On the surface, aluminum and data don’t seem to have much in common. But when you dig deeper, there are some striking similarities that reveal how overlooking unstructured data is akin to passing by a valuable rock without a second thought.

Aluminum has become essential to modern life, from aircraft to electronics. Yet in raw form as bauxite ore, it looks like any ordinary rock. The immense value of aluminum only became clear after refining techniques were developed to process the ore.

The same holds true for the unstructured data produced in most organizations today – from images and videos to call logs and chat transcripts. This data often gets buried in silos or excluded from analytics. But when labeled, refined, and integrated, unstructured data unlocks powerful business insights.

The Allure of Structured Data

Structured data in neat rows and columns has dominated business intelligence and analytics. It can efficiently answer retrospective questions about sales, costs, production levels and more. But structured data alone can’t reveal the reasons behind the numbers.

Unstructured data represents real-time, experiential information. An AI analysis of customer calls, for example, could uncover pain points dragging down satisfaction scores. News sentiment analysis may explain an unexpected sales spike. Production equipment sensor data can predict maintenance needs.

These forward-looking insights are only possible by incorporating unstructured data into analytics. Yet while most companies capture this data, few actually use it.

Overlooked and Underutilized

Unstructured data remains an untapped asset as it requires preprocessing before use. Data must be transcribed, labeled, and annotated – a task with no clear owner in most organizations.

By default, this unglamorous job falls to data engineers. But manual labeling is time intensive and pulls them away from other critical projects. Many resort to imperfect workarounds instead.

Some purchase pre-labeled data, but it lacks company-specific context. Others use AI to automate labeling, but this synthetic data can’t match real-world nuance. And some skip labeling entirely, which restricts analysis capabilities.

But accurately annotating unstructured data and integrating it with existing structured data delivers a competitive advantage. The key is dedicating the proper resources.

Refining Raw Data into Value

Just like raw bauxite requires refinement to produce aluminum, unstructured data needs transformation to unlock its potential.preprocess.

Dedicated data labelers should manage this effort – either in-house experts or outsourced specialists with domain experience. They can carefully extract entities, sentiments, keywords and other metadata to prepare unstructured data for downstream analytics.

With refined, integrated data, predictive analytics, natural language processing, and other AI techniques can reveal deep insights not visible in structured data alone.

Unstructured data can seem uninteresting and low-value at first glance, but its immense potential becomes clear once refined. Forward-looking organizations don’t leave this raw data sitting untouched – they actively transform it to stay ahead.

With accurate labeling and integration, unstructured data combines with structured data to provide a comprehensive view of business operations and a competitive edge other companies will envy. Don’t overlook your unlabeled data. Recognize its true potential early on to outpace your competition.