New Perspective of Data Analytic

Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making.

A data analyst is an explorer, a detective, and an artist all rolled into one. Analytics is the quest for inspiration. You don’t know what’s going to inspire you before you explore before you take a look around.

When you begin, you have no idea what you’re going to find and whether you’re even going to find anything. You have to bravely dive into the unknown and discover what lies in your data.

There is a pervasive myth that someone who works in data should know the everything of data. I think that that’s unhelpful because the universe of data has expanded. It’s expanded so much that specialization becomes important. It’s very, very difficult for one person to know and be the everything of data.

So after analysts have created insights from data, what happens? Well, a lot. Those insights are shared with others, decisions are made, and businesses take action. And here’s where it can get really exciting. Data analytics can help organizations completely rethink something they do or point them in a totally new direction.

There is a pervasive myth that someone who works in data should know the everything of data. I think that that’s unhelpful because the universe of data has expanded. It’s expanded so much that specialization becomes important. It’s very, very difficult for one person to know and be the everything of data.

Now, data science, the discipline of making data useful, is an umbrella term that encompasses three disciplines: machine learning, statistics, and analytics. These are separated by how many decisions you know you want to make before you begin with them.

If you want to make a few important decisions under uncertainty, that is statistics. If you want to automate, in other words, make many, many, many decisions under uncertainty, that is machine learning and AI. But what if you don’t know how many decisions you want to make before you begin? What if what you’re looking for is inspiration? You want to encounter your unknown unknowns. You want to understand your world. That is analytics. When you’re considering data science and you’re choosing which area to specialize in, I recommend going with your personality. Which of the three excellences in data science feels like a better fit for you? The excellence of statistics is rigor.

Statisticians are essentially philosophers, epistemologists. They are very, very careful about protecting decision-makers from coming to the wrong conclusion. If that care and rigor is what you are passionate about, I would recommend statistics.

Performance is the excellence of the machine learning and AI engineer. You know that’s the one for you if someone says to you, “I bet that you couldn’t build an automation system that performs this task with 99.99999 percent accuracy,” and your response to that is, “Watch me.” How about analytics? The excellence of an analyst is speed. How quickly can you surf through vast amounts of data to explore it and discover the gems, the beautiful potential insights that are worth knowing about and bringing to your decision-makers? Are you excited by the ambiguity of exploration? Are you excited by the idea of working on a lot of different things, looking at a lot of different data sources, and thinking through vast amounts of information, while promising not to snooze past the important potential insights? Are you okay being told, “Here is a whole lot of data. No one has looked at it before. Go find something interesting”? Do you thrive on creative, open-ended projects? If that’s you, then analytics is probably the best fit for you.

A piece of advice that I have for analysts getting started on this journey is it can be pretty scary to explore the unknown. But I suggest letting go a little bit of any temptations towards perfectionism and instead, enjoying the fun, the thrill of exploration. Don’t worry about right answers. See how quickly you can unwrap this gift and find out if there is anything fun in there. It’s like your birthday, unwrapping a bunch of things. Some of them you like. Some of them you won’t. But isn’t it fun to know what’s actually in there?

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 that improve the way that company operates.

Being a people analyst involves using research, experiments, and 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 we’ve been learning in this program are: ask, prepare, process, analyze, share, and act. These six steps apply to any data analysis. Here, you will read more about how a team of people analysts used these six steps to answer a business question about employees, but you will use these same six steps to answer any data analysis question. Let’s break down what this team did, step-by-step.

1. Ask Question

First up, the analysts in our example needed to define what the project would look like and what would equal 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.

1. Prepare Data for Exploration

It all started with a solid plan. The group built a timeline and decided how they wanted to relay their progress to interested parties. Also during this step, the analysts identified what data they needed to reach the successful result they identified in the previous step — in this case, the analysts chose to gather the data from an employee survey and identified what kinds of questions the survey would include. Rules were established for who would have access to the data collected, what specific information would be gathered, and how best to present the data visually. The end of this step included a brainstorm session of all the possible project- and data-related issues and how to avoid them.

1. Process Data from Dirty to Clean

The group sent the survey out. Great analysts know how to respect both their data and the people who provide it. The employee survey provided the data, so it was important to make sure all employees gave their consent to participate. The data analysts also made sure employees understood how their data would be collected, stored, managed, and protected. In order to maintain confidentiality and protect and store the data effectively, access was restricted to a limited number of analysts. Collecting and using data ethically is one of the responsibilities of a data analyst. You’ll learn more about this later in this course, but for the people analysts in this example, ethical data collection and use means three things: getting consent from the participants; ensuring confidentiality in how the data is analyzed and reported; and carefully storing and protecting the data. Then the data was cleaned up to make sure it was complete, correct, and relevant, and uploaded to an internal data warehouse for an additional layer of security.

1. Analyze Data to Answer the questions

Now it is time for the analysts to do what they do best: Analyze! From the completed surveys, the data analysts discovered that the employee experience ranged from extremely positive to extremely negative. While the natural impulse is to sugarcoat bad news, the group knew it was important to document exactly what they found, no matter what the results said. To do otherwise would hurt trust in the survey process and reduce their ability to collect truthful data from employees.

1. Share Data to Answer Questions

Time to report their findings. The team showed how their results stacked up against the organizational average, as well as how this year’s results compared to results from the previous year. This helped identify team-specific as well as organization-wide successes and challenges. Just as they made sure the data was carefully protected, they did the same with sharing their reports. For example, in order for a manager to receive their team’s survey report, a minimum number of team members had to participate. This ensured confidentiality of the respondents. The group presented the results to leaders first to make sure they had the full picture, then asked them to deliver the results to their teams. This gave leaders the opportunity to put the results into context and have productive team conversations about the results and next steps.

1. Act on the results and focused on improving key areas

The last stage of this process for the team of analysts was to work with leaders within their company and decide how best to implement changes and take action based on the findings. As a result, the organization made a plan and worked hard to improve key focus areas. A year later, the same survey was distributed to employees with the hopes that a comparison between the two sets of results would indicate the success or failure of their action plans. Turns out, the changes improved the employee experience and the action plans were successful!

One of the many things that makes data analytics so exciting is that the problems are always different, the solutions need creativity, and the impact on those around us can be great — even life-changing or life-saving. As a data analyst, you can be part of these efforts. Maybe you are even inspired to learn more about the field of people analytics. If so, consider researching more about this field online and adding that research to your data analytics journal. You never know: One day soon, you could be helping a company create an amazing work environment for you and your colleagues!