What on Earth Even is Data Science?
As a Data Science Consultant I have to stay on top of what's going on in my industry, which means I read a lot of articles, think pieces, and how-to's about Data Science. This much reading is great as it gives me a diverse set of views on the industry and the direction it's headed. But that diversity also highlights one overarching truth: everyone out there thinks Data Science is something different!
A big reason for this is that the term "Data Science" is rapidly being used to describe more and more things. When I first began my career seven years ago, the terms "Data Science" and "Data Scientist" were barely used at all. Then a few years ago these terms started to become very commonplace to describe a specific and highly specialized class of data practitioners. Today, Data Science is being used to describe almost anything data-related.
Frankly, it's ok that the term is getting watered down like that, that's just how language evolves. When I was in college the widely used term was "Analytics", and I'm sure a few years down the road, there will be a new buzzword that replaces Data Science to mean basically the same thing. What's really important is not to have an ultra-specific definition of Data Science, but to understand the larger concepts and how they can be used in your organization to have better information and make better decisions.
So then, what the heck are these concepts?
Well, for me it boils down to three key areas:
It's that simple.
But it's also extremely powerful when done properly. If you think about it, turning the secrets stored into your data into a compelling story that a human being can understand ultimately has the power to lead to significantly better performance for your organization. In my career I've seen it used to help construction companies make better contract bids, marketing teams target the right leads, and even baseball pitchers pick the right pitches to throw. Imagine what data could do in your organization.
Then when you're tired of just imagining, go ahead and reach out and we'll figure out how to get you there!
A big reason for this is that the term "Data Science" is rapidly being used to describe more and more things. When I first began my career seven years ago, the terms "Data Science" and "Data Scientist" were barely used at all. Then a few years ago these terms started to become very commonplace to describe a specific and highly specialized class of data practitioners. Today, Data Science is being used to describe almost anything data-related.
Frankly, it's ok that the term is getting watered down like that, that's just how language evolves. When I was in college the widely used term was "Analytics", and I'm sure a few years down the road, there will be a new buzzword that replaces Data Science to mean basically the same thing. What's really important is not to have an ultra-specific definition of Data Science, but to understand the larger concepts and how they can be used in your organization to have better information and make better decisions.
So then, what the heck are these concepts?
Well, for me it boils down to three key areas:
- Having good clean data you can use
- Using statistics to translate that data into valuable insight
- Presenting that insight to key stakeholders in a way that is equally accurate and accessible
It's that simple.
But it's also extremely powerful when done properly. If you think about it, turning the secrets stored into your data into a compelling story that a human being can understand ultimately has the power to lead to significantly better performance for your organization. In my career I've seen it used to help construction companies make better contract bids, marketing teams target the right leads, and even baseball pitchers pick the right pitches to throw. Imagine what data could do in your organization.
Then when you're tired of just imagining, go ahead and reach out and we'll figure out how to get you there!