Knowing how data analysis works can set you up for a successful career.

From sports to politics to marketing to transportation, more and more organizations are using data analysis to get ahead. Modern information technology has given us powerful tools to acquire, sort, and interpret information in ways that illuminate potential competitive advantages and reveal organizational weak spots. But despite all of that, data analysis remains a bit of a mystery to most of us. What does data analysis really mean? And how is it done? Here are the basics:

You Use Data Analysis All the Time

You’ve conducted data analysis, whether you’re aware of it or not. For instance, do you have a preferred laundry detergent? How did you decide on that detergent? If you’re like most people, you tried multiple brands and then compared them, noting differences in cost, scent, effectiveness, and ease of use. Those were your data points. You then (knowingly or unknowingly) weighed the data based on each point’s importance to you and used the results to make a decision.

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That’s all data analysis is. It’s collecting pieces of information, comparing them, and then using the results of the comparison to guide your decision-making.

Information Technology Makes It Easy to Gather and Sort Huge Amounts of Data

In the book Moneyball, Michael Lewis describes how the Oakland Athletics baseball team learned to use data analysis to assemble a high-quality yet cost-effective team. The team picked through every bit of data regarding the performance of hundreds of players, compared them to salary costs, and then brought together players whose specific skill sets made them affordable but valuable members of a winning team.

How was this possible? Computers. The same is true for all other forms of modern data analysis. Without information technology, no person—not even a team of people—could gather and hand-sort all of the available data. But computers allow us to store incredible amounts of information and then, with a click of a few buttons, sort and compare that data however we want. This makes it possible to find connections and trends that we could have never found without information technology.

Despite All the Technology, the Human Element Is Vital in Data Analysis At every phase of data analysis, a person or a team must make important decisions and take important actions. When you handle a data analysis project, you must:

  • Decide the objectives
    What is the purpose of gathering data in the first place? What do you hope to achieve for your organization? You must know this before you take any other steps.
  • Identify what can change
    Where in your processes can you actually make a change? For instance, in our first example, your objective is to have cleaner clothes for a reasonable cost. Since you can’t change the fact that you need clothes, you wouldn’t gather data about the need for clothes.
  • Collect the data
    While databases can store an incredible amount of data, a human must, at some point, enter that data. If you aren’t working with other people’s data, you have to input data yourself. If you are working with other people’s data, you have to gather it so you can use it.
  • Clean the data
    Everything from typos to unnecessary data points can pollute your data, making it hard to interpret results. It takes a person to clean this up.
  • Model the data
    Data is static. Before you analyze it, you have to make the decision of what to compare and how to weigh each data point.
  • Interpret the results
    Computers don’t make decisions. Once you’ve run your data through your model, you have to understand what the results reveal and how you can use those revelations to your advantage.

A fast-growing field

Between cloud computing, big data, mobile/virtual workforces, and the increasing desire to connect with audiences electronically, there is a true need for trained IT professionals. In fact, according to the Bureau of Labor Statistics, computer and information technology occupations are projected to grow at a pace that is faster than most—about 13% from 2016–2026. In addition, during that same time frame, it is anticipated that about 557,100 new jobs will be added to the workforce.1

You Can Learn Data Analysis Skills With a BS in Information Technology or MS in Data Science

If you want to truly understand data analysis and use those skills in your career, one of the best choices you can make is to earn an IT degree. At the bachelor’s degree level, a BS in Information Technology can help increase your knowledge of data analysis, and equip you with the technical skills you need to succeed in the IT field while also providing a strong understanding of key managerial and organizational concepts. At the master’s degree level, an MS in Data Science can prepare you with the skills you need to solve critical data science problems in any industry. You’ll be well-versed in the collection, interpretation and communication of data.

You can even use technology to earn an information technology degree. Thanks to online learning, you can earn your degree through an advanced, online platform that gives you the flexibility and convenience you need to fit a degree program into your life. When you choose to earn your MS in Data Science or BS in Information Technology degree online, you can continue to work full time and complete coursework on a schedule that works for you.

Whether your career goal is to be a data architect, chief information officer, a database administrator, or a data analyst, earning an online IT degree will give you the knowledge and credentials to get you closer to your goal. It’s a great way to learn about data analysis and all the other ways information technology is changing—and improving—business.

Walden University is an accredited institution offering an online BS in Information Technology degree program and MS in Data Science. Expand your career options and earn your degree in a convenient, flexible format that fits your busy life.


Walden University is accredited by The Higher Learning Commission,

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