- At the end of the day, 
for businesses,
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.
