VIDEO: Being Data-Driven: Myths and Facts

VIDEO: Being Data-Driven: Myths and Facts

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Being Data-Driven: Myths and Facts

According to a Fortune 1000 executive survey, in 2021, 99% of organisations actively invested in data-driven capabilities, and the pace has accelerated for 9 out of 10 of them.

And yet, only 24% of the executives think their organisation is data-driven! But what does it mean to be data-driven? And how can you actually become data-driven?

LIST OF RESOURCES
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Resources mentioned in the Video:

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We all need to make decisions about the future, so we can plan resources accordingly. But what does it take to make well-informed, data-driven decisions? Can you become better at it? Do you need to know lots of Stats? Do you need to be a Maths prodigy?
What about code? Do you even need it?!

There are 7 commons myths when it comes to being data-led:

1. Data-driven myth number one:

Nowadays, predictions are made by data teams, so I don’t need to worry about being data-driven.

2. Data-driven myth number two:

Most organisations are data-driven. People believe that other organisations are more data-driven than they actually are.

3. Data-driven myth number three:

My organisation doesn’t have enough (good quality) data, so I can’t make predictions.

4. Data-driven myth number four:

I need to know how to code to make forecasts.

5. Data-driven myth number five:

“Superforcasters” need to be great at maths and stats.

6. Data-driven myth number six:

But, some of us are not that good at making forecasts

7. Data-driven myth number five:

You need a team of experts to make good predictions

It turns out that good data-driven forecasters have a few things in common:

Willingness to change their mind…often!

Willingness to change your mind often means that you have to be willing to see things from different perspectives and question everything – yourself included!

Basic numeracy (data literacy).

Basic numeracy means that you should have knowledge of basic arithmetic. In most cases, arithmetic is covered in the first two years of high school.

In fact, one of the mathematicians in Philip Tetlock and Dan Gardner research, who happened to be a super forecaster, made a point of using nothing more than the most basic of arithmetic to illustrate that it’s not about the maths!

Thinking in shades of maybe (data literacy).

Thinking in shades of maybe aims to highlight the fact that nothing is absolutely certain, and it asks of us to assign probabilities to outcomes.

So there you have it, 7 common myths to becoming data-driven, and 3 things that good data-driven forecasters have in common. So you can become data-driven too!

We would love to hear your thoughts in the comments below! How are you becoming data-driven?

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