As the demand for real-time insights and data-driven decisions grows, many business leaders are evaluating the potential of large language models (LLMs) for analytics. While LLMs, like ChatGPT, offer a simplified way to interact with data through natural language, their use in business data analytics carries substantial risks. This article explores these risks, discusses the limitations of LLMs for accurate analytics, and presents a safer alternative for enterprise data needs.
Choosing the Right Analytics Approach for Business Intelligence
Companies looking to use conversational analytics for business intelligence now face a choice: adopt tools that facilitate secure, accurate data analysis for business users or take risks with LLMs that might compromise data accuracy and security. LLMs are impressive for a range of tasks, but when it comes to generating Python or SQL code for analytics, they’re falling far short of reliability. This is especially concerning, considering errors and data misinterpretation can lead to costly decisions for businesses.
Instead, some companies are turning to specialized conversational analytics platforms that allow business users to securely ask questions of their data without requiring coding expertise. Here, we’ll examine both approaches and the distinct advantages of solutions designed specifically for enterprise data analytics.
The Risks of Large Language Models (LLMs) in Business Analytics
Using LLMs for analytics can seem tempting—they can quickly generate Python or SQL code, and sometimes even direct insights based on simple prompts. However, there are several critical risks associated with this approach.
Risks of inaccurate data processing with LLMs
LLMs often struggle with data accuracy, producing results that can be misleading or outright incorrect. Because they are not built for complex data environments, they tend to generate code that is both inaccurate and inefficient. This poses a significant risk, especially for business users without a technical background who may rely on LLM-generated code for analytics, assuming it to be accurate. LLMs are also prone to “hallucinations”—instances where the model generates plausible yet incorrect responses based on faulty assumptions. In a business context, even minor inaccuracies can snowball into costly misinterpretations, leading to poor decision-making based on unreliable insights.
Data privacy and security concerns with LLM-based analytics
When it comes to data privacy and security, using LLMs introduces significant confidentiality risks. Sensitive business data uploaded to LLM platforms is often stored on external cloud infrastructure, raising concerns about unauthorized access and potential data exposure. This is particularly problematic for organizations that must adhere to strict regulatory standards. Without a robust data handling protocol, LLM use can easily lead to compliance violations, exposing companies to legal penalties and potentially damaging their reputations.
Loss of data control in LLM analytics platforms
Another major risk is the loss of control over data when using LLMs for analytics. Once business data is input into an LLM platform, it’s nearly impossible to track or manage its storage and processing. This can leave companies in the dark regarding where their data is stored, how long it is retained, and whether it aligns with corporate data policies on usage and deletion. Furthermore, companies risk compromising their intellectual property, as sensitive information processed by LLMs could be inadvertently disclosed or used to “train” the model, eroding competitive advantages if that information leaks or becomes accessible to others.
DataChat: A secure, reliable alternative to LLMs for enterprise analytics
Due to the risks associated with LLMs, many companies are turning to conversational analytics platforms built to protect sensitive data while delivering reliable insights. DataChat is unique among these platforms in that it doesn’t send data to an LLM or rely on LLMs to generate and execute code. DataChat allows business users to securely analyze data in plain English, with the data never leaving the underlying database.
What are conversational analytics, and how do they benefit enterprises?
Conversational analytics allow business users to ask natural language questions of their data. In the case of DataChat, our platform enables users to generate insights by asking questions in natural language of the data they already have access to. DataChat focuses on iterative analytics, allowing users to refine questions and reshape data, making data insights accessible to non-technical users without requiring coding knowledge.
Key benefits of conversational analytics platforms like DataChat
Data security and compliance for sensitive business data
DataChat keeps data securely within the company’s environment, reducing risks related to unauthorized access. With no reliance on external LLMs, DataChat eliminates concerns around data residency and control, making it ideal for organizations managing sensitive or regulated information. By maintaining a secured environment and avoiding third-party platforms, DataChat offers unparalleled security in the conversational analytics space.
Accurate and validated insights without LLM limitations
DataChat’s conversational analytics are distinct from LLM-based platforms, as they rely on built-in, enterprise-tested SQL and Python code for accuracy. This tested code ensures that business users receive precise, reliable responses, minimizing the risks associated with errors common in LLM-generated analytics. DataChat’s approach also provides transparency, documenting workflows to build confidence in insights and allowing data teams to validate the analytics process.
Reducing operational costs with conversational analytics
With DataChat, business users can independently generate insights, expediting the analytics process and freeing data teams for higher-level tasks. This streamlined process reduces dependency on technical staff and lowers operational costs. Without the need for frequent hiring or training, businesses can efficiently empower team members to confidently use data.
Exploring data with iterative analytics
Iterative analytics are unique to DataChat, enabling users to refine questions and explore data in real time. Business users can ask follow-up questions to dive deeper into their insights, uncovering more detailed answers. For example, a retail manager could start by asking which promotions were most effective, then dig deeper to assess customer lifetime value from various promotions—all without leaving DataChat’s secure platform. This iterative approach fosters more thorough, data-driven decisions and is unmatched by LLM-driven alternatives.
Selecting a secure data analytics platform for your business needs
The limitations of LLMs in analytics underscore the importance of secure, specialized platforms like DataChat. By opting for solutions designed for business data analytics, companies can empower teams to make informed, data-driven decisions without compromising on security or accuracy.
In a landscape where data privacy and accuracy are critical, choosing the right analytics platform is essential. DataChat provides a reliable, secure way to obtain actionable insights while avoiding the risks linked to general-purpose LLMs.
Conclusion: Secure, reliable analytics solutions for business insights
While LLMs may seem like a quick route to analytics, their risks—including inaccuracies, privacy concerns, and compliance challenges—pose substantial barriers. Investing in secure, purpose-built conversational analytics can enable organizations to harness data safely and effectively, empowering business users with the insights needed to drive growth.
Explore DataChat today to see how you can secure your data while maximizing its value through actionable insights.