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  • - 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.

- At the end of the day, for businesses,

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B1 中級

企業應該如何開始數據科學[數據科學101]? (How should companies get started in data science [Data Science 101])

  • 80 9
    陳賢原 發佈於 2021 年 01 月 14 日
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