Today’s Business Challenges

• Data Analytics

As technological advances provide more efficient ways to connect and personalize products and services, the expectations and demands of customers are increasing. They want even more personalized products with an even faster turnaround time.

Now, companies are tasked with finding new ways to make smarter and faster decisions, and obtain superior insights into customer needs, behaviors and profitability. In order to remain competitive, agile and innovative, organizations must make analytics an integral part of their strategic planning, investing time and talent to explore the best possible ways to utilize data. Among the biggest challenges: how to harness unstructured data – such as call center logs, blogs and social media posts – to overcome modern business challenges and increase revenues.

New Kid on the Block Shows Incredible Promise

At a macro level, unstructured data encompasses any data that cannot be contained in a database or fit within a pre-defined data structure. Until recent years, many didn’t think it was possible to fully utilize the unstructured aspect of Big Data. They didn’t think analytics solutions existed to extract meaningful data from unstructured text, audio and video, nor did most companies have the infrastructure or capabilities to experiment with unstructured data.

Today’s market is much different. The surge of digital and social media enables companies to glean a 360-degree view of their customers, and now the ability to utilize unstructured data in a reasonable and realistic manner is within reach. On average, 70-80% of an organization’s data consists of unstructured data. If analyzed correctly, this volume of data can contribute to about 20-30% of total insights, enabling companies to far better understand customer mindsets, purchase preferences and sentiments than they could with structured data alone.

Read More: Advanced analytics to derive actionable insights.

Emerging Solutions Give Meaning to Unstructured Data

While there is no shortage of unstructured data, the challenge comes in accessing that data and transforming it into something that is usable and actionable. The following technological advancements are helping companies converge structured and unstructured data to overcome modern business barriers:

Text Mining and Neuro-Linguistic Programming (NLP)

Text Mining and NLP help companies tap unstructured data to analyze customer feedback, such as emails, blog posts, customer comments and queries, to extract meaningful insights. For instance, these capabilities have the power to:

• Managing real-time data feeds
• Convert unstructured text into structured data, telling you “who” is being discussed, “what” is the context of the conversation, and whether that feedback is positive or negative
• Reduce manual efforts and turnaround time significantly
• Scale solutions to continually manage voluminous data

Artificial Intelligence

AI has been used with research methodologies in medicine, robot control, defense and remote sensing industries. One key distinction is that AI diagnostic methods place an emphasis on the algorithm, as opposed to standard predictive analytics techniques, and are able to predict business scenarios and trends by leveraging historic data.

Machine Learning

Machine learning allows rapid processing of large amounts of customer-centric data, including customer conversations in the form of calls, email and chat. It has ability to handle huge volumes of data, enabling companies to incorporate multiple data streams without compromising the accuracy or relevance of the results. For instance, companies are using machine learning to build a single view of customers to develop the next best offer or predict churn.

Sentiment Analytics

By tapping into the world of social media and combining it with cutting-edge algorithms such as NLP, analysts are able to decode complex human interactions and evolving languages to understand what customers are really saying. For instance, movie makers can use algorithms and computational models to learn and gain knowledge about their audience to script a movie that will have maximum impact.

Related: Sentiment analysis from text

High-Performance Computing

As unstructured data consists of nearly 80% of a company’s total data, the boundaries between unstructured data analytics and high-speed computing powers continue to blur. High-performance computing has the ability to handle huge volumes of data almost on a real-time basis, hence transforming the way analysis used to happen. The large volume of unstructured data combined with its ever-increasing velocity demands that companies move away from traditional computing systems. Further, as the algorithms become more and more complex in nature, their execution requires higher computing power to sustain and be effective.

What’s Next for Unstructured Data Analytics?

Recognizing the critical benefits of unstructured data, companies are ready to invest in research and development to solve complex challenges. The opportunity cost of ignoring unstructured data is now too high in this globally competitive environment.

Technology advancements in unstructured data analytics are helping companies across industries understand customer DNA, making sales agents more efficient and enabling maximum returns from marketing activities.

Statistics and data management have served as two prominent pillars of analytics. Yet with the increased computing power and improvised techniques, unstructured data is quickly emerging as the third pillar. We’ve only seen the tip of the unstructured data iceberg. Innovative companies with the foresight and desire to dig deeper have the potential to change the way we live.

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