Unstructured data, such as text, images, and videos, increasingly dominates the digital landscape, making up over 90% of all data. This type of data is challenging to analyze due to its unorganized nature, but it offers valuable insights into customer behavior, preferences, and emotions.

Organizations are particularly looking at unstructured data in 2023 because of the advent of generative AI. They are realizing how large language models (LLMs) can be leveraged to put unstructured data into use better than what was possible before.

 

The Resurgence of Unstructured Data in the Age of Generative AI and LLMs

Generative AI and LLMs, in particular, are playing a key role in reviving organizations’ interest in unstructured data. They hold immense potential in parsing, interpreting, and generating human-like text, images, videos, etc.

LLMs like BERT, RoBERTa, and GPT models decode language intricacies and provide context to unstructured data. This is instrumental in converting unstructured data into actionable insights to foster data-driven decisions.

Also, it’s not just what generative AI can do during data aggregation and analysis; it’s also about how the relevant information can be put to use for, say:

  • Personalizing and customizing interactions
  • Summarizing key correlations for predictive analytics
  • Trimming down the expenses incurred in the data-to-insight-to-action trajectory

 

What Benefits Can Enterprises Enjoy from Unstructured Data?

With generative AI driving the use of unstructured data, enterprises can enjoy the following benefits:

  • Easier and cheaper storage: Unstructured data is easier to store because it’s in its native format. Plus, it can be housed in data lakes, which are cheaper than other storage options.
  • More granular information: Unstructured data provides more granular information about the subject at hand.
  • Reduced cost and increased productivity: Using generative AI to make sense of unstructured data can reduce cost and bolster productivity — on the back of end-to-end automation of data analysis.
  • Fraud detection and risk management: Information obtained from unstructured data can help with sophisticated applications like fraud detection and adherence to compliance.

 

Challenges and Complexities of Working with Unstructured Data

Regardless of the many benefits of inclining toward the use of unstructured data, there are a number of caveats related to the effective use of this data type.

 

The Nature of Unstructured Data

Unstructured data does not have a predefined structure. It includes text, images, videos, audio, and other types of media that do not fit neatly into rows and columns.

This data is often characterized by its variety, velocity, and volume. It can come from a wide range of sources, such as social media, customer interactions, sensor data, and medical records. It is also generated at a rapid pace, and it can be challenging to keep up with the inflow of data.

Handling and Extracting Value

One of the biggest challenges of working with unstructured data is extracting value from it. This is because unstructured data is often difficult to search, analyze, and visualize.

While structured data is typically stored in databases with predefined categories and labels, unstructured data does not have a predefined structure.

Data Processing Lagging Behind Growing Data Size

In the contemporary landscape, enterprises are amassing vast volumes of data. It is anticipated that the global data volume will surge to 180 Zettabytes by 2025 — posing the complex challenge of efficiently and promptly capturing this information.

Data processing needs to catch up with the growth of data. A gap here can result in enterprises struggling to make relevant decisions.

Data Privacy and Security Concerns

Unstructured data often contains sensitive information about customers, employees, and other stakeholders. It is essential to have strong data security measures in place to protect this data from unauthorized access and misuse. Otherwise, businesses could face serious consequences, such as financial losses, reputational damage, and regulatory fines.

According to Egnyte’s 2021 Data Governance Trends Report, uncontrolled data expansion and disarray elevate cybersecurity risks. This is especially valid for unstructured data, which is more susceptible to mishandling and often stored within isolated data systems.

Even generative AI isn’t spared from security concerns. Organizations need to be cognizant of:

  • Securely training models on sensitive data
  • Security of the third-party storage spaces
  • The difference between real and synthetic data

 

Best Practices for Leveraging Unstructured Data

The following best practices and tips can help enterprises to effectively leverage unstructured data.

  • Organizations must establish a data governance framework that keeps tabs on the consistency and integrity of data. They can leverage generative AI to ensure data quality (by unearthing errors, redundancies, etc.) and streamline the compliance process (by training models on evolving regulations).
  • They must look to invest in adequate data storage solutions that are both scalable and cost-effective. NoSQL databases, for instance, are often preferred for such storage. Organizations can look to cloud offerings from Azure and AWS to ensure flexible storage.
  • Organizations must also invest in ethically training the generative AI models to ensure that they comprehend real-world scenarios without bias. There should also be a framework in place to keep tabs on the model’s performance over time.

 

At Trinus, we help organizations overcome the aforementioned challenges through such best practices. With our expertise in data integration, data warehousing, big data and data lakes, MDM/PIM, data modeling, data governance, etc., we ensure that enterprises successfully leverage the potential of unstructured data. Book a strategy call today to learn more.