GPT BI Prompt boards - Revolutionize Business Intelligence with Interactive GPT Crystal Ball

Are you curious to know how Future of Business Intelligence going to be? How GPT can Provide Actionable Insights and Driving decision making faster?

In today's data-driven world, businesses and organizations rely heavily on dashboards to make informed decisions. Dashboards are often used to visualize key performance indicators (KPIs) and other metrics, but they can be limited in their ability to provide actionable insights.

Traditional dashboard charts can be limiting in their ability to provide interactive and engaging data analysisLimited interactivity: Traditional dashboard charts are often static, offering limited interactivity or the ability to explore data in-depth. This can limit a user's ability to identify patterns and trends within the data.

Lack of personalization: Traditional dashboard charts typically offer a one-size-fits-all approach to data analysis, which may not meet the unique needs of individual users or departments. This can limit the effectiveness of the dashboard in providing actionable insights.

Limited scope: Traditional dashboard charts are often designed to present high-level information, and predefined set of data. This can limit the effectiveness of the dashboard in identifying key trends or patterns.

Time-consuming to create: Traditional dashboard charts can be time-consuming to create, requiring significant manual input and formatting. This can limit the agility of the dashboard in responding to changes in business needs.

May not be visually engaging: Traditional dashboard charts may not be visually engaging or appealing to users, which can limit their effectiveness in communicating insights and driving user adoption.

In short, these are prebuilt for human to interpret and extract info. To make meaningful info, we need to do deep analysis and summarize that we need to find out..

Let us see what happens when human access traditional Dashboard:

Is it human interpretation dependable? still its called Business intelligence? Traditional dashboard charts often require significant time and effort to interpret, especially when dealing with large and complex datasets.

Users may need to spend time manually analysing data points and identifying trends, which can be a time-consuming process. This can be particularly challenging when dealing with multiple data sources or complex data structures.

Furthermore, traditional dashboard charts often require users to have a strong understanding of the underlying data and metrics. This can be a challenge for users who are not data experts or who are not familiar with the specific metrics being used in the dashboard. It can also lead to misinterpretations or misunderstandings of the data, which can lead to poor decision making.

BI GPT Prompt boards

Welcome to new world with chart less BI GPT Prompt boards. with the emergence of GPT (Generative Pre-trained Transformer) models, there is now a new way to get insights from dashboards.

GPT models are capable of analyzing data directly from dashboards and providing actionable insights. This means that users can receive insights and recommendations from their dashboard in real-time, without having to manually interpret the data.

let's say a company top level execute a dashboard to track customer feedback. Traditionally, the dashboard would display charts and graphs of the feedback data, and the user would need to interpret the data and identify trends and insights.

With GPT, the dashboard could provide insights directly to the user. For example, the dashboard could generate a summary of the most common feedback themes and recommend actions to address these themes. The user could also ask questions of the dashboard, such as "What are the top three issues customers are experiencing?" and receive immediate answers.

Another advantage of using GPT for dashboard insights is its ability to perform natural language processing (NLP). This means that GPT can analyse text-based data and provide insights based on the content of the text. For example, a dashboard could analyse customer feedback comments and identify common themes and issues.

Key highlights of Using GPT for Dashboard Insights:

• Real-time insights and recommendations
• Ability to perform natural language processing (NLP) on text-based data
• Saves time by eliminating the need for manual interpretation of data
• Improves decision-making by providing more informed insights

Examples of Using GPT for Dashboard Insights:

• Generating a summary of common feedback, trends and recommended actions
• Getting insights about data in real-time
• Getting focussed areas where top level executives needs to pay attention or informed decesioning

Solution - How it works?

LlamaIndex will transform your original document data into a query-friendly vectorized index. It will utilize this index to find the most pertinent sections based on how closely the query and data match. The information will then be loaded into the prompt, which will be sent to GPT so that GPT has the background necessary to respond to your question.

After that, you may ask ChatGPT, given the feed in context

Ingestion of Custom data

This section dives into more detail on the steps necessary to ingest data.

LlamaIndex (GPT Index) is a software project that provides an interface to connect your LLM (Llama Language Models) with external data. It aims to provide a centralized platform for developers to manage their LLM models, test and deploy them, and access external data sources easily.

LlamaIndex (GPT Index) is built on top of the GPT (Generative Pre-trained Transformer) architecture, which is a type of deep learning algorithm used for natural language processing tasks such as text generation, translation, and summarization.

By integrating external data sources, LlamaIndex (GPT Index) enables developers to create more powerful and versatile LLM models that can provide more accurate and meaningful insights for a wide range of applications such as chatbots, virtual assistants, and text-based analytics. lamaIndex (GPT Index) is an open-source project, and its source code is available on Github for developers to use, modify, and contribute to.

How about SQL based Data ? DatabaseReader?

There are many sort of data that can be put on it. In this case, we will use DB reader. DatabaseReader class defined in the llama_index.readers.database module, which provides a way to read data from a database using SQL queries.

To use DatabaseReader, you first need to instantiate the class with the necessary configuration parameters for your database, such as the hostname, username, password, and database name. Then you can use the execute_query() method to execute SQL queries on the database and retrieve the results.

Query data

Once we’ve ingested the data, we can now use it in a chatbot interface. In order to do this, we will use the ChatVectorDBChain. In order to customize this chain, there are a few things we can change.

Let us test drive with 3 different use cases ..

Use case 1 - Banking KPI Insights

Let us first look at what sort of data we have. Here is the raw data in the database.. and will query directly, index it and interact

Question-Answering Prompt

This is the prompt that takes in documents and the standalone question to answer the question. This can be customized to give your chatbot a particular conversational style

Question-Answering (QA) prompt is a type of text input used to generate answers to questions using natural language processing (NLP) models. QA prompts typically consist of a question or a prompt phrase followed by a placeholder for the answer, which is then filled in by the NLP model based on the context of the prompt and the available information.

For example, a simple QA prompt might be: "What is the capital of Solar System? Answer: [insert answer here]". When this prompt is fed into an NLP model, the model would use its understanding of the English language and its knowledge of facts and entities to fill in the answer placeholder with the correct response, which in this case would be "Sun".

QA prompts can be used for a wide range of applications, such as chatbots, virtual assistants, and search engines. They are particularly useful when there is a large amount of information available and users need to quickly find specific answers to questions without having to manually sift through the data themselves

Let us make into use. Instead of glancing the charts, we ASK GPT, tell me which is the te best branch?

“Considering Branch waiting time, feedback score., tell me the best branch”

Let us ask more.. we need more info based on the past monthly data

“Given data showing month, best branch name, waiting time, feedback score. Please keep only one row for each month and tell the best branch”

 Use Case #2 : HR? Employee Care takers

Let us look at the underlying – what sort data we have from DB

Once creates the vector, start asking GPT directly

Tell me who needs Tender care?.. I mean who are not being well.

Let us dig deeper.. let us ask GPT… GPT, “based on the started working date info, can tell me who are the best employees. I mean those who started working from 2015 onwards and promoted every 2 years. Please include start date info in the responses”

Let us stitch to different Domain …

Use Case 3 : Banking Dashboard

Look at the underlying data what we have:

GPT, tell me, trends can be observed from the data

Here formatted version of GPT API Response :

<quote>

Based on the data provided, we can make the following observations about the trends in the different categories:

Corporate Banking has seen steady growth in deposits and loans over the past five months, with a consistent increase in net interest margin, return on assets, return on equity, and efficiency ratio. The bank has been able to increase revenue and profit margin steadily over this period, with a growth rate of 5% per month.

Retail Banking has seen a decrease in total deposits and loans in the past two months, but with a slight recovery in May. The net interest margin, return on assets, and return on equity have also decreased slightly in the past two months, while the efficiency ratio has increased. The bank has been able to maintain its revenue and profit margin, with a growth rate of 2% per month.

Wealth Management has shown consistent growth in total deposits over the past five months, with a healthy profit margin of 24%. The annualized growth rate has been consistent at 19% per month. Customer retention rate has been consistently high at 94% or above.

Open API Banking has been growing rapidly, with revenue increasing from $10M in January to $20M in May, and the number of API calls increasing from 1 million to 2 million during the same period. Profit margin has also increased from 30% to 40% during this period. Gross margin and customer retention rate have consistently remained high.

Mobile Banking has been growing steadily, with revenue increasing from $50M in January to $100M in May, and the number of active users increasing from 1 million to 1.4 million during the same period. Profit margin has also steadily increased from 25% to 29% during this period. Gross margin and customer retention rate have consistently remained high.

Overall, the trends suggest that the bank has been able to maintain a healthy growth rate in most of its business categories, with steady increases in revenue and profit margin. The bank has been successful in retaining its customers in all categories, with high customer retention rates.

<unquote>

Let us ask to support with % values

Let us see if GPT can compare the channel adoption say API /Partner channel vs Mobile Banking

Ok. Compare it in the last 3 months

how about Customer Retention Rate?

From user perspective :

With Traditional Dashboard, its mega process

With GPT, you just ask. Like the crystal ball

Overall, GPT has the potential to revolutionize the way we use dashboards to gain insights and make decisions. By providing real-time insights and recommendations, GPT can help businesses and organizations make more informed decisions and improve their performance. As the field of GPT continues to evolve, we can expect to see even more powerful insights and recommendations coming from our dashboards.

Summary: GPT can be a valuable tool for CX executives, allowing them to quickly gain insights and make informed decisions. By generating concise summaries and recommendations, GPT helps CX executives save time and drive business growth.

So we have seen, using advanced technologies such as GPT can significantly reduce the time and effort required to interpret data. GPT models can generate interactive and dynamic visualizations, which allow users to explore and analyze data in real-time. This can help users to identify trends and patterns more quickly, and can also provide more personalized and user-friendly experiences.

Overall, the time and effort required to interpret traditional dashboard charts can be a significant challenge for many businesses. By leveraging GPT models to generate interactive and engaging visualizations, businesses can reduce the time and effort required to interpret data, and gain deeper insights into their data more quickly.

Let give the data the chance to talk by itselt!!