LLMs (large language models) are undisputedly one of the biggest game-changers in artificial intelligence and related fields. Apart from many other applications, LLMs allow you to simplify data analytics, even if you don’t possess advanced technical knowledge. That’s because, with large language models, all you have to do is ask a specific question, and the AI analytics tool will provide you with easy-to-understand/interpret answers. Let’s have a look at how data analytics teams can use LLMs in their daily work.
The importance of LLM in data science
Large language models are, in essence, AI algorithms that have been trained on huge amounts of textual data. This training enables them to communicate with users in a simplified question-answer manner. Combined with data analytics capabilities, LLMs can simply provide you with all the answers you need regarding your datasets, even when it comes to analyzing complex data.
And because LLMs are almost fully automated, the amount of time needed to get them fully operational is minimized as well. With LLMs, even non-technical employees can get the information they need in their work simply by asking a straightforward question, such as:
- Who placed the most orders in the past year?
- Which customer segment is the most profitable one for our business?
- What sales trends are expected in 2025?
The LLM model will analyze the question and look for the answer in the available data sources. In a matter of moments, the user gets an answer that’s as straightforward as the question that was asked.
This functionality comes in handy, especially concerning decision-making processes.
The role of LLMs in improving data-driven decisions
LLMs help you make better data-driven decisions that are based on comprehensive input comprising all the available data. There are four aspects of this functionality:
- Understandable and actionable answers: With LLMs, you don’t need any additional analysis. The answer you get is straightforward and to the point. All you need to do is implement the AI’s suggestions!
- No more data complexity: Even if your company operates on dozens of complex datasets, LLMs will streamline the analysis process for you so that you’re not overwhelmed by the outcome. Frequently, LLMs will summarize their recommendations, thus allowing you to focus on decisions rather than on “decoding” data.
- Scalability: LLMs can process even large datasets at scale, thus enabling you to get all the insights you need in a matter of seconds or minutes.
- Unstructured data analytics: LLMs can analyze even unstructured data so that you can save a lot of time and work and get quick access to all the information you need.
Practical applications of LLM analytics for businesses
There are seven applications that your business can implement in the context of LLM analytics:
Natural language query interface for data warehouses
This feature allows your employees to “interact” with data warehouses using natural language. LLMs will interpret the query and obtain the necessary information on auto-pilot. All in a 100% user-friendly manner that doesn’t require advanced tech knowledge.
Enhanced data interpretation layer for visualization tools
LLMs can be integrated with data visualization tools, such as Tableau or Power BI. Thanks to this integration, you can ask LLMs to interpret visual data, e.g., concerning trends, patterns, or anomalies. The LLM tool will analyze the visual data for you and provide you with actionable insights. This feature comes in handy concerning reporting and budget-planning purposes.
CRM and ERP system integration for contextual data analysis
Contextual data analysis supports your work with CRM and ERP systems. Use LLMs to analyze data coming from those systems so that you can make more customer-focused decisions. Again, you can use this feature to help non-technical teams get all the customer-related data they need in a simplified manner.
Automated data interpretation and summarization
Interpreting and summarizing data frequently poses many challenges and is definitely time-consuming. With LLMs, you can automatically analyze, interpret, and summarize all the available data. Thanks to this feature, you can create comprehensive reports in a matter of minutes. Additionally, LLMs will help you pinpoint crucial findings and data points that should be analyzed in greater detail, e.g., during a board meeting.
Semantic search over structured data
Even though structured data is generally better for data analysis purposes, it can create some challenges concerning semantic search. LLMs can help you retrieve information based on the meaning behind natural queries, which means you are no longer limited to keyword-based searches. And that’s true even if the relevant records have not been explicitly tagged with those keywords.
Custom data transformation and preprocessing
LLMs can even help you with data transformation and preprocessing, especially when it comes to:
- Data cleaning
- Standardizing records
- Categorizing data
As a result, even disorganized datasets can be ready for analysis in a much shorter time.
Automated contextual recommendations
Lastly, with LLMs, you can use contextual information to get recommendations for your sales and marketing teams. This is especially useful in analyzing and addressing changes in customer behavior and shopping patterns. You can use this feature to come up with new cross-selling/upselling opportunities in your company.
Wrapping up
Thanks to LLMs, even non-technical teams can make the most of the data available in your business, and they can do so in a matter of minutes instead of hours! If you’d like to discover how LLMs can improve the way your company handles and analyzes data, check ContextClue.
