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How and why to use internal data with AI?
In today’s digital world, data is the new gold. The data reserves of companies grow daily, yet often remain unused or insufficiently analyzed. To use internal data with AI and gain valuable insights, it is essential to choose the right technologies and methods for data analysis. In this blog post, we explore various approaches to using internal data with AI, starting from simple database queries to advanced AI methods like knowledge graph databases and fine-tuning AI models.
Overall, it should be noted that working with AI always involves a degree of uncertainty. Depending on the nature and volume of internal data, some methods work better than others. The following approaches are ordered by complexity and provide an overview of different ways to approach the use of internal data with AI.
Simple database queries
A first step to using internal data with AI is to execute predefined database queries. These allow for filtering entries and calculating statistical metrics such as average or median values.
Simple database queries are particularly useful for companies looking to quickly and cost-effectively extract basic information from their databases. This method is well-suited for reports and dashboards that need regular updates without requiring complex systems. Typically, such queries are static and do not require AI.
Advantages of simple database queries
- Quick Implementation: Database queries are relatively easy to implement and do not require extensive programming.
- No Ongoing Costs: Once set up, predefined queries incur no additional costs.
Disadvantages of simple database queries
- Limited Flexibility: The results are limited to predefined queries and simple arithmetic calculations.
- Limited Insights: Complex analyses and deeper insights are not possible with this approach.
Advanced database queries with large language models (LLMs)
Another step involves analyzing the results of predefined database queries with large language models (LLMs). These models, such as GPT-4, can further process and use internal data with AI. An advantage over simple database queries is that this approach can also analyze text.
LLMs offer a powerful way to recognize complex data patterns and use natural language processing (NLP) to analyze query results. This method is particularly suitable for companies looking to ensure deeper insights into internal data with AI without significant upfront investments in custom AI solutions.
Advantages of advanced database queries
- Quick Implementation: The combination of database queries and LLMs can be quickly implemented.
- Iterative Improvement: Results can be optimized through repeated adjustments and improvements.
Disadvantages of advanced database queries
- Possible Inflexibility: The results may not be flexible enough to cover all questions.
- Costs: Each run incurs costs in the cent range, which can add up with frequent queries.
Automatic generation of database queries with LLMs
An advanced approach to using internal data with AI is the dynamic generation of database queries by LLMs. These models can generate SQL queries that are executed and then further processed.
This approach enables companies to optimize their database queries in a way that would be difficult to achieve manually. The automated creation of queries saves time and resources and can significantly increase the efficiency of data analysis.
Advantages of automatic query generation
- Greater Flexibility: LLMs can flexibly respond to different questions and generate appropriate queries.
- Better Results: The dynamic adjustment of queries can lead to more accurate and relevant results.
Disadvantages of automatic query generation
- Difficult to Iterate: The adjustment and improvement of queries can be more complex.
- Higher Variable Costs: Using LLMs to create and execute queries can lead to higher variable costs.
(Partial) Vectorization of results
To use data with AI, especially texts, results from database queries can be vectorized. This allows for similarity searches and better filtering based on textual content.
Vectorizing data enables companies to efficiently search through complex text data and quickly find relevant information in addition to numbers. This is particularly useful in industries that process large amounts of unstructured data, such as customer service analysis or legal research.
Advantages of vectorization
- Improved Filtering: Vectorization allows data to be filtered based on similarity, leading to more precise results.
- Faster Performance: Pre-vectorization can increase processing speed.
Disadvantages of vectorization
- Complexity: Defining vectorization strategies and managing the vector database requires expertise and effort.
- Regular Updates: To account for current data, vectorization must be regularly updated.
Implementation of an agentic workflow
To start using data with AI, an AI agent can be developed that independently conducts database queries and interprets the results. This approach can deliver very effective results but requires considerable fine-tuning at the outset.
Implementing an agentic workflow is ideal for companies that want to largely automate the use of internal data with AI. A well-tuned AI agent can continuously learn and improve, resulting in better outcomes over the long term.
Advantages of AI agents
- High Effectiveness: AI agents can perform complex tasks independently and deliver highly accurate results.
- Automation: Once set up, the AI agent can work continuously and autonomously.
Disadvantages of AI agents
- High Volatility: In the early stages, the performance of the AI agent can vary greatly.
- Significant Effort: Setting up and fine-tuning the agent requires considerable resources.
Conversion to a knowledge-graph database
Another way to utilize internal data with AI is to convert the existing database into a knowledge graph database like neo4j. This allows for complex queries regarding the relationships between data and is particularly useful for pattern recognition.
Knowledge graph databases offer a unique way to visualize and analyze data in ways not possible with traditional databases. This can be especially useful for industries that manage complex networks of information, such as healthcare or finance.
Advantages of a knowledge-graph database
- Pattern Recognition: Knowledge graph databases are excellent for identifying complex relationships and patterns in data.
- No Variable Extraction Costs: Once set up, there are no additional costs for data extraction.
Disadvantages of a knowledge-graph database
- High Setup Effort: Converting and setting up a knowledge graph database requires significant resources.
- Possible Flexibility Issues: Compared to AI agents, knowledge graph databases may be less flexible.
Fine-tuning AI models with internal data
Finally, existing AI models can be fine-tuned with additional internal data. This allows for high customization and powerful results but requires extensive preparation and training work.
Fine-tuning AI models is particularly beneficial for companies that already have extensive data reserves and want to make the most of them. This approach enables AI solutions to be tailored precisely to the specific needs of the company, maximizing the benefit of the available data.
Advantages of fine-tuning
- High Customization Options: Fine-tuned models can take into account the specific requirements and questions of a company.
- Powerful Results: Using company-specific data can significantly improve outcomes.
Disadvantages of fine-tuning
- Significant Preparation and Training Effort: Fine-tuning AI models requires extensive data preparation and training resources.
- Model Management Required: To achieve the best results, models must be continuously monitored and adjusted as needed.
Conclusion on using internal data with AI
Choosing the right approach to using internal data with AI depends on the specific requirements and goals of your company. A strategic approach is crucial to achieving the best results.
By implementing one or more of these methods, companies can not only use internal data with AI more effectively but also enhance their competitiveness. Whether through simple database queries or advanced AI models – the right strategy can make a significant difference and lead to substantial improvements in decision-making and efficiency.
Additionally, the use of internal data with AI can often be excellently applied to the development of services and products. Depending on the offering, the possibilities here are very diverse but generally present.
To begin using internal data with AI, it is advisable for medium-sized companies to consult experts who can quickly provide information on which approaches are most effective in a specific case.