Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. And it doesn't stop there. Data evolves over time which means this analysis or analytics, as we call it, can give us new information throughout data's entire life cycle. Data is everywhere. You use and create data everyday. Have you ever read reviews of a product before deciding whether or not to buy it? That's data analysis. Or maybe you wear a fitness tracker to count your steps so you can stay active throughout the day. That's data analysis. But you don't just use data. You also create huge amounts of it every single day. Any time you use your phone, look up something online, stream music, shop with a credit card, post on social media or use GPS to map a route, you're creating data. Our digital world and the millions of smart devices inside of it have made the amount of data available truly mind-blowing. Here at Google we process more than 40,000 searches every second. That's 3.5 billion searches a day and 1.2 trillion searches every year. Here's another way to think about it. YouTube has almost two billion users. If YouTube users made up a country, it would be the largest in the world. All of that data is transforming the world around us. The publication The Economist recently called data the world's most valuable resource. It's easy to see why data analysts are so valued by their organizations. What exactly does a data analyst do? Put simply, a data analyst is someone who collects, transforms, and organizes data in order to help make informed decisions.
As you have been learning, you can find data pretty much everywhere. Any time you observe and evaluate something in the world, you’re collecting and analyzing data. Your analysis helps you find easier ways of doing things, identify patterns to save you time, and discover surprising new perspectives that can completely change the way you experience things.
Here is a real-life example of how one group of data analysts used the six steps of the data analysis process to improve their workplace and its business processes. Their story involves something called people analytics — also known as human resources analytics or workforce analytics. People analytics is the practice of collecting and analyzing data on the people who make up a company’s workforce in order to gain insights to improve how the company operates.
Being a people analyst involves using data analysis to gain insights about employees and how they experience their work lives. The insights are used to define and create a more productive and empowering workplace. This can unlock employee potential, motivate people to perform at their best, and ensure a fair and inclusive company culture.
The six steps of the data analysis process that you have been learning in this program are: ask, prepare, process, analyze, share, and act. These six steps apply to any data analysis. Continue reading to learn how a team of people analysts used these six steps to answer a business question.
An organization was experiencing a high turnover rate among new hires. Many employees left the company before the end of their first year on the job. The analysts used the data analysis process to answer the following question: how can the organization improve the retention rate for new employees?
Here is a break down of what this team did, step by step.
First up, the analysts needed to define what the project would look like and what would qualify as a successful result. So, to determine these things, they asked effective questions and collaborated with leaders and managers who were interested in the outcome of their people analysis. These were the kinds of questions they asked:
It all started with solid preparation. The group built a timeline of three months and decided how they wanted to relay their progress to interested parties. Also during this step, the analysts identified what data they needed to achieve the successful result they identified in the previous step - in this case, the analysts chose to gather the data from an online survey of new employees. These were the things they did to prepare: