Data Analysis: What is it?

Whoever said data analysis had to be boring? According to Investopedia, “Data analytics is the science of analyzing raw data to make conclusions about that information” (1). Simply said, data analysis is the process of working with data to collect important information that can then be used to make decisions based on data. Everyday, data analysis has an impact on everyone, but they are likely unaware of it happening. The alarm goes off, the snooze button is hit, and the morning routine starts. On your way to work, you place your daily mobile order at Starbucks and go to get your productivity boost for the day. You grab your favorite drink (obviously, an iced caramel macchiato!) and head to work. Data analysis has just occurred. Starbucks collected data about what, where, and when you purchased, through their mobile app. This allows the company to offer even more personalized experiences and promotions. Starbucks can learn about their consumers' purchasing behaviors, preferences, and patterns through data analytics. In this scenario, data analysis meets consumers' needs through targeted ads while increasing the company's sales. 

Data analysis is an important component. It allows us to understand trends and patterns. The success and growth rate of an organization is dependent on data analytics. Analyzing data and identifying trends can help businesses gain a competitive advantage. This enables strategic planning, accurate predictions, and appropriate adjustments. The Adobe Team says, “Data analytics can also reduce spending and increase return on investment (ROI) by finding bottlenecks, streamlining operations, and optimizing resource allocation” (2). It is important to use data analytics to optimize business performance and results, as well as your audience. So, as we like to say at Dodo Data, “Do Data!”

Data analysis is not just glorified statistics. It is the process of gathering and analyzing information to help make informed decisions. Of course, statistics are incorporated, but they are not the final destination. Data analysis examines numbers and leads them somewhere. According to Corsera, data analysis consists of six steps: asking a question, collecting data sets, processing your data for analysis, analyzing your data, sharing your conclusions, and acting on your data (3). Let's take the broad view of data analytics and focus in.

Asking a question. The first and most important step is to determine the objectives and the questions the analysis will discover and answer. It consists of understanding the factors and identifying the information needed. 

Collecting data sets. There are different methods for collecting data. Such methods include surveys, interviews, databases, and observations. 

Processing data for analysis. Data cleansing is critical in the data analysis process. During this step, you correct or delete incorrect or incomplete data.

Analyzing data. This involves applying a statistical method to data to identify patterns, connections, and trends. Analyzing data means reviewing all of the data obtained and drawing conclusions.

Sharing the conclusions. After the data has been analyzed, the next step is to interpret and visualize the results in an understandable format. Graphs are a good illustration of this.

Acting on the data. This is where we put all of the data we've collected to use. This step is critical for presenting the findings.

Each step is dependent on the one that came before it. Therefore, each step of the process must be approached with intention and diligence. 

Don't overcomplicate data analytics. There are four primary types of data analytics: descriptive, diagnostic, predictive, and prescriptive. These four types improve decision-making. Catherine Cote wrote an article called 4 Types of Data Analytics to Improve Decision-Making (4), which was published by Harvard Business School. In her post, she offers four helpful questions to better understand the four primary points of data analytics. These are as follows: 

Descriptive analytics - “What happened?”

Diagnostic analytics - “Why did this happen?”

Predictive analytics - “What might happen in the future?”

Prescriptive analytics - “What should we do next?”

Cote goes on and says, “The four types of data analysis should be used in tandem to create a full picture of the story data tells and make informed decisions.” Strengthening analytic skills can help you take advantage of the insights that data gives and advance your career. When in doubt, ask questions.

Data isn’t dead! For fun, I'll leave you with an interesting and persuasive fact. According to McKinsey, companies who use customer analytics are twenty-three times more likely to outperform their competitors when it comes to gaining customers (5). There are many tips, tricks, and strategies that go into data analysis. Learn more about data analysis in our upcoming articles, which will cover all you need to know about collecting, analyzing, interpreting, and applying data insights to your social media efforts. 

1. The Investopedia Team. Analytics: What It Is, How It’s Used, and 4 Basic Techniques. Investopedia. (2023).

2. Adobe Team. Everything You Need to KNow About Data Analytics. Adobe. (2023).

3. Coursera Staff. Data Analysis Terms: A to Z Glossary. Corsera. (2023).

4. Cote, C. 4 Types of Data Analytics to improve Decision Making. Harvard Business School. (2021).

5. Edmond, S. 25 Interesting Facts About Data Science. Data Camp. (2021).