The three terms that we're going to walk through are digitization, data, and analytics. Now, you're going to see as we walk through each one of these concepts, there is overlap between all three of them. They will rely on each other, and you're going to have to use them in some form or another (that's perfectly fine). So, at the end of this guide, if they all seem very similar, that is natural. I want you to understand what they are, and specifically how they're different from each other.

Let's start with the first one. Digitization, I think the easiest way of understanding it, along with each one of the other concepts, is to walk through a real world example. Digitization can be represented by imagining going to a doctor's office. You walk in and they would look you up in a giant set of cabinets. They are full of all these paper records with your medical history. Every time you've gone into that doctor, they took notes and they can see your medical history with those set of paper records.

What digitization did, is it digitized each one of those records, so now instead of going in and them looking you up with paper records, they can perform a database inquiry in their computer and automatically pick those out.

Where this comes in very handy is when they want to transfer those data records. Before, they would have to do things like fax over your full file. Now what they can do is they can share it securely, so that if you go to another doctor, they can then immediately have access to your digital records.

The next concept we're going to walk through is data. Now technically, just about everything that you do on your computer is related to data in some form or another. Let's take an example of a sports team.

Years ago when a specific sports team (it could be football, it could be baseball, it could be basketball) wanted to place their players in a specific position on the field, they usually were using their own gut instincts.

If it was baseball, they would try to position the players on the field with where they expected the hitter to hit the ball. Now with data, what they can do is they can go back and look through historical records and instead of just going with their gut instinct, they can actually use digital proof for how to make a recommendation.

The last term is the term analytics. Analytics is a very broad concept, it relies on data very heavily and one of the best examples you're going to see of how to use analytics is actually in the form of how you interact with websites.

Imagine that you go on Facebook and occasionally you may notice that certain items on the page have been moved, so your suggested friends might be on the top right hand side for one day, and then it might be on the bottom left hand side the next day.

Well, what Facebook is doing is they're using analytics, so they're placing different components on the page and then they're tracking to see how you interact with those components. What they can do then is they can leverage your reaction, they can leverage the data, so that they can optimize their application so that people use it in the most efficient manner possible.