How to Analyze Data: A Guide for Beginners

Wherever you fall in the job market, your position will require that you interact with data. Whether that means you’re peering over thousands of statistics or you’re noticing a decrease in customers, you’re working with data.

Data is essential to improving the way that businesses, people, and societies move forward. Odds are, you’re wondering how to analyze data effectively for a job that has you face-to-face with a great deal of information.

We’re going to take a look at some data analysis fundamentals today, giving you a framework to operate with as you move forward.

How to Analyze Data Effectively

The first thing to establish is that all data operates with a different purpose depending on its environment. One statistic might be used one way in a science data context, and in an entirely different way when used as business data.

We imagine that “data analysis” is a process of peering through numbers and using advanced algorithms to come to solutions. In reality, data analysis has a lot of different faces, and you can only do it effectively when you understand your particular context.

This should be a relief because the odds are that you have a decent idea of how things work in your field. Whatever your professional environment, you can follow the following ideas as you work with data.

Get Organized First

You might be introduced to an already-organized set of information, or you could be thrown into a small business environment that leaves all of the accumulation up to you.

What matters is that you first establish a way to organize the data for the purposes of your work. For example, working to improve a small business’ success might require that you look at sales information, employment information, and marketing information all at once.

Establish a way for you to breeze between those different categories at will once all of the data is in place. That way, you can use it to easily engage in the next steps of the data analysis process.

Use Regression Analysis

Regression analysis is a process used to find the statistical relationships between two variables. This is the name of the game when it comes to using data to improve businesses.

Essentially, look back through the information to find positive or negative relationships between numerous factors. With enough data, this process can help you make concrete assertions about different elements of a business.

For example, you can say with near-certainty that one action positively impacts sales. You could also say with confidence that the action has had no impact on sales over a period of time.

A deep understanding of the regressional values of different pieces of data in a specific context can also help you make predictions about the future. That information can be used to then improve the business in the long term.

Use Data To Create Tests

Your job as a data analyst might not be to implement experiments. You could, however, be the most qualified person to layout potential experiments for the business to carry out.

Be on the lookout for relationships in the data that speak to opportunities for growth. You can take those opportunities and bring them to the attention of the business and they can take action.

The result is more data for you to interpret and see if things went the way that you expected them to. If not, your data will allow you to see just why things didn’t work and what you can do next time.

Don’t Shy Away From Qualitative Data

Qualitative data, or data that can’t be directly expressed through numbers, is just as valuable as quantitative data.

Let’s take customer feedback, for example. Customers don’t express their satisfaction or dissatisfaction in a way that directly leads to numbers you can interpret.

Your mission should be to find ways that those behaviors are reflected in numbers, though. You might also try to imagine ways to categorize qualitative data in a way that can be easily analyzed.

For example, an extremely positive review could be marked down as a “5,” whereas a bad review might be a “1.” Those data points can be analyzed in reference to other sets of information that are quantitative.

For example, a decrease in the number of employee hours worked might correlate to a trend in worse reviews. That insight might not have been recognized without the data being translated into a statistic.

Ask for Direction, Use Tools

Make sure that your employer gives you a direct set of goals and outcomes that they’re looking to achieve. Whether those are specific expectations that they have of you or a list of desired outcomes for the business at large, they’re important for you as you try to analyze data.

Again, the field of data analysis is vast and can apply broadly to any area of society. The beautiful thing is that there’s a myriad of tools that it a lot easier for professionals like yourself to interpret information.

Take a look at the analysis of Nanostring nCounter data to get a feel for the type of sophistication that various tools can offer. Do some research into the various data analysis tools that experts in your field use to get a grasp on the information they have.

You might not need to peer over a spreadsheet with a pencil, doing puzzles in your mind to come to conclusions. Odds are that there is software that perfectly addresses your data analysis needs and can offer various insights that you might not achieve on your own.

If you’re a little scared to work in data analysis, just keep in mind that you’re working with data in your head all of the time. You’re constantly taking down information, reading it, and using it to make decisions for the future.

The only difference is that now you’re doing it to try and improve a business, not just your own personal experience.

Need More Business Insights?

Hopefully, our exploration of how to analyze data has been helpful to you. As you move forward, you might need a little more help here and there. We’re here to assist you on that front.

Explore our site for more business ideas, statistical insights, and strategies on how to work more effectively.