I am a research student at a large educational organization.
There are many business intelligence tools in the market but only a few of them are well known. Why is AWS QuickSight not as popular as other business intelligence tools? What BI tool do you use and why?
Data Office Lead at a comms service provider with 501-1,000 employees
Nov 24, 2022
First of all, AWS QuickSight is a versatile BI tool and is cloud-native. Some of the reasons which discourage BI teams from using AWS QuickSight are I) AWS QuickSight access requires AWS account setup ii) AWS QuickSight usage requires some knowledge of setting up AWS resources and IAM policies iii) AWS QuickSight lic is split between read-only and author roles, so additional admin work iv) Using AWS QuickSight for building or accessing dashboards, both incur cost v) Being a cloud-native app steady internet connection is required for a smooth experience vi) AWS QuickSight doesn't have a desktop client vii) AWS QuickSight is fairly new the industry and still improving on features.
I may be wrong but to the best of my knowledge AWS QuickSight doesn't accept scripts in R or Python. Other BI tools do.
I am a user of both languages and somehow my machine learning (ML) projects can be easily implemented in a company with such user-friendly BI tools. I take them to be an entry point for managerial ML solutions (not so much for industrial solutions).
That's why I disregard BI tools that don't handle R and Python.
I work at a large financial organization.
I have been wondering what types of problems (=use cases) can be solved/optimized using the Edge AI technology in industries such as Banking and Finance, Power and Land and Agriculture?
I appreciate your thoughts and suggestions.
AI has already been used for decades in banking, finance and agriculture.
Credit and loan analysis use a lot of logistic regression to know who to loan money to. Finance uses AI for regression analysis of forecasts and optimum stock portfolios, agriculture to predict production and supplies needs based on the amount of rain forecasted.
What we have is the evolution of tools, methods and techniques. We no longer need cubes. Although Hadoop is meant for safe storage at low cost and not for AI purposes, tools have evolved and new tools arose providing the speed required for AI analysis.
We have evolved in terms of data governance with the use of DMBoK (Data Management Body of Knowledge), visual planning frameworks like the Machine Learning Canvas, Self Service BI approaches stimulating collaboration amongst users. We now have tools, practices, frameworks and enough manpower that makes the job more challenging.
We have been using AI in those sectors for quite a while, but now we have the momentum to reach a different level of data thinking organizations.
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