Mastering the Chaos

How to Use LLM to Manage Unstandardized Data?

Data is a vital resource; leveraging it effectively is crucial in any industry in this information era. From deriving insights to identifying anomalies, proper procurement and processing of data might provide valuable inferences into the functioning and operation of any industry and differentiate your product from the competition.

However, the large volume of data generated within hi-tech industries, the multiple tools, and the non-standardized practices followed by several teams and engineers pose a significant challenge. The volume and non-standardized nature of the data results in costly errors & inefficiencies.

Engineers find it strenuous to work on large volumes of data, work with different tools, or adopt new practices and standards quickly & efficiently. Moreover, it can also be arduous to develop analysis software for unstructured data. For example, engineers must work on the data collected in 20 different file formats and then create software that can comprehend 20 different file formats, making it nearly impossible when working with larger, dynamic & increasing datasets.

Understanding the adversities of unstandardized data, it is no wonder big businesses are investing sums of money to standardize the data. So, is the notion of unstandardized data truly chaos? And, with the majority of the data being unstandardized, is there no way to utilize it or draw insights from it?


Can Standardization be the Answer?

To prevent unstandardized data from causing chaos, most businesses seek out ways and techniques to standardize their data.

Standardization transforms the acquired data into a single, consistent format that can be read and interpreted manually with a little effort and effortlessly with software. One idea is to store all the data in a single system and map them into a standard structure, making it easy for engineers to acquire data across multiple systems and correlate them.

Standardization encourages & enables reusability in test and measurement and helps simplify the test measurement process. Standardization is essential in cases where data is stored in different systems and managing the data transformation effectively is a necessity. So, it is no wonder that massive investments are made in developing enterprise-scale applications/tools that standardize data and structure the data into standard conventions.

Since most data being acquired is unstructured, an enterprise-scale standardization initiative is a challenging task involving a large investment of cost, effort, and time, and one tool/infrastructure might not be enough to standardize every data. It makes one wonder if standardization is the only solution or the right solution to derive insights, and if it is worth the investment.


LLMs can be a Potential Alternative to Master the Chaos

As processing unstandardized data is arduous and standardization involves heavy investments, embracing an alternative like AI can act as a co-pilot in both environments.

These LLMs and AI models can be ideal tools for helping you make sense of unstructured and partially structured data, owing to their ability to comprehend similarities between multiple entities, map them, and extract data and insights from them. In a standardized environment, they aid in mapping the data, eliminating unambiguous and disparate data across systems for better interpretation and simplified analysis.

If systems that can effectively work with both standardized & non-standardized data can be made possible, one needs to critically question the need for investing in standardization software.


How LLMs Help Contextualize the Unstandardized Data and Derive Insights from Them: A Real-time Example

Consider an example use case where the user seeks to extract data from a database, apply the custom algorithm to identify anomalies pre-set in the data, and identify root causes for these anomalies. This use case involves several complex tasks, requiring engineers to work with different tools and infrastructure, and is likely to take up considerable time to accomplish.

Leveraging the power of LLMs to accomplish this task as an alternative offers a powerful solution for solving the multiple complex tasks involved above and simplifying the entire experience for the organization/end user.

LLM providing answer for a complex taskLLM providing answer for a complex task

Pondering Over the Question

What we have described above is just one possibility – using artificial intelligence with structured and unstructured data can open doors to addressing more profound scenarios and solving complex use cases. It is simply a matter of exploring & identifying the right problems to apply the power of AI & LLMs to and reap its benefits.

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