In this article, I would like to talk about 5 data science use cases for every business.
Watch this episode on YouTube here.
Every organization, every business is trying to make the most of available data, to get a competitive advantage.
But you don’t get data science projects out of anywhere…
You need to identify and validate data science use cases, before getting into typical data science lifecycle.
So identifying and validating the data science use cases is a task in itself.
Today, I would like to talk about 5 common areas to look for data science use cases, which are relevant to any business.
The first area to investigate for data science use cases is Customers.
Who are your customers?
What they generally buy or might buy?
How data can help you better understand your customers?
The second area to look into for data science use cases is Products and Services.
Which products and services are performing good or bad and why?
How data can help you understand your offerings?
How can you make your products & services smarter?
The third area to find data science use cases is Operations.
Which processes are troublesome, what are the frequent interruptions?
How you can make your processes smooth?
How data can help you to optimize your operations?
The fourth area I would like to talk about is Decisions.
Do you collect and analyse the effects of a decision made for your business?
Do you look into historical data while making decisions?
How data can help you make better decisions?
The fifth and final area to look for data science use cases is Data itself.
Do you treat your data as a real asset?
Are you monetizing your data?
Can you build a data product which can help your customers?
So these are the 5 areas to look for data science use cases in any business.
Did you find the content useful? Let me know in the comments section.
Like, share & subscribe to my YouTube channel (ankitrathi.com/youtube) to get the latest updates.
Ankit Rathi is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.
If you have any questions or comments, click the "Go To Discussion" button below!