ChatGPT, as an AI-powered tool, has shown great potential in helping businesses make sense of their data. While it may not replace a full-fledged data science framework just yet, it can certainly serve as a valuable addition to your organization’s data analytics toolkit and be used as a proof of concept for introducing AI-driven frameworks.
One of the most promising use cases for ChatGPT in data analytics is to transform data feeds into easily digestible content and insights. While ChatGPT can be utilized for various purposes in data analysis, such as writing Python code or creating SQL statements, its ability to convert complex data into simple, easy-to-understand concepts has significant potential.
In this blog post, we will explore how ChatGPT can be leveraged to create AI-driven automated conversational reports. To illustrate this, I have utilized a collection of sample data along with more complex data sets from Google Analytics and Sitecore CDP, developed using the principles of Data Mesh, to showcase the practical capabilities of ChatGPT. It’s worth noting that this post is based on the widely used GPT-3.5 model. However, the latest version, which is currently in preview, has the capability to handle significantly larger payloads. For instance, the “gpt-4-32k-0314” version can process eight times the amount of data compared to V3.5.
Let’s explore three common ways ChatGPT can help improve Conversational Analytics.
1. Predictive analytics: ChatGPT can be used for predictive analytics, such as predicting customer churn or forecasting future sales. It can also be used for anomaly detection, identifying unusual patterns or behaviors in data sets.
2. Data exploration: ChatGPT can be used to explore normalized data sets, helping businesses understand data insights in a conversational manner. Additionally, ChatGPT can assist in data visualization by generating graphs and charts based on the user’s queries. This can help users to better understand their data, identify patterns, and make informed decisions.
3. Data summarization: ChatGPT can be used to summarize datasets into key insights, helping analysts quickly understand the most important aspects of a data set without needing to sift through large amounts of data.
And a follow-up question:
Implementing ChatGPT into an organization’s analytics platform can be a straightforward process. However, it is crucial to take into account legal considerations and data governance principles that the organization must follow. Data privacy regulations vary from country to country, and it is essential to ensure that all data collected and analyzed by ChatGPT is compliant with these regulations. Additionally, data governance principles such as data quality, data security, and data access must be taken into account to ensure that the implementation of ChatGPT is both effective and secure. Organizations must have a clear understanding of the data they collect and analyze, the purpose of collecting it, and how it will be used before integrating ChatGPT into their analytics platform. Following the Data Mesh principles would ensure data compliance and will simplify AI adoption.
Beyond the legal and data governance implications, it is possible to integrate Analytics with AI Models using Azure Logic App and Microsoft Teams without writing any code. This integration enables bidirectional communication between the AI model and end-users through MS Teams. Users can ask questions in the Teams channel, which are then combined with formatted data streams from analytics data sources and sent to ChatGPT. The results are then posted back to the Teams Channel.
Integrating an AI model, such as ChatGPT, into an organization’s analytics platform has the potential to unlock the full power of AI and enhance decision-making processes significantly. However, if ChatGPT is not feasible, other AI tools, such as in-preview as this moment Azure Open AI Services, Azure Cognitive Services, Power BI or Microsoft Health Bot Service, can be considered to achieve similar benefits. There is an array of tools offered by Microsoft and other vendors that can help organizations securely analyze their web analytics data, and selecting the most appropriate model depends on the specific requirements.