The age of data has dawned, and it’s going nowhere. In 2020, there were an estimated 44 zettabytes of data globally. That number will grow to 175 zettabytes in 2025.
With all the data available today, enterprises have a powerful resource to thrive in an increasingly competitive marketplace. However, only 4% of business leaders are prepared to succeed when it comes to working with data.
To truly benefit from your information assets, it’s not enough to simply collect large volumes of data. You need to know how to interpret data as well. It’s the only way you can make informed decisions from your data.
In today’s blog, we tell you all you need to know about data interpretation. Let’s get started.
What Is Data Interpretation?
Data interpretation refers to the process of reviewing data using a set of predefined steps to help assign meaning to the data and reach a relevant conclusion from it. Data interpretation takes the result of data analysis and makes inferences on the relations you’ve studied, from which you draw conclusions.
Before you can interpret data, you need to analyze it. Data analysis involves ordering, categorizing, exploiting, and summarizing data to get answers to certain data research questions. Data analysis is typically the initial step in the entire data interpretation process.
How Do You Interpret Data?
The value of data interpretation in all manner of tasks, including software testing, is unquestionable. But where do you start when you want to interpret data?
Experts have come up with data interpretation methods to help make things easier for people who want to make sense of numerical data. Let’s take a closer look at each of these methods:
Qualitative Data Interpretation Approach
This data interpretation method analyzes qualitative data, also referred to as categorical data. The technique uses text instead of patterns or numbers to describe data.
Typically, qualitative data is collected using different person-to-person techniques. The data is then coded into numbers before the actual data analysis begins. Numerical values are preferred at this stage because text can be cumbersome, time-consuming, and error-prone.
Once an analyst does coding in qualitative data interpretation, the coding needs documenting so other analysts can use it as well.
Qualitative data is divided into two main types: nominal data and ordinal data. Both types are interpreted using a similar method, although interpreting ordinal data is generally simpler than interpreting nominal data.
Quantitative Data Interpretation
As the name suggests, quantitative data interpretation focuses on analyzing quantitative data, also referred to as numerical data. Given that the data contains numbers, its analysis also uses data rather than text.
Quantitative data is divided into two major types: discrete data and continuous data. The latter is subdivided into ratio data and interval data, with both types being numeric.
Quantitative data analysts don’t need to use coding techniques before analyzing quantitative data, given its existence as a number. Analysts apply such statistical modeling methods as mean, median, and standard deviation to analyze the data.
Data Interpretation Steps
Now that you’ve seen what data interpretation is and what types of data interpretation there are, it’s time to look at the actual process. The typical data interpretation process has four main steps. Let’s take a closer look at each:
Assembling the Information
It’s impossible to interpret data unless you have the data in the first place. Thus, the first step in interpreting data is to collect all the information you’ll need for interpretation.
For instance, if you’re about to enter a particular market, you’ll need to do some market research to make the right decisions. Some of the data you’ll want to collect includes information about your target customer, including their demographics, needs, and buying decisions.
You’ll also need data regarding your potential competitors, especially their strengths and weaknesses. Find out how they promote their products, their price point, and what you like about their strategy.
There are many simple ways to collect the data you need. Some of the common approaches you can use include interviews, surveys, customer observation, and focus groups.
Developing Findings
Now that you have the data you need to interpret, it’s time to start developing findings from it. This means making observations about your data. Typically, findings are made through statements that summarize important points from your data.
The main purpose of findings is that these statements help you come up with conclusions. Simply put, your findings will help you form an accurate and more thorough interpretation.
Typically, people look at data once they’ve collected it and try to explain it before they thoroughly start to observe and summarize the trends or patterns they see.
Coming Up With Conclusions
The next step in data interpretation involves developing conclusions from the findings you’ve made. These are essentially explanations of why your data looks the way it does.
The conclusions you draw need to relate back to the research questions you had at the start of your monitoring program.
Developing Recommendations
Now that you have your findings and conclusions, you can make recommendations based on them. Conclusions usually take two forms.
The first form is the specific actions that need to be taken based on your conclusions. The other form recommends gathering further information to make more accurate decisions.
Leverage the Power of Data to Stay Ahead in Your Marketplace
All the data in the world will do you no good without a solid data strategy. Part of this strategy is knowing how to interpret data well. In so doing, you can make informed decisions based on what’s happening and gain a competitive edge over your competition.
Did you enjoy reading this article? Keep visiting our blog https://rumpletech.com for more interesting content.
