Is SaaS reporting truly enough in today's data-driven world?

[hornj]

Traditional SaaS reporting often involves a manual, time-consuming process of selecting and organizing data to generate insights. However, the logical way of building reports is just a fraction of what we can truly show our users. Imagine having a database with 150 tables – the sheer number of combinations for joins is staggering.

Using the formula for combinations, we can calculate the total number of potential joins:

C(n, k) = n! / [k! * (n - k)!]

For our database scenario:

n = 150 (total number of tables)

k = 2 (number of tables selected for each join)

Plugging these values into the formula:

C(150, 2) = 150! / [2! * (150 - 2)!]

After computation, we find there are 11,175 possible combinations of joins for our database.

This complexity highlights why AI-powered reporting is superior. AI algorithms can swiftly navigate through vast amounts of data, identifying patterns and generating meaningful insights without being bogged down by manual processes. With AI, we can uncover correlations, trends, and outliers that might have otherwise remained hidden.

 

[nucleous]

@hornj Ok. Go on. So what is your suggestion?

 

[hornj]

@nucleous In our conversations with numerous product and engineering professionals grappling with the challenges of creating and maintaining reports, it's evident that the demand for flexible data analysis continues to persist. In this scenario, I suggest considering the implementation or integration of an AI-powered reporting tool directly into your systems. Such a tool would empower your customers to effortlessly explore their data by posing natural language queries.

Thanks to the advancements in LLM's this vision is now achievable. Though LLM are good at solving natural language to SQL it requires lot of effort under the hood to provide the right context, we have been helping some SaaS business to get this feature on them at EnqDB.

https://www.enqdb.com/

 

[nucleous]

@hornj Agree with all of that.

You are saying that professionals need a lot of custom metrics for good reporting. And now with AI we can make custom analysis much more accessible to more people using natural language prompts. Let AI do the digging.

Follow up question: Which professional users specifically will get the most value out of early versions of this product? Why will they find it more useful than others? Is it advanced analysts or sales managers or who?