1.2.3.Thinking about the Outcomes - sj50179/Google-Data-Analytics-Professional-Certificate GitHub Wiki

Using data to drive successful outcomes

As a reminder, they're curiosity, understanding context, having a technical mindset, data design, and data strategy.

Data-driven decision-making involved using facts to guide business strategy.

It gives you greater confidence about your choice and your abilities to address business challenges. It helps you become more proactive when an opportunity presents itself, and it saves you time and effort when working towards a goal.

Curiosity and Context - The more you learn about the power of data, the more curious you're likely to become. You'll start to see patterns and relationships in everyday life, whether you're reading the news, watching a movie, or going to an appointment across town. The analysts take their thinking a step further by using context to make predictions, research answers, and eventually draw conclusions about what they've discovered. This natural process is a great first step in becoming more data-driven.

Having a technical mindset - Data analysts have gut feelings too. But they've trained themselves to build on those feelings and use a more technical approach to explore them. They do this by always seeking out the facts, putting them to work through analysis, and using the insights they gain to make informed decisions.

Data design - which has a strong connection to data-driven decision-making. To put it simply, designing your data so that is organized in a logical way makes it easy for data analysts to access, understand, and make the most of available information. And it's important to keep in mind that data design doesn't just apply to databases. If you make decisions that are informed by data, you are more likely to make more informed and effective decisions.

Data strategy - which incorporates the people, processes, and tools used to solve a problem. This is a big one to remember because data strategy gives you a high-level view of the path you need to take to achieve your goals. Also, data-driven decision-making isn't a one-person job. It's much more likely to be successful if everyone is on board and on the same page, so it's important to make sure specific procedures are in place and that your technology being used is aligned with your data-driven strategy.

Real-world data magic

Here at Google, our mission is to organize the world's information and make it universally accessible and useful. All of our products, from idea to development to launch, are built on data and data-driven decision-making.

The HR department wanted to know if there was value in having managers. Were their contributions worthwhile? Or should everyone just be an individual contributor? To answer that question, Google's people analytics team looked at past performance reviews and employee surveys. The data they found was plotted on a graph because as you've learned, visuals are extremely helpful when trying to understand a problem or concept. The graph revealed that Googlers had positive feelings about their managers, but the data was pretty general and the team wanted to learn more. So they dug deeper and split the data into quartiles. A quartile divides data points into four equal parts or quarters. Here's where the really cool stuff started happening. The data analysts discovered that there was a big difference between the very top and the very bottom quartiles. As it turned out, the teams with the best managers were significantly happier, more productive, and more likely to want to keep working at Google. This confirmed that managers were valued and make a big difference. Therefore, the idea of having only individual contributors was not implemented. But there was still more work to do. Just knowing that great managers create great results doesn't lead to actionable insights. You have to identify what exactly makes a great manager, so the team took two additional steps to collect more data. First, they launched an awards program where employees could nominate their favorite managers. For every submission you had to provide examples or data about what makes that manager great. The second step involved interviewing managers who were graphed on the top and bottom quartiles. This helped the analytics team see the differences between successful and less successful management behaviors. The best behaviors were identified as were the most common reasons for a manager needing improvement. The final step was sharing these insights and putting a procedure in place for evaluating managers with these qualities in mind. This data-driven decision continues to create an exceptional company culture for my colleagues and me. Thanks, data.

Another interesting example comes from the nonprofit sector. Nonprofits are organizations dedicated to advancing a social cause or advocating for a particular effort, such as food security, education or the arts.

In this case, data analysts researched how journalists can make a more meaningful impact for the nonprofits they would write about. Because journalists write for newspapers, magazines, and other news outlets, they can help nonprofits reach readers like you and me, who then take action to help nonprofits reach their goals. (...) The data analysts used a tracker to monitor story topics, clicks, web traffic, comments, shares and more. Then they evaluated the information to make recommendations for how the journalists could do their jobs even better. In the end, they came up with some great ideas for how nonprofits and journalists can motivate people everywhere to work together and make the world a better place.

Test your knowledge on outcomes

Question 1

Fill in the blank: Curiosity, understanding context, and having a technical mindset are all examples of _____ used in data-driven decision-making.

business strategies

data models

thought processes

analytical skills

Correct. Curiosity, understanding context, and having a technical mindset are all examples of analytical skills used to make data-driven decisions.

Question 2

Surveying customers about their preferences and using that information to inform business strategy is an example of data-driven decision-making.

True

False

Correct. Surveying customers about their preferences and using that information to inform business strategy is an example of data-driven decision-making.

Question 3

In data analysis, which analytical skill involves the management of people, processes, and tools?

Data design

Data analytics

Data control

Data strategy

Correct. Data strategy involves the management of the people, processes, and tools.