Updated on May 10, 2024

Data today has become the lifeblood of many enterprises. According to this report, the world generates 328.77 million terabytes of data every single day!! Google alone processes 3.5 billion searches daily, amounting to 20 petabytes of data.

There may be a myriad of reasons for this increased data deluge, including the explosion in the number of connected devices, the rise of digital services, and the increased adoption of data-driven business models. As more and more aspects of modern lives become digitized, the trail of data we leave behind grows larger and more intricate.

But it’s not just the sheer volume of data that poses a challenge for today’s enterprises, it is also the increasing complexity of this data.

 “Every company has big data in its future, and every company will eventually be in the data business.” – Thomas H. Davenport, American academic and author specializing in analytics, business process innovation, and knowledge management.

Data no longer exists in neatly structured formats such as spreadsheets or relational databases. The data landscape today has sources that span across the digital realm, from social media posts to video streams and sensor logs.

Data is also multidimensional these days – meaning there are intricacies introduced such as data types, formats and the intricate relationships that exist between different data points.

And lastly, the pace at which data is created and consumed has reached to near real-time levels. Businesses are under constant pressure to swiftly react to changing market conditions, operational dynamics, and customer preferences.

Where there is data, there are spreadsheets, and analysts trying to make sense out of these spreadsheets. But here again, the challenges are aplenty. Some of them include:

i) Unstructured Data

Unstructured data within Excel sheets will make it more and more complex in data management and analysis. As an example, there may be cells containing free-form text descriptions, or a mix of numeric and alphanumeric values that can make it difficult to perform calculations.

Having multiple data points crammed together into a single cell, such as “Eric Bahn, Consultant, Munich” can hinder data extraction and interpretation. Also, there is the lack of standardization of naming conventions or data validation rules can result in inconsistencies in data entry, further complicating data processing and reporting.

The solution to these problems? Implementing structured data formats, consistent naming conventions, and setting up clear data validation rules will help you effectively manage these Excel spreadsheets.

ii) Data no longer in Silos

In the beginning, a single Excel sheet will have been sufficient to serve as the centralized repository for all the organization’s data needs. But, as the business expands and there are more and more stakeholders, the data becomes scattered across multiple sheets, folders and even different systems.

The primary difficulty that organizations will face from this data sprawl is the difficulty in finding and accessing relevant information. The necessary data could be buried in countless tabs, or be present in different spreadsheets that are maintained by different team members.

iii) Difficult to get up to date view of data

In an enterprise, often, there are multiple multiple stakeholders who make edits and modifications to a myriad of Excel sheets that they have created. The number of tabs and filters keeps multiplying, and it becomes more and more difficult to get a cohesive and up-to-date view of the data.

Each stakeholder, in this case, may apply their own filters, sort the data based on their own specific requirements, or introduce custom formatting or formulas. This lack of standardization can lead to confusion, inconsistencies and potential data integrity issues.

iv) Problems with data access

When there are multiple stakeholders involved in accessing and modifying the same data in an Excel sheet, chances of accidental deletion, overwriting of data, or unintentional formula changes are quite significant. In industries such as healthcare, finance or scientific research, data integrity is critical, and this can have severe implications.

Concurrent access to data risks data corruption and overwriting. If there are no access control measures in place, then there is also the risk of privacy and security. Excel is not known for its collaboration capabilities, so this makes tracking changes and version control even more cumbersome. 

As you can see, the problem statement with using Excel sheets to store your enterprise data is quite complex, but it is also a double edged sword, given how enterprises cannot live without Excel sheets.

As an organization that lives and breathes Generative AI, we here at Kommunicate saw that this was a problem that can be solved using chatbots. Want to see how? Read on.

Chat with an Excel sheet

So today, we are introducing a solution where enterprises get the capability of chatting with an Excel sheet, using ChatGPT and Retrieval-Augmented Generation (RAG).

For the uninitiated, RAG is a technique used by Large Language Models to enhance the accuracy and reliability of the answers that they provide. The LLMs do this by fetching the data from external sources, which is different from their training data. 

While we don’t want to get too deep into what RAG is, we here at Kommunicate have used a combination of LLM and a Python library to solve this problem of using a chatbot to talk to an Excel sheet.

How will this help your business or enterprise? Let us take a look at a few advantages and disadvantages of chatting with an Excel sheet.

Advantages of chatting with an Excel Sheet

Why does an enterprise need to use a chatbot and chat with an Excel sheet?  The short answer is to save time and get more efficient. 

Here are some of the advantages of having this capability:

1. Data accessibility:  When users upload their Excel data, chatbots can access and analyze specific information that is relevant to the user’s needs. This leads to more personalized responses.

2. Intuitive Experience: When you integrate an LLM with an Excel sheet, users can communicate clearly with the chatbot in natural language, making the overall experience more user-friendly.

3. Data analysis: The chatbot can use the LLM’s capabilities to give insights and recommendations based on the Excel data that the customers have uploaded, giving valuable information without a lot of manual effort.

Challenges of chatting with an Excel sheet

Although uploading an Excel file and allowing customers to chat with it may sound exciting, there are a few challenges with this ability. They are:

1. Data privacy concerns: When you allow users to upload sensitive data, there are privacy and security issues that require set measures so that you protect user information.

2. Model limitations: LLMs are powerful, but they still come with their own set of limitations. These limitations include the ability to understand complex data structures or perform advanced calculations, which may restrict the functionality of the chatbot.

3. Technical challenges: When you want to integrate an LLM and integrate it with an Excel upload feature, to ensure seamless communication between the two you need to be technically really sound.

4.  Duplicacy of data: When there is inconsistent or flawed data presented, there is a possibility that enterprises that base their decisions on this data can go wrong. When multiple users edit the same Excel file simultaneously, it can also result in hallucinations, as it introduces redundant or contradictory changes. To mitigate this, enterprises must implement data validation rules, version control, and deduplication processes in order to maintain data integrity.

As you can see, although there are a few difficulties when it comes to using an LLM to chat with an Excel sheet, the pros far outweigh the cons.

Want to see these chatbots in action? Let us now look at 3 use cases where the chatbot that we built can actually chat with Excel sheets to solve real-world use cases.

Real-life examples of chatbots deployed on Excel sheets

1. Finance

Financial institutes have some of the most complex spreadsheets, which are usually spread across different departments. Imagine being part of a bank where there are four different banking agents, who are responsible for a large number of account holders from customers.

Now, if you want to find the agent(s), who has managed accounts with a balance exceeding $30,000, that would mean you have to apply multiple filters and then sort through the data to find the agent’s name.

Or, you can simply upload the document to the chatbot and ask it the question “ Find me the agent who has managed accounts with a balance exceeding $30,000 for the last 3 months”

The chatbot will do the heavy lifting and give you the response, as shown below:

2. Manufacturing

Manufacturing is a sector that is rapidly adopting technology, and, as this traditional sector moves into the digital realm, it will see a rise in the number of executives using Excel sheets. Imagine you are on the shop floor of a car service showroom, and you have 4 car service technicians who generally service vehicles.

Now, if you want to find out the chassis number of the car serviced by a particular service agent, previously, you had to apply a filter running his name and then hunt through the sheet looking for your data.

Enter chatbot trained on excel. Using a simple prompt “Find me the chassis number of the car serviced by Sam”.. the chatbot will fetch the data for you, saving you precious minutes. These minutes, when compounded over a long period of time, will translate into hours of productive time saved, and thus greater efficiency and cost savings.

3. Retail

Retail is another sector where spreadsheets are the norm. There are usually hundreds of store agents or clerks who handle customer, inventory and shipping data, and this inevitably leads to a bunch of Excel sheets stored on different systems.

To counter this, we can simply use a chatbot that can be trained on Excel data. In this scenario, you are running a shop where you have different store clerks in charge of different items. If you want to find out the total sales done by one of your agents, previously, you had to filter out that agents name and then do a “Sum” calculation.

But say goodbye to all those complicated methods, thanks to the magic of RAG. As you can see, you can ask the chatbot a simple question, “What was the amount of Sales done by Estelle last week?” and the chatbot will give you the answer.

We have just given you a sample, and the limits of this technology are only your imagination. Chatting with an Excel sheet will have significant impact on the time savings of your business. With chatbots answering directly from your Excel sheets, you get a more personalized experience, which will lead to greater user satisfaction. Chatbots can also use LLM’s data analysis capabilities to extract valuable information for the customers without a lot of manual effort.

The best part about chatting with an Excel sheet? Savings in time. According to this report by Ventana research, people spend more than 18.1 hours (close to two days of productive work) on the maintenance of a wide variety of Excel spreadsheets that they have created at their organization. Imagine if this time is instead invested in doing actual, productive work. That’s what chatting with an Excel sheet will give you the capability of.

Parting words

And there you have it – the ability to chat easily with your Excel Sheet using ChatGPT. The solution we built requires some creativity, but, once refined, we are sure that this technology will be a game changer for many enterprises.

As you can see, the use cases make it a strong compelling case for your enterprise to adopt this technology, and we are sure there will be hours of saved time and improved efficiency using Kommunicate’s Chat with Excel feature.

Keep watching this space as we add more details, and get ready to chat with your Excel sheet.

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