Advantages & Pitfalls of Using ChatGPT to Interface with Your Enterprise Data

Extracting Insights from Your Data with GPT-4

This blog is the first of a two-part series and explores the advantages and potential pitfalls of using ChatGPT and GPT-4 with your enterprise data. The second blog explores how DvSum helps you maximize the advantages of GPT-4 and overcomes the pitfalls.

First, thanks to OpenAI for ChatGPT writing portions of this blog which are called out below. 

The challenge: easily extracting real-time, reliable insights from your data

A major challenge in the enterprise today is that decision-makers are drowning in data, yet paradoxically starving for insights. That’s because analyzing data requires technical skills, and thus there is a dependency on IT and Data Analysts. Furthermore, there’s a lack of trust in data because of data quality issues. Data & Analytics leaders are struggling to prove value to their organizations, according to a recent Gartner survey.

‘Less than half of data and analytics (D&A) leaders (44%) reported that their team is effective in providing value to their organization, according to a new Gartner, Inc. survey.’

Source: Gartner online survey, Fall 2022, 566 Data &Analytics leaders

A Better Way to Insights: AI-powered Chat – with Caveats to Address

At DvSum, we believe AI-powered chat is the key to democratizing business insights and unleashing agility with data. Therefore, we prompted ChatGPT: “let’s talk about using GPT-4 as an interface to a company’s internal enterprise data,” to which ChatGPT replied:

Using GPT-4 as an interface to a company’s internal enterprise data can offer several benefits such as improved accessibility, faster decision-making, and more efficient workflows. Here are some potential ways this could work:

1. Natural Language Querying: GPT-4 can be used to query internal enterprise data by employees using natural language. This can help make the data more accessible to all who may not be as familiar with the technical jargon or data structures.

2. Automated Reporting: GPT-4 can be used to generate automated reports based on specific queries, saving time and effort for employees who would otherwise need to manually compile data and create reports.

3. Personalized Recommendations: GPT-4 can analyze a user’s past behavior and data consumption patterns to make personalized recommendations for relevant data sources or insights.

4. Automated Data Analysis: GPT-4 can be trained to perform automated data analysis and provide insights based on trends, patterns, and correlations in the data. This can help companies make data-driven decisions faster and more efficiently.

Of course, there are also potential drawbacks to using GPT-4 as an interface to internal enterprise data. One major concern is data security and privacy, as sensitive enterprise data must be protected from unauthorized access or disclosure. Another potential issue is the accuracy of GPT-4’s responses, which can vary depending on the quality of the training data and the specific context in which it is used. Therefore, it’s important to carefully evaluate the risks and benefits before implementing GPT-4 as an interface to internal enterprise data.

Great points. In summary:

1. Advantages

• An easy chat interface
• Automated analysis and reporting
• Personalized recommendations 

2. Pitfalls

• Accuracy of the data within the specific context
• Data security: data access rights and protecting sensitive data

An Example of DIY with ChatGPT

For those of you interested to go for it, Microsoft was kind enough to offer some practical strategies in their blog about how to use ChatGPT in a ‘do-it-yourself” (DIY) fashion using Microsoft products for extracting insights from enterprise data. So once again, we asked ChatGPT, referencing that blog’s link, about any additional points that may need to be scrutinized. ChatGPT replied:

Some of these challenges include:

1. Data Integration: One of the biggest challenges is integrating enterprise data with GPT-4 to make it accessible through the interface. Companies may need to overcome technical hurdles and ensure that the data is properly formatted and organized for use with GPT-4.

2. Accuracy and Reliability: GPT-4’s responses may not always be accurate or reliable, which can be a problem when employees rely on the interface to make important decisions. Companies may need to invest in additional training data or implement safeguards to improve the accuracy of GPT-4’s responses.

3. Privacy and Security: As mentioned earlier, privacy and security are major concerns when it comes to using GPT-4 as an interface to internal enterprise data. Companies must ensure that sensitive data is properly protected and that access to the interface is tightly controlled.

4. User Adoption: Finally, getting employees to adopt the new interface can be a challenge. Some employees may be resistant to change or may not see the value in using GPT-4 to access enterprise data. Companies may need to invest in training and change management to encourage adoption and ensure that the interface is being used effectively.

Summary of advantages and pitfalls using ChatGPT for your enterprise data

So adding to our prior list we now have:

1. Advantages 

• An easy chat interface
• Automated analysis and reporting
• Personalized recommendations 

2. Pitfalls

• Data integration
• Accuracy and reliability of the data 
• Accuracy of the data within the specific context
• User adoption 
• Data security: data access rights and protecting sensitive data

Conclusion

As we see, ChatGPT and GPT-4 offer great promise in interfacing with your enterprise data. But there are important pitfalls to overcome. In the next blog of this series, we will explore how DvSum helps overcome these pitfalls.