How large language models are transforming data teams
Business Intelligence
Jun 9, 2025
Large language models are revolutionizing data teams by automating tasks, enhancing collaboration, and streamlining data analysis, driving efficiency and insights.
Large language models (LLMs) like GPT are changing how data teams work by automating tasks, improving collaboration, and simplifying data analysis. Here’s what you need to know:
Faster Workflows: LLMs automate repetitive tasks like cleaning data, writing SQL queries, and fixing code errors.
Better Collaboration: Tools like QueryGPT let non-technical users ask questions in plain English, bridging gaps between business and technical teams.
Improved Decision-Making: LLMs uncover patterns and generate insights from both structured and unstructured data, helping teams make informed decisions.
Cost and Time Savings: For example, Microsoft Copilot reduced a company’s sales summarization time from 4 hours to 15 minutes, saving $50 million annually.
How To 10x Your Data Team's Productivity With LLM-Assisted Coding
What Large Language Models Are and How They Work
Large language models (LLMs) are advanced deep learning algorithms designed to handle natural language processing (NLP) tasks [3]. These models are essentially neural networks - systems inspired by the human brain - that can interpret, process, and generate text that feels human-written by learning from vast amounts of written data [3].
LLMs are incredibly versatile, capable of tackling tasks like writing code, summarizing information, or analyzing datasets, making them valuable tools for data teams [3].
The backbone of modern LLMs lies in transformer models, which include an encoder and decoder working in tandem [3]. Unlike older systems that processed text one piece at a time, transformers can analyze entire sequences of text all at once. This makes them faster and more efficient [4].
How LLMs Process and Create Text
Training LLMs involves exposing them to trillions of words, enabling them to grasp grammar, context, and meaning by predicting the next word in a sequence [3]. For instance, if prompted with "The weather today is very", the model might predict "sunny", "cold", or "pleasant" based on what it has learned.
This training process relies on unsupervised learning, where the model teaches itself by predicting subsequent words in sentences [3].
A key aspect of LLMs is their use of word embeddings, which represent words as multidimensional vectors. Words with similar meanings, like "car" and "automobile", are placed close together in this mathematical space, helping the model understand their relationship [4].
The scale of these models is staggering. OpenAI's GPT-3, for example, has 175 billion parameters, while AI21 Labs' Jurassic-1 model boasts 178 billion parameters [4]. These parameters act as adjustable settings that the model refines during training to improve accuracy.
Modern LLMs also use self-attention mechanisms, allowing them to consider the full context of a sentence when making predictions [3]. For example, they can differentiate between "river bank" and "savings bank" based on the surrounding words.
These capabilities make LLMs powerful tools for tasks like summarizing data, detecting anomalies, and more.
Why LLMs Work Well for Data Tasks
LLMs bring technical strengths that can simplify data workflows and enhance decision-making for businesses. They can answer questions, generate reports, and identify patterns in large datasets, making data more accessible to both technical and non-technical teams [4][5][8].
"Large language models are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages and completing sentences." - AWS [4]
LLMs excel at spotting trends, patterns, and anomalies in data, offering valuable insights into operations, customer behavior, and market shifts [5]. Instead of requiring expertise in SQL or statistical analysis, team members can simply ask questions in plain English and get clear, actionable answers.
Another strength of LLMs is their ability to automate repetitive tasks and streamline workflows [5]. By extracting insights from data automatically, they free up data professionals to focus on more strategic, high-value work.
The growing investment in LLM technology highlights their effectiveness. In fact, 77% of organizations using natural language processing plan to increase their spending on LLMs by 2024 [6]. Additionally, AI-driven business value is expected to reach $3.9 trillion [7].
"One reason these large language models remain so remarkable is that a single model can be used for (multiple) tasks, such as answering questions, summarizing documents, generating text, completing sentences, translation and more." - Bernard Koch, computational social scientist at UCLA [6]
For data teams, perhaps the most exciting aspect is the ability to fine-tune LLMs for specific tasks, such as document summarization, programming help, or customer sentiment analysis [9]. This customization allows businesses to create tools tailored to their unique challenges and needs.
How LLMs Automate Data Workflows
Data teams often find themselves bogged down by repetitive tasks. In fact, over 80% of a data scientist’s time is spent on cleaning data, while 60–80% of a data engineer's effort goes into preparing raw data for analysis [10][11]. Large Language Models (LLMs) are changing this dynamic by automating many of these tedious processes, allowing professionals to focus on deeper analysis and strategic decision-making.
Automating Daily Data Tasks
LLMs are particularly effective at handling the routine tasks data teams encounter every day. These AI systems can break down and simplify cleaning processes [10] with reasoning that mimics human thought.
For instance, LLM agents can automatically detect and fix data quality issues like missing values, type mismatches, and schema errors [11]. Unlike older rule-based systems, LLMs leverage natural language understanding and code generation to interpret column names, documentation, and plain-English instructions.
Here are some common tasks LLMs can manage:
Filling in missing values
Inferring and converting data types
Standardizing and normalizing data
Removing duplicates and resolving entities
Mapping schemas
Imagine a dataset with inconsistent date formats. An LLM can identify the pattern, infer the correct format from context, and generate the necessary transformation code - all without human intervention.
Real-world applications of LLMs highlight their versatility. For example, a retail company uses LLMs to analyze industry articles, blogs, and social media to uncover emerging trends and consumer preferences [2]. By extracting key themes and sentiments, the company gains actionable insights that influence product development, marketing strategies, and inventory decisions.
Similarly, a financial institution applies LLMs to evaluate potential risks in its loan portfolio [2]. By analyzing textual data from loan applications, customer interactions, and credit reports, LLMs spot patterns that signal potential defaults or fraud. This enables the institution to make more informed lending decisions and reduce risk.
For teams adopting LLM automation, a few best practices can ensure smoother integration:
Provide metadata like table comments, schema diagrams, and sample queries to guide the model and reduce errors [11].
Run deterministic tests alongside LLM suggestions to validate results [11].
Use sandbox environments to execute generated code safely and prevent unintended operations [11].
Beyond cleaning tasks, LLMs also shine in generating code and crafting queries.
Faster Query and Code Creation
LLMs don’t just save time on data cleaning - they also speed up technical tasks like writing code. These models can translate plain-English instructions into functional code in seconds. Instead of spending hours crafting SQL queries or Python scripts, data professionals can simply describe their needs, and the LLM delivers the code.
For example, LLMs can generate Python scripts for exploratory data analysis (EDA), such as reading CSV files, identifying important columns, or creating visualizations [13]. They’re also capable of summarizing column and table descriptions, helping analysts save time during the initial stages of EDA [13]. Whether it’s building a Wordcloud or writing a complex SQL query, LLMs can handle it [13].
The benefits don’t stop at basic tasks. LLMs can produce tailored transformations in SQL, Python, or Spark that align with your team’s tools and project goals [11]. This adaptability allows organizations to enhance productivity without overhauling their existing workflows.
Here’s another example: a hospitality company leverages LLMs to analyze customer feedback from online reviews and social media [2]. By applying sentiment analysis, the company identifies customer pain points and areas for improvement. This leads to targeted service enhancements, better reviews, and higher revenue.
To get the most out of LLMs for code generation:
Be precise in your requests - LLMs perform better with clear, detailed prompts [12].
Provide context by including additional information in your input to guide the model [12].
Break down complex tasks into smaller steps, and include examples to help the LLM understand your desired outcome [12].
Ultimately, LLMs work best as a complement to human expertise, not a replacement [2]. By combining the efficiency of LLMs with human judgment, teams can tackle larger workloads while maintaining high-quality results and focusing on the most critical analytical challenges.
Making Data More Accessible to All Team Members
With the rise of automation in routine tasks, large language models (LLMs) are now breaking down barriers to data access across organizations. Many businesses struggle with a disconnect between data experts and decision-makers. In fact, 46% of companies admit to underutilizing their data due to technical hurdles [17]. However, industries are already addressing these challenges with forward-thinking solutions.
Take Uber, for example. Its QueryGPT processes an impressive 1.2 million queries monthly, slashing query creation time from 10 minutes to just 3 minutes [14]. Similarly, Pinterest saw a 35% faster completion rate for SQL tasks by integrating Text-to-SQL into Querybook [14]. These improvements highlight a shift in how businesses make data accessible and actionable.
Giving Everyone Access to Data
Traditional data workflows often create bottlenecks. Business users frequently need insights but are stuck waiting for data teams or learning complex query languages themselves. LLMs are changing this dynamic by translating plain English into SQL queries, making it easier for anyone to ask questions naturally.
For instance, a marketing manager might ask, "How many units have we sold so far today?" [14], without needing to understand database structures or query syntax. Tools like Querio are leading the way by connecting directly to databases and offering natural language interfaces. These platforms enable users - regardless of technical expertise - to query, explore, and report on data as easily as having a conversation. Integrating semantic layers with LLMs has even achieved 92.5% accuracy in translating natural language into SQL queries [15].
When more team members can independently access data, organizations benefit from quicker decisions and less reliance on technical teams. Interestingly, while 92% of business and IT leaders are open to learning new data skills, only 57% feel highly enthusiastic about working with data [17]. Natural language interfaces can ease this hesitation by removing the complexity traditionally associated with querying data.
This shift toward broader data accessibility fosters smoother collaboration between business and technical teams.
Better Teamwork Between Data and Business Teams
Improved data access naturally strengthens collaboration between technical and business teams. Historically, these teams often faced communication gaps, with business users struggling to articulate their needs and technical teams deciphering and fulfilling those requests. Tools like LinkedIn's SQL Bot, built using LangChain and LangGraph, address this issue by enabling employees to turn natural language questions into SQL queries, streamlining workflows [16].
This collaboration works both ways. When business users see how their questions translate into queries, they gain a better understanding of data structures. At the same time, technical teams get a clearer sense of business priorities. For example, one startup reduced project turnaround times by 30% after implementing an LLM Gateway [18]. Similarly, a sales team using an AI-powered collaboration tool saw a 50% boost in lead conversion rates [18].
The most effective tools offer flexibility, supporting multiple interaction modes like SQL, Python, drag-and-drop interfaces, and natural language queries. This ensures that technical users aren’t limited by overly simplified tools, while business users aren’t overwhelmed by complexity.
In addition to enabling easier access, LLMs help data teams create clear documentation, plain-language summaries of analysis, and transparent assessments of data quality. This builds trust and encourages more informed decision-making.
Ultimately, the best results come from combining LLM-powered tools with focused training and pilot projects. These efforts enhance technical expertise rather than replace it, ensuring that teams are equipped to make the most of their data.
Better Code Quality and Data Security with LLMs
As data teams turn to large language models (LLMs) to improve accessibility and collaboration, ensuring code quality and security becomes even more important. While LLMs can speed up development and simplify workflows, they can also introduce vulnerabilities if not used carefully. These tools are reshaping how teams handle complex data workflows, but their effectiveness hinges on maintaining high standards for quality and security.
Modern data workflows often involve intricate SQL queries, Python scripts, and integration code that can hide subtle bugs or security risks. Traditional code reviews sometimes overlook logical errors that don’t trigger syntax warnings. LLMs step in here, offering advanced debugging tools and proactive security analysis to catch these issues early.
Spotting and Fixing Code Errors
LLMs go beyond catching syntax errors - they can identify logical flaws that lead to incorrect outputs. They’re capable of analyzing complex conditional statements, detecting unreachable code, and understanding why certain logic paths might never execute.
Take GitHub Copilot as an example. It generates about 46% of developers’ code and boosts coding speed by up to 55% [19]. Interestingly, it’s also less likely to produce vulnerable code compared to what developers might write themselves [19]. With proper use, well-trained LLMs can significantly enhance baseline code quality.
Different LLMs bring unique strengths to debugging. ChatGPT excels at quickly identifying bugs, Claude is known for strong reasoning, Gemini offers detailed explanations, and Deepseek handles complex edge cases. Studies show ChatGPT achieves ~68% accuracy for detecting Null Dereference bugs and ~77% for Resource Leak bugs [19]. It also boasts approximately 94% precision in removing false-positive warnings for Null Dereference issues [19]. Beyond error detection, LLMs can suggest multi-step corrections, flag runtime errors before they occur, and even refactor code to make it clearer without changing its functionality.
But LLMs aren’t just about fixing errors - they’re also powerful tools for securing data workflows.
Strengthening Data Workflow Security
LLMs also help protect data workflows by identifying vulnerabilities in real time. They can scan code for issues like memory management errors, file handling problems, injection attacks, and authentication flaws, often flagging problems before deployment.
These security advantages are backed by numbers. For example, ChatGPT-generated code has been found to contain 20% fewer vulnerabilities than code sourced from Stack Overflow [19]. Advanced models show even better results: CodeLlama-7B achieved an F1-score of 82% in vulnerability detection with discriminative fine-tuning, along with a precision of around 89% and a recall of about 78% [19]. Additionally, GPT-2 Large and GPT-2 XL achieved F1-scores of 95.51% and 95.40%, respectively, for identifying buffer and resource management errors [22].
However, LLM-generated code isn’t inherently secure. Studies indicate that between 25% and 70% of outputs from leading models still contain vulnerabilities [21]. This highlights the need for a comprehensive approach to security when integrating LLMs into data workflows.
Best practices for securing LLMs include sanitizing inputs and outputs to prevent harmful content or sensitive data leaks, enforcing role-based permissions and multi-factor authentication, and storing credentials externally with frequent rotations. Monitoring model activity through logging, anomaly detection, and recovery plans is also essential for minimizing risks, especially when handling sensitive data.
Real-world incidents underline the importance of these precautions. For instance, engineers from Samsung’s semiconductor division used ChatGPT to troubleshoot code issues, unintentionally exposing proprietary information. This case illustrates how LLMs can retain sensitive training data, posing significant risks [20].
Real Examples of LLMs in Data Teams
Examples from real-world applications highlight how large language models (LLMs) are reshaping data workflows. Businesses across sectors are using LLMs to improve data access, streamline analysis, and speed up decision-making. Whether it’s natural language querying or generating automated insights, these tools are driving measurable gains in productivity and efficiency.
Asking Questions About Data in Plain English
One of the standout benefits of LLMs is how they simplify database interactions. Team members can now ask questions in plain English and get instant, actionable insights - no SQL expertise required.
Take Uber, for example. Their QueryGPT tool handles around 1.2 million queries every month, reducing the time spent crafting queries by 70% [14]. This improvement allows teams to make faster decisions across Uber’s global network.
Pinterest has also seen impressive results by adding text-to-SQL capabilities to their Querybook platform. Engineers and analysts now complete SQL tasks 35% faster, accelerating data-driven decisions in areas like product development and marketing [14].
The accuracy of these systems continues to improve as well. For instance, GPT-4 achieves about 48% accuracy in generating SQL queries with basic prompts. When schema-aware prompting and column-level descriptions are added, accuracy rises to 65%. Incorporating few-shot examples and custom instructions pushes accuracy even further, up to 70% [23].
Tools like Querio take this concept even further, enabling anyone - regardless of technical skill - to query databases using natural language. For example, a business team member might ask, “What was our revenue last quarter by region?” without needing IT support. This accessibility speeds up decision-making and brings more voices into the process.
Automatic Insights from Data
LLMs don’t just answer specific questions - they can uncover patterns and generate insights that might otherwise go unnoticed. By analyzing large datasets, they can highlight trends, anomalies, and correlations that inform strategic decisions.
For example, Rittman Analytics demonstrated this capability in June 2024. Using Looker Studio Pro and Gemini 1.5-Flash, they analyzed 36 months of KPI data and automatically generated narratives for each visualization [28]. This approach saved time while providing executives with clear, actionable insights.
In healthcare, LLMs help clinicians access complex insights without sifting through charts manually [24]. Risk analysts can investigate fraud by asking questions like, “Show suspicious transactions over $10,000 from last month” [24]. E-commerce teams can easily analyze product trends, inventory levels, or return rates - all through straightforward natural language queries [24].
According to McKinsey, generative AI boosts issue resolution rates by 14% per hour and cuts time spent managing cases by 9% [30]. These gains extend to existing business intelligence tools, making everyday analytics more efficient.
Working with Current Data Tools
LLMs also integrate smoothly with existing business intelligence (BI) platforms, enhancing workflows without requiring a complete system overhaul.
For example, Tableau AI lets users create advanced visualizations and even full dashboard layouts using natural language prompts [29]. Users simply describe the analysis they need, and Tableau generates the appropriate charts and insights. As Tableau explains:
"AI in Tableau democratizes data analysis and simplifies insights consumption at scale by bringing trusted generative AI to the entire Tableau platform" [25].
Other BI tools, like Looker and Qlik Sense, are also incorporating LLMs to enhance their capabilities [27]. These platforms combine natural language interfaces with traditional BI functions, making analytics more intuitive and accessible.
By 2025, Gartner predicts that over 50% of business analytics queries will be conducted through search, natural language processing, or voice [26]. This trend reflects a growing expectation for data tools to be as user-friendly as search engines.
LLM integration isn’t limited to BI tools. Customer support and CRM teams are also benefiting. For instance, they can identify patterns in support tickets or detect signs of customer churn through conversational queries [24]. Developers, too, can debug databases more efficiently by asking questions like, “What were the last 10 updates to the orders table?” instead of writing complex queries [24].
Current Limits and What's Coming Next
LLMs are reshaping how data workflows operate, but they’re far from perfect. They come with challenges that can slow productivity and impact accuracy. Understanding these hurdles - and staying informed about upcoming advancements - can help organizations get the most out of their AI investments.
Where LLMs Still Fall Short
Despite their capabilities, LLMs face several key limitations. One of the most pressing concerns is hallucinations - instances where the model generates incorrect or nonsensical information. OpenAI reported that GPT-3 produced such errors about 15% of the time [31]. Additionally, token limits restrict their ability to process large datasets, and their knowledge is capped at the time of their last training. For example, models trained before 2023 won’t recognize updates or features introduced afterward [31][32].
Another issue is multi-step reasoning. LLMs often struggle to break down complex problems logically, and bias remains a significant concern. A 2021 study revealed that GPT-3 generated biased responses 19% of the time when asked about politically sensitive topics, which could skew data analysis in certain contexts [31]. Privacy risks are also a problem, as these systems can inadvertently reveal sensitive customer or business information based on input data [31].
While these challenges are real, the next wave of LLM advancements promises to address many of these shortcomings.
What’s Next for LLM Technology
The future of LLMs looks promising, with developments aimed at solving current limitations. One major breakthrough on the horizon is real-time data integration. Future models are expected to connect to external sources and provide citations for their answers, reducing hallucinations and ensuring their information is current. Microsoft Copilot offers a glimpse of this potential, combining GPT-4 with live internet data to answer real-time questions [34].
Specialized models are also making strides. For example, BloombergGPT, a 50-billion-parameter model trained on financial data, and Google’s Med-Palm 2, designed for medical applications, show how domain-specific training can deliver more precise and relevant results [34]. Advances in reasoning models are another exciting area. These models aim to go beyond surface-level answers, providing detailed, step-by-step solutions. DeepSeek recently unveiled its DeepSeek-R1 model, boasting 671 billion parameters and competitive performance at a lower cost compared to leading tech giants [34][35].
Autonomous agents are poised to revolutionize how data teams work. Gartner predicts that by 2028, one-third of enterprise applications will include autonomous agents, enabling 15% of workplace decisions to be made automatically [36]. These agents could handle tasks like generating reports or detecting anomalies without needing constant human oversight. Meanwhile, multimodal capabilities are expanding, allowing future LLMs to process text, images, audio, and video all at once [34][36].
The market reflects this momentum. Projections suggest the global LLM market could grow from $6.4 billion in 2024 to over $36.1 billion by 2030. Additionally, Goldman Sachs estimates that generative AI could contribute up to a 7% increase in global GDP over the next decade [35].
To prepare for these advancements, data teams should prioritize a few key strategies. First, maintaining clean and validated data will be more critical than ever, so establishing robust data hygiene practices is essential [33]. Collaboration across roles - bringing together data engineers, machine learning experts, and domain specialists - will be crucial for effectively implementing and optimizing these systems [33]. Finally, setting up continuous monitoring and feedback processes will help teams adapt quickly as technology evolves [33].
"I think we are going to see a lot of motion next year around agents, and I think people are going to be surprised at how fast this technology comes at us." – Sarah Friar, OpenAI CFO [35]
Conclusion: Using LLMs to Build Better Data Teams
Large language models (LLMs) are reshaping how data teams work, moving from a futuristic concept to an everyday tool. This shift represents a leap from manual processes to smarter, more efficient workflows. Companies adopting LLMs are already experiencing boosts in productivity, quicker decision-making, and stronger collaboration among teams.
These models are making waves across all areas of data work. LLMs handle repetitive tasks with ease and make data more accessible by transforming plain language questions into actionable insights [1][37]. Acting as intelligent assistants, they can break down complex research, extract insights from unstructured data, and speed up decision-making across entire organizations [37].
And this isn’t just theory - it’s happening now. Mastercard, for example, improved fraud detection by an impressive 300%, while Bank of America’s virtual assistant resolved 98% of over two million daily customer queries [41]. In fact, generative AI is projected to contribute $200–$340 billion annually to the banking sector alone [41].
LLMs are also proving useful across diverse roles, simplifying tasks like data synthesis, campaign creation, risk evaluation, and onboarding [37].
To get the most out of LLMs, organizations need to adopt them thoughtfully. Start with a gap analysis, ensure data is clean and well-prepared [1][38], and implement strong governance practices like role-based controls, audit trails, and content filters [39].
The message is clear: adopting LLMs quickly is crucial for staying competitive. With AI adoption growing at an annual rate of 36.6% [40], hesitation could mean falling behind. Success lies in starting small, addressing real challenges, and acting decisively. The time to lead this transformation is now.
FAQs
How do large language models improve collaboration between technical and non-technical teams?
Large language models (LLMs) are transforming teamwork by breaking down complex data and removing barriers between technical and non-technical teams. They can take dense, technical language and translate it into clear, straightforward terms, ensuring everyone on the team can grasp key insights and actively participate in discussions.
On top of that, LLMs can handle tasks like data analysis, creating reports, and summarizing information, which cuts down on manual work. This means non-technical team members can interact with data directly, even without specialized skills, leading to more inclusive and collaborative decision-making. By speeding up communication and eliminating bottlenecks, LLMs help teams work more smoothly, sparking creativity and boosting overall productivity.
What are the best practices for securely and accurately integrating large language models into data workflows?
How to Integrate LLMs into Data Workflows Securely and Accurately
Incorporating large language models (LLMs) into your data workflows can be transformative, but it’s crucial to prioritize security and accuracy. Here are some essential practices to keep in mind:
Set up robust data governance policies: Establish clear rules for managing sensitive information. This helps prevent unauthorized access or accidental exposure during both model training and usage.
Work with high-quality datasets: Training your models on well-curated, pre-labeled data reduces bias and ensures more reliable results.
Perform regular audits: Frequently review the model's performance to catch inaccuracies or potential security risks before they become larger issues.
By following these steps, your data team can leverage LLMs to enhance workflows and uncover valuable insights while safeguarding data integrity and security.
How do large language models address data privacy when handling sensitive or confidential information?
Large language models (LLMs) are powerful tools for processing enormous datasets, but working with sensitive or proprietary information demands a sharp focus on data privacy. A key concern is the risk of data leakage, where confidential details could unintentionally be exposed during interactions with the model.
To reduce these risks, organizations should follow strict practices like securing input data, limiting access to the model, and using output filters to catch potential disclosures. Opting for private or fine-tuned LLMs can add another layer of protection, though it's important to remember that no solution is entirely foolproof. Regular audits, consistent monitoring, and strict compliance with privacy regulations are vital to safeguarding sensitive information and maintaining trust in these technologies.