What is Data Interpretation and how to do it right?
Apr 20, 2021 ● 7 min read
As we mostly rely on the digital environment to communicate, shop, or simply socialize, the importance of data interpretation and data analysis also increases. This also means that the amount of data that is accumulated within different data sources increases as well, and creating better capabilities to store data is crucial.
Table of Contents
- What is Data Interpretation?
- Examples of Data Interpretation
- Steps of Data Interpretation
- Data Interpretation Methods
- Benefits of Data Interpretation
The business generates revenue by creating value for its target consumers, so getting user feedback is the best way to improve your product. Addressing concerns, improving user experience, and creating more efficient products is what drives the sales. Aggregating and interpreting data are essential steps to uncovering those concerns or demands and finding adequate solutions. To that end, we will focus on the topic of data interpretation, what it is, what its benefits are, and popular methods of interpreting data.
What is Data Interpretation?
Data interpretation is the process where analyzed data or cleansed data is viewed through a frame that can assign meaning to that data and allow us to draw relevant or meaningful conclusions. So, it is a step that comes after a data analysis that we use to make a decision.
Examples of Data Interpretation
One good example of data interpretation is looking at pie charts or bar charts. A pie chart or a bar chart only displays analyzed information that can pertain, let's say to age groups of the user base. So a company can notice which age group is mostly engaged with their content or product. Based on bar charts or pie charts, they can either decide to come up with a strategy for marketing that will make their product more appealing to the less involved groups or a strategy that doubles down on the outreach to their core user base.
This shows that data analysis gives you relevant inputs, but it does not interpret what happened or what we need to do. This is something that board members do by focusing on KPIs (key points of interest) and by interpreting analyzed data.
Through analyzing data, we bring order, manipulate, categorize and summarize raw data that was aggregated through data collection. The final step of data analysis is data interpretation, as it turns the results into actionable items.
Steps of Data Interpretation
Data interpretation is conducted in 4 steps:
- Assembling the information you need (like bar graphs and pie charts)
- Develop findings or isolate the most relevant inputs
- Develop conclusions (like our approach is not working, or this turn out to be a better strategy, etc.)
- Come up with recommendations or actionable solutions.
Considering how these findings dictate the course of action, it is extremely important that data analysts are accurate with their findings and that they examine the raw data through multiple angles. Different variables may allude to different problems, so having the ability to backtrack data and repeat the analysis using different templates is an integral part of a successful business strategy.
Data Interpretation Methods
Data analysts or data analysis tools need to help people make sense of the numerical data that has been aggregated, transformed, and displayed. There are two main methods for data interpretation quantitative and qualitative method.
Qualitative Data Interpretation
This is a method that is used to break down or analyze so-called qualitative data, also referred to as categorical data. One important thing to note is that bar graphs or line charts are not used here but rather it relies on text. This is because qualitative data is collected by relying on person-to-person techniques, and thus making it difficult to present using a numerical approach.
The collection of data is done through surveys as you can assign numerical values to answers, which then makes it easier to analyze. If we rely only on the text, that would be an extremely cumbersome process and prone to errors, which is why it needs to be transformed.
Qualitative data can be divided into two main types:
Both of them are interpreted in the same way. However, ordinal data is a lot easier to interpret compared to nominal.
Ordinal data can be labelled with numbers during the process of collection, so you won't have to use complex code in order to perform the analysis. Nominal data takes more time, and it usually requires advanced algorithms that can speed up the interpretation process.
Quantitative Data Interpretation
This interpretation is applied when we are dealing with quantitative or numerical data. Since we are dealing with the numbers, the values can be displayed in a bar chart or pie chart.
Once again, there are two main types:
- Continous (this one is further divided into ratio data and interval data)
Numbers are easier to analyze since it involves statistical modelling techniques like mean and standard deviation.
This is an average value of a particular data set which is obtained or calculated by dividing the sum of the values within that data set with the number of values within that same set.
- Standard Deviation
This is a technique used to ascertain how responses align with or deviate from the average value or mean. Basically, it relies on the mean to describes the consistency of the replies within a certain data set. You can use this when you are calculating an average pay for a certain profession and then display upper and lower values in the data set.
As stated, there are tools that can do this automatically, especially when it comes to quantitative data. Whatagraph is one such tool as it can aggregate data from multiple sources using different system integrations. It will also automatically organize and analyze that which will later be displayed in pie charts, line charts, or bar charts, however you wish.
Benefits of Data Interpretation
There are multiple data interpretation benefits that explain its significance within the corporate world, medical industry, and financial industry.
Informed decision making - In order to take action and implement new methods in either healthcare, retail, or other industry, the managing board or governing body needs to examine the data. This stresses the importance of well-analyzed data and a well-structured data collection process. Companies, on average, are around 5% more productive and profitable when they rely on data-driven decision-making.
Anticipating needs and identifying trends - Data analysis provides relevant insights which users can leverage to predict trends based on customers' concerns and expectations. If, for example, a lot of people are concerned about privacy and personal information leaking, products that offer better protection and anonymity are more likely to become a trend. This allows manufacturers to focus on developing specific features and come up with a more relevant marketing strategy in order to increase the number of sales once a new version of the product is released.
Cost efficiency - It should come as no surprise that if a company relies on data-driven decision-making that it will also save money. It's true that analysis itself results in extra costs. However, if your strategy is more likely to succeed and be more impactful, you avoid unnecessary expenses on plans that might not work. Moreover, if you end up earning more because of a better-formulated strategy, then not relying on data means you are missing out on the opportunity to generate more revenue.
Clear foresight - Lastly, those companies that aggregate and analyze data gain better insight into their own performance and how they are viewed by the consumers. This allows them to have more knowledge of their shortcomings, and they can work on solutions that can significantly improve their performance.
What are the three steps in interpreting data?
The three main steps in data interpretation are:
- Examining the findings
- Draw conclusions
- Come up with solutions
So we need to examine the analyzed data, and based on that, draw conclusions on the specific topic, which is then followed by an actionable strategy that needs to solve the issue or help us achieve the desired goal.
What are data interpretation and analysis?
Data analysis is the process where we filter and transform the collected raw data in order to give more precise findings. Data interpretation is an examination of those findings, and through that examination, users need to make an informed decision on what to do next.
What should users question during data Interpretation?
In order to interpret data accurately, users should be aware of potential pitfalls that can be present within this process.
You need to ask yourself if you are mistaking correlation for causation. Just if two things occurred together does not indicate that one caused the other.
The second thing you need to be aware of is your own confirmation bias. This occurs when you try to prove a point or a theory and focus only on the patterns or findings that support that theory while discarding those that do not.
The third problem is irrelevant data. To be specific, you need to make sure that the data you have collected and are analyzing is actually relevant to the problem you are trying to solve.