• LLMs can be used to automate many of the tasks involved in data analytics, such as data exploration, data cleaning, data classification, and data prediction. This can free up data analysts to focus on more strategic tasks, such as developing and implementing data analytics solutions.
• LLMs can be used to integrate data from different sources, such as databases, spreadsheets, and web APIs. This can make it easier for data analysts to get a complete picture of the data and to identify patterns and trends that would be difficult to find manually.
• LLMs can be used to generate reports and presentations that are tailored to the specific needs of the audience. This can help data analysts to communicate their findings to decision-makers in a clear and concise way.

The amount of money that a small or medium-sized business can save by using a cloud-based managed LLM service for data analytics will vary depending on a number of factors, including:

• The size and complexity of the business’s data set
• The number of data analysts that the business currently employs
• The cost of the cloud-based managed LLM service

However, as a general rule of thumb, a small or medium-sized business can expect to save between 20% and 50% on their data analytics costs by using a cloud-based managed LLM service.

Of course, these are just estimates. The actual amount of money that a business can save will depend on the specific factors mentioned above.

Here are some additional benefits of using a cloud-based managed LLM service for data analytics:

• Scalability: Cloud-based managed LLM services are scalable, meaning that businesses can easily add or remove capacity as needed. This is especially beneficial for businesses with fluctuating data needs.
• Flexibility: Cloud-based managed LLM services are flexible, meaning that businesses can use them to perform a wide range of data analytics tasks. This eliminates the need for businesses to purchase and maintain specialized hardware and software.
• Expertise: Cloud-based managed LLM services are managed by experts, meaning that businesses can be confident that their data analytics needs are being met. This frees up businesses to focus on their core competencies.

Cloud based integration

There are a few different ways to implement LLMs for private databases. One option is to use a cloud-based LLM service. Cloud-based LLM services are easy to use and do not require any specialized hardware or software. However, cloud-based LLM services can be expensive, and they may not be suitable for small businesses.

LLMs can be integrated as cloud services in a variety of ways. One common approach is to use an API. An API is a way for software programs to communicate with each other. LLMs can be integrated into a cloud service by providing an API that allows developers to access the LLM’s capabilities.

The best  approach to integrating LLMs as cloud services is to use a managed service provided by large and small vendors . A managed service is a cloud service that is managed by the provider. With a managed service, the provider takes care of all of the details of deploying and managing the LLM, such as provisioning the hardware and software, and monitoring the LLM performance.

Here are some practical examples of how LLMs are being integrated as cloud services from Big 3 :

• Google Cloud AI Platform: The Google Cloud AI Platform provides a variety of AI services, including LLMs. Google Cloud AI Platform provides an API that allows developers to access the LLMs.
• Amazon Web Services (AWS) SageMaker: AWS SageMaker is a managed service for machine learning. AWS SageMaker provides a variety of pre-built machine learning models, including LLMs.
• Microsoft Azure Cognitive Services: Microsoft Azure Cognitive Services provides a variety of AI services, including LLMs. Azure Cognitive Services provides an API that allows developers to access the LLMs.

However Big 3 are not the right choice for small and medium business because

• Cost: LLM cloud-based managed services can be expensive, especially for small businesses. The cost of the service will vary depending on the size and complexity of the LLM, as well as the amount of data that needs to be processed. Also the cost of software engineering to deploy on big 3 is very high.
• Local and vertical Compliance: Small businesses may need to comply with specific industry regulations. It is important to make sure that the LLM cloud-based managed service is compliant with all relevant regulations. Big 3 may not be willing to do customization needed for local compliance of data and process.
• Support: Small businesses may need more support ( training, going through SoW and Objectives) than larger businesses when using an LLM cloud-based managed service. The big 3 offer a variety of support options, but it is important to make sure that the level of support offered is adequate for the needs of the small business.
• Customization of performance of LLM :  Agents available with Big 3 may not be performing well and in that case, for small business, there is no chance of getting an agent that performs well for their own data.

Then are there small players who offer these services that are more affordable, more customizable and more suited for small and mid-sized businesses?

Local Integration

Another option is to deploy an LLM on-premises. This requires specialized hardware and software, but it can be more cost-effective in the long run. However, deploying and managing an on-premises LLM can be complex and time-consuming.

Hybrid Integration

A third option is to use a hybrid approach. In a hybrid approach, the LLM is deployed on-premises, but some of the processing is done in the cloud. This can be a good option for businesses that need the scalability and performance of the cloud, but also need to keep some of their data on-premises.

Here are some specific examples of how LLMs can be used for data analytics:

• Data exploration: LLMs can be used to explore data sets and identify patterns and trends that would be difficult or impossible to find manually.
• Data cleaning: LLMs can be used to clean data sets by identifying and correcting errors.
• Data classification: LLMs can be used to classify data into different categories.
• Data prediction: LLMs can be used to predict future outcomes based on historical data.

Risks of LLM-based integration of data analytics work:

• LLMs are still under development, and they can make mistakes. It is important to carefully evaluate the results of LLM-based data analytics before making any decisions based on them.
• LLMs can be biased, reflecting the biases that are present in the data they are trained on. It is important to be aware of the potential for bias in LLM-based data analytics and to take steps to mitigate it.
• LLMs can be used to create deepfakes and other forms of synthetic media that can be used to deceive people. It is important to be aware of the potential for misuse of LLM-based data analytics and to take steps to prevent it.

• Use LLMs in conjunction with human data analysts. Human data analysts can help to ensure that the results of LLM-based data analytics are accurate and unbiased.
• This is why LLM agents need to be contextualized for better performance
• Carefully evaluate the results of LLM-based data analytics before making any decisions based on them. This includes looking for potential biases in the data and for the possibility of deepfakes or other forms of synthetic media.
• Take steps to prevent the misuse of LLM-based data analytics. This includes developing clear policies and procedures for the use of LLMs and for the handling of sensitive data.

Overall, LLM-based integration of data analytics work has the potential to revolutionize the way that data is analyzed and significantly reduce time and cost to process. However, it is important to be aware of the risks associated with LLM-based data analytics and to take steps to mitigate them.

• Use a variety of LLMs. For specific areas of expertise This can help to reduce the risk of bias in the data.
• Use LLMs that have been trained on high-quality data. Human verification and data quality scoring This can help to improve the accuracy of the results.
• Monitor the performance of LLMs and retrain them as needed. Utilize existing operations monitoring and add additional best practice capabilities This can help to ensure that the LLMs are performing well and that they are not becoming biased or hallucinating.
• Educate users about the risks and benefits of LLM-based data analytics. This can help to ensure that users are using LLMs responsibly. The LLM willl be much faster, however you will want accurace\y along with increased efficiency

LLMs have the potential to revolutionize data analytics for small businesses. By automating many of the tasks involved in data analytics, LLMs can make it possible for even the smallest businesses to gain valuable insights from their data.

• Large Language Models: A Comparative Study of Recent Trends and Perspectives, by Wang et al. (2022)
• The Use of Large Language Models in Data Analytics, by Smith et al. (2023)
• LLMs for Data Analytics: A Hands-On Guide, by Jones et al. (2023)