So three people can go to the same exact restaurant and look at the whole thing from a complete

different lens. One can go insane, I just want good food and good service. Another person can go

insane, I wonder if this waitress is going to do a good job because they used to be a waitress

and they're watching all their mannerisms. Another person can go insane, look at the way I walked

in, the menu, the chef, the plates, how they're lined up because they own a restaurant. Today,

I'm going to give you a lens to look at data in a way maybe you've never looked at before.

Okay, so stick around to Leverian, I'm going to give you PDF on how to go use this with your

employees, your team, your staff so you can get better data and get better results to drive your

company to newer heights. Let's get right into it. There's four different ways to analyze data.

The first kind is the descriptive analytics, then it's diagnostic analytics, then it's predictive

analytics, then it's prescriptive analytics. The first one, descriptive analytics is basic.

You're researching and summarizing historical data in order to identify patterns.

How many sales that we do in a month of January, compared to February, compared to March,

it's very basic, it's very descriptive. Number two is diagnostic, which is understanding why

behind what happened. Why do we have a bad March versus last year? What happened? What were the

events? What happened? Did COVID change it? Did new laws come out? Did we lose the customer?

Did we gain the customer? What happened there? So it's the why? Number three, predictive analytics

study in those to predict what's about to happen next. Well, the way we did last October and the

way we're trending this Q3 versus last Q3, this October, we're probably going to end up only

doing $2 million. We're only going up doing $3 million or we're poised to have the biggest

month ever because we have this. But if you look at the data and you see patterns that you're

not happy about your numbers and you look at the predictive analytics saying, oh my gosh,

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