they know one thing, that if they are unable to measure

something, they are unable to improve it.

And if they are unable to measure their costs,

they are unable to reduce them.

If they are unable to measure their profits,

they are unable to increase them.

So the first thing a company has to do

is to start recording information, start capturing data.

Data about costs, data about.. and then differentiated

by labor costs and material costs.

The cost to, how much it costs to sell one product

and the total cost.

And then you look at the revenue.

Where is your revenue coming from?

Is 80% of your revenue coming from 20% of your customers?

Or is it the other way around?

So first thing first, start capturing data.

Once you have data, then you can apply algorithms

and analytics to it.

So the first thing to do would be to capture data.

If you're not capturing it, start capturing it.

If you're capturing it, archive it.

Do not overwrite on your old data

thinking you don't need it anymore.

Data never gets old, data is always relevant.

Even if it's a hundred years old, 200 years old,

it is relevant to you and your firm and your success.

So keep data, capture it, archive it.

Make sure nothing goes to waste.

Make sure there's a consistency

so someone 20 years later trying to understand

that data should be able to do so.

So have proper documentation.

Do it now, put the best practices for data archiving

in place the moment you start a business.

And if you're already in business

and you haven't done it, do it now.

- Start measuring things.

Too many companies haven't measured things properly

for a decade and then they decide they want data science.

Data science inside a company

is only going to be as valuable as the data collected.

Garbage in, garbage out is a rule in any sort of analysis.

- If something is not measured,

it's very difficult to improve it or to change it.

So the very first step is measurement.

If companies have existing data,

then they should start looking at it and cleaning it.

If they don't have existing data,

then they need to start collecting it.

- I think to look for a team who love to work

as a data scientist.

- The first step is to have employees,

that they are interested in science.

Cause if you don't have interest in your company,

you will not have like engagement.

- Companies should remember that it's key

to have a team, so it's not one data scientist

but a team of them, that each of them have strengths

in different areas of data science.