Currently, the use of natural language processing models has been extended to optimize information search and\/or analysis tasks. Among these models, the one that has had the greatest impact to date has been Chat GPT, developed by OpenAI, which is capable of understanding and generating text in a coherent and contextual manner. <\/p>\n\n\n\n
The combination of Business Intelligence and Chat GPT can become the tool that changes the way data-driven decisions are made. Chat GPT can help automate and improve many aspects of BI, from report generation to user interaction.<\/p>\n\n\n\n
In this article, we will try to provide some keys on how the integration of Business Intelligence and Chat GPT can improve the way organizations analyze their data and make decisions. We will provide examples of its application in the real world and detail some of the benefits it can bring to businesses. So, if you’re interested in Business Intelligence and Chat GPT, keep reading to discover more.<\/p>\n\n\n\n
How Chat GPT Improves Your BI<\/h2>\n\n\n\n
The integration of Chat GPT into your organization’s Business Intelligence (BI) strategy can bring significant benefits immediately. Chat GPT, with its advanced natural language processing capability, can interact with users in a more intuitive and natural way, facilitating understanding and access to data.<\/p>\n\n\n\n
Here are some areas where these AI models can be integrated to substantially improve BI solutions:<\/p>\n\n\n\n
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- Chat GPT to automatically generate written reports from the data managed in your BI system. This is particularly useful for companies that handle large volumes of data. Instead of having to manually analyze that large amount of information and write reports, Chat GPT can be integrated, through ad-hoc developments, to do it automatically, saving time and effort.<\/li>\n\n\n\n
- Chat GPT removes certain barriers to data access in any organization. Instead of relying on a team of analysts\/data scientists, company staff, without the need for technical knowledge, can interact with Chat GPT to get the information they need. This can speed up decision-making and improve the efficiency of the company.<\/li>\n\n\n\n
- The use of Chat GPT allows you to take advantage of its ability to learn and improve over time. As it interacts with users and receives feedback, Chat GPT can improve its understanding and text generation, which will allow it to generate more accurate and useful data analysis.<\/li>\n<\/ul>\n\n\n\n
In summary, the combination of Business Intelligence and Chat GPT can provide any company with a competitive advantage by improving the efficiency of their BI systems.<\/p>\n\n\n\n
Chat GPT Integration<\/h2>\n\n\n\n
As we have seen in the previous section, significant benefits can be achieved through the integration of BI systems and Chat GPT. Indeed, certain developments and integrations will be required as obtaining these advantages using the public Chat GPT service is unfeasible or at least very challenging.<\/p>\n\n\n
\n![\"Open](\"https:\/\/ignos.blog\/wp-content\/uploads\/2023\/05\/openai-logo-horizontal-flat-black-300x125.png\")
Open AI Logo<\/figcaption><\/figure>\n<\/div>\n\n\n The optimal way to achieve this integration is by utilizing the OpenAI API (https:\/\/openai.com\/blog\/openai-api<\/a>). This API will allow us to access the models, using any programming language, for their use as “Text Completion,” as well as specific training functionalities through “Fine Tuning.” With the models used in “Text Completion,” we can create prompts that include the data extracted from our BI system and formulate the questions or actions to be performed with them. This approach, when implemented correctly, can yield optimal results.<\/p>\n\n\n\n
Use case<\/h2>\n\n\n\n
As an example of creating an automated report, we will use a dataset related to gasoline prices in the US as input information (https:\/\/vincentarelbundock.github.io\/Rdatasets\/csv\/AER\/USGasG.csv<\/a>). Using the API, and utilizing the latest version of the “Text Completion” model, in this case, the latest version of the DaVinci model, we form a prompt where we concatenate the data contained in the CSV file, followed by the action “Create a report related to the above data.” In this case, we will obtain a response that includes an analysis of both the meaning of each column (field) and the dataset as a whole. Here is a response from the model:<\/p>\n\n\n\n
This report provides an analysis of a series of data covering 36\n periods that encompass a variety of economic and demographic \nfactors, including gas prices, income, prices of new and used \ncars, transportation expenses, durable and non-durable goods, \nservices, and population.\n\nGas Prices: Gas prices show a general increase over the 36 \nperiods. Starting at 129.7, gas prices rise to a peak of 297.8 \nin the last period.\n\nIncome: There is also a general increase in income over the \nperiods, ranging from 6036 to 11934.\n\nPrices of New and Used Cars: Prices of new and used cars also \nshow an overall upward trend. Prices of new cars increased from \n1.045 to 2.815, while prices of used cars rose from 836 to 5.224.\n\nTransportation Expenses: Transportation expenses have shown a \nsteady increase over time, ranging from 0.81 to 6.41.\n\nDurable and Non-durable Goods: The trend in durable and \nnon-durable goods is also upward. Durable goods have increased \nfrom 444 to 1.239, and non-durable goods have increased from 331\nto 1.419.\n\nServices: Services have also shown a steady increase, ranging \nfrom 302 to 1.786.\n\nPopulation: The population has shown a steady increase over the 36 \nperiods, ranging from 180.7 to 263.2.<\/code><\/pre>\n\n\n\nAs you can see, with a relatively simple implementation, it is possible to achieve results that add value to the BI system. It is important to note that the cost of using these models is associated with the length of the prompts and responses, so this factor should be taken into account when designing an automated data processing system.<\/p>\n\n\n\n
Improvement using the Wolfram plugin<\/h3>\n\n\n\n
The models that incorporate the use of plugins are not accessible, at the time of publishing this article, to the general public. But by using access to Beta versions, it is possible to significantly improve the previous example by leveraging Wolfram<\/strong>. This plugin provides computational and mathematical problem-solving capabilities to the general model. In this way, we can create prompts that, based on the data from the previous example, obtain information such as: “What was the average price of gasoline in the 70s and 80s?”<\/em> This question would not be answered by conventional OpenAI models, but with the use of the Wolfram plugin, we will obtain an accurate response:<\/p>\n\n\n\n
\nThe average price of gasoline in the 1970s was approximately \n234.97, while in the 1980s it was approximately 252.15.<\/code><\/pre>\n\n\n\nConclusion<\/h2>\n\n\n\n
As seen in this article, while services like Chat GPT, based on natural language processing models, have become popular in a short period of time, the advantages they can bring when integrated with BI systems are remarkable. Even though the article focuses on the use of Open AI technology, the Ignos<\/a> team has tested other popular models (including open-source ones) that can yield similar results.<\/p>\n\n\n\n
If you want to implement the combination of Business Intelligence and tailored Chat GPT in your organization, our team can assist you.<\/p>\n\n\n\n