Large Language Models Can Bridge the Analytics Skill Gap, but It’s Not Enough

By Inon Maman


LLMs can overcome the big data analytics skill barrier. But to become truly data-driven, enterprises must remove four more critical barriers to answer their biggest, most important questions

In an increasingly digital world, the ability to ask and answer critical business questions using data is paramount. Enterprises now find that their highest-value projects can be limited by the scalability of their datasets, the complexity of queries, or the time required for analysis. Previously, SQream introduced the concept of “Ask Bigger”, a phrase first uttered by a SQream customer, to encourage enterprises to challenge these limitations and break down the barriers that prevent them from fully utilizing their data.

Large Language Models (LLMs), like OpenAI’s GPT, have emerged as promising tools to help address these challenges, particularly the skill barrier. These advanced AI models can understand and generate human-like text or code, enabling analysts to interact with data in more intuitive and natural ways. However, LLMs alone are not sufficient. To realize the full value from data, enterprises must address four other crucial areas: scale, location, time, and cost.

#1: Scale Beyond Limits: Overcoming Data Size Challenges in Analytics

The sheer volume of datasets organizations collect makes it increasingly difficult to analyze them effectively. As data continues to grow exponentially, companies are faced with difficult choices: either scale down the size of datasets for analysis or scale data analytics solutions accordingly to handle the volume. Graphics Processing Units (GPUs), with their parallel processing capabilities, provide the computational power needed to handle large-scale data analysis. By harnessing the power of GPUs, enterprises can break through the scale barrier and include all data, without limits, to obtain accurate insights from their vast datasets.

#2: Breaking the Chains of Data Location: Liberating Insights from Location Constraints

The ability to access and analyze data regardless of its location is crucial for a data-driven enterprise as migrating large datasets across different locations can be time-consuming and expensive which can impede analytics projects. However, LLMs were born in an online environment (on the cloud), and enterprises need a data analytics solution that can seamlessly integrate with various data sources (on-premise, on cloud, at the edge). No matter where your data is stored, you should be able to analyze it in a single source of truth, using the relative advantages of LLMs even if you prefer a more secure and private environment on-premises.

#3: Time Lost Can Equal Opportunities Missed: Conquering the Delays in Obtaining Actionable Insights

Speed is of the essence in the lifecycle of analytics projects. Insights that are not timely can become irrelevant and fail to deliver value. LLMs can assist by writing queries faster or even optimizing existing queries, but when dealing with complex queries and large datasets, a more comprehensive solution is necessary to avoid bottlenecks in the pipeline. Also, to work well with native language queries, the next-generation data analytics architecture should be able to run any query rapidly and without the need for extensive data preparation.

#4: Balancing the Budget, Maximizing Value: Mitigating the Cost Burden of Data Analytics

Big data comes with big costs. From data storage and processing to the tools and talent required for data analysis, the expenses can quickly add up​​. While LLMs can reduce some costs by simplifying the data analysis process, enterprises must also consider the overall cost-effectiveness of their data analytics solution. A cost-efficient solution should be able to deliver valuable insights that justify its costs and provide a strong return on investment for your business. 


Removing Barriers to Asking Bigger Questions: Empowering Enterprises in the Data Analytics Journey

While LLMs offer an exciting new way to engage with data and can help bridge the skill gap, they’re just one piece of the puzzle. To truly “Ask Bigger,” enterprises must consider a comprehensive data analytics solution that addresses all the barriers that prevent them from fully harnessing the power of their data. 

This is what we strive for at SQream – helping enterprises dig deeper, go faster, and reach anywhere, breaking down the barriers of scale, location, time, cost, and skillset​1​. By doing so, we empower our customers to ask the big questions and get the answers they need to drive their business forward.