In the modern world, data is everywhere whether you like it or not. There are two groups of people and organizations. Those that are drowning in or not properly utilizing their data and those that are excelling because of their data.
One of the key aspects of making your data work for you is having a proper data interpretation process. It’ll ensure you’re drawing the right conclusions and taking action on the information available.
In this guide, you’ll learn the nuances of data interpretation and how to use it to make the best decisions possible in every situation.
What is data interpretation
Data interpretation, at its core, is the process of taking information and data from one or more sources, using relevant analysis methods, and drawing conclusions. The data can be qualitative or quantitative in nature and the reason interpretation is necessary is that raw data is difficult for most people to understand.
The conclusions drawn from data interpretation can be used in multiple ways such as making policy decisions, making business decisions, presenting recommendations for others in an organization, or coming to a better understanding of important topics.
A basic example of this can be reviewing the information related to an area you want to move into. You may look at crime statistics, the concentration of amenities, the school district, demographic breakdown, etc. before making a decision.
Why is data interpretation so essential?
There are many reasons why data interpretation is essential for businesses and individuals alike. Data is collected to help make informed and useful decisions. The raw data is useless unless you’re able to interpret it properly.
Make better decisions – The backbone of any decision made is the information available at the time. In the past, people believed that many illnesses were caused by bad blood – one of the four humors. So, the solution was to remove the bad blood. We now know that things like viruses, bacteria, and immune responses cause illness and act accordingly.
Likewise, when you’re able to collect and interpret data properly, the quality of your decisions improves. Instead of working with assumptions, you can confidently choose a path for your organization or even life. When more data becomes available, you can correct course.
The most important thing is that you follow a clear process to minimize decision-making errors and fatigue.
Spot trends and take action – Another powerful application of data interpretation is to get in front of trends before they reach their peak. Some people have made a career of deep research into industries, spotting trends, and then taking big bets on them.
For example, people that decided to go into virtual reality or the metaverse (shoutout to OASIS) are reaping the rewards today. Usually, these are people that have been in the industry for years and understand how it moves or innovators that caught wind of a major shift.
With the right data interpretation and a bit of effort, you can catch the beginning of trends and capitalize on it for business or personal growth. There are countless examples of this occurring.
Better resource allocation – The last benefit of data interpretation we’ll touch on is the ability to put people, tools, money, etc. to use more efficiently. For example, let’s say you figured out there’s an underserved market through sound data interpretation, you’d naturally tackle it with more energy and come out on top as a result.
Likewise, you may realize that a market you thought was a good fit would be detrimental. This may be because the market is more mature than your products can cater to, too much competition, or some other factors.
Whatever the case, you can move the necessary resources around faster and more effectively to get better outcomes.
Methods for data interpretation
There are many ways or methods to make sense of your data – qualitative and quantitative. Irrespective of the specific method chosen, there are a few steps that should be taken. This will narrow your focus, reduce the time for analysis, and make sure you’re making the most relevant choices.
Essential data interpretation steps
- Determine the questions you want answered – Collecting a wide swath of data can be useful but it’s also more difficult to answer questions. Before you even start collecting data, you should know what questions you need answers to. The most important benefit of this approach is speed and focus.
- Identify and gather the relevant data – If you know what you want to answer, you can determine the type of data that’s needed. For example, if you’re trying to figure customer loyalty, there are many types of surveys that can be used but the most effective include NPS surveys and customer satisfaction surveys.
- Figure out the most relevant findings – Even if you use the right research instruments and collect the right data, you’ll realize that some info is more important than others. For example, in an employee satisfaction survey, who answered the question (employees with 3+ years of experience or new employees) is more important.
- Draw conclusions – Once you’ve identified the most relevant data, decide what it means. If 80% of your workforce is unsatisfied, what does that mean to you? If 98% of your customers come back in 6 months, what conclusions can you draw?
- Make recommendations and take action – After drawing the initial conclusion, it’s essential that you take action on the data. If 80% of your workforce is unsatisfied, what is contributing to the dissatisfaction and what steps can you take?
Quantitative data interpretation
Quantitative data interpretation is focused on numbers and quantifying the information you have. It’s often visualized with graphs and charts to communicate the key points to stakeholders. Even if you’re the only one interacting with the data, the graphs can be a helpful aid to pull out insights. Three methods are used most often.
Mean – the mean, in this context, is the average of the values in a data set. You arrive at this number by summing up all the values and dividing it by the number of values in the data set. It’s a straightforward analysis method that can help you find a useful number when you only have a sample derived from a large population. For example, the average age of people working in the media industry or the average age of people living in a city/country.
Averages can be misleading when there are outliers in a dataset. For example, if you have a data set with 100 people. 10 make $25,000, 10 make $30,000, 20 make $35,000, 40 make $40,000, 17 make $45,000, and 3 make $1,000,000 a year then the data will be pushed up by the three outliers.
Without them, the mean is $37,268. With them, the mean jumps to $66,150. In cases like this, other statistical methods may be more useful.
Frequency distribution – This is an analysis method where the number of times a value appears in a data set is measured. For example, you may analyze the rate at which options in a Likert scale appeared. This can be used in many types of analysis and, when there are outliers, they’re properly acknowledged and accounted for.
Standard deviation – This form of analysis seeks to determine how far away from the mean the values in the data set are. How well they align or deviate from the average displayed. In our example above, the standard deviation is massive which shows us that there are large variations in the data set.
- Regression analysis – Using historical data to draw conclusions related to a dependent variable and one or more independent variables.
- Cohort analysis – This is one of the most powerful analysis methods when segmenting markets based on various criteria such as their demographics or behaviors. It groups data sets with similar characteristics over specific time periods and runs analysis on them. For example, analysis of the purchase behavior of people aged 21 – 25 over the last three months.
- Predictive analysis – This is an interpretation method that attempts to predict what will happen in the future based on the data you have now and in the past. For example, it’s used to predict trends in the market, make financial plans (like purchasing large assets), etc.
Qualitative data interpretation
Quantitative data interpretation used numbers while qualitative data interpretation uses descriptions and categorizations. Because of this, it’s not as easy to carry out qualitative data interpretation but it can yield even deeper insights.
- Interviews – Interviews are a powerful tool because you’re able to explore relevant topics in depth. They’re also more expensive and time consuming to carry out than a basic survey. The responses can be categorized based on topic or theme and further analyzed.
- Focus groups – Focus groups are among the most expensive method to gather and interpret data. They’re also useful because they can simulate real world environments to help you determine how people act in those situations. Be wary of group think in focus groups.
- Surveys – Surveys are a mainstay and can be used with open ended questions to produce qualitative data that can be further analyzed. Like interviews, the answers can be categorized based on theme or topic.
- Observations – This is a more passive method of data interpretation and cannot be used in all situations. The goal is to look for behavioral patterns based on certain criteria such as frequency, time it was carried out, etc.
This guide has gone through what data interpretation is, the nuances behind it, the different methods available, and even shared multiple examples.
Now, it’s your turn to apply the essential data interpretation steps to your products and reap the benefits that come with them. Let me know what you think in the comments and don’t forget to share.