Data analysis and interpretation have taken center stage with the advent of the digital age... and the sheer volume of data can be staggering. According to a study by Digital Universe, the total amount of data in 2012 was 2.8 trillion gigabytes! Based on this amount of data alone, it's clear that the ability to analyze complex data, gain actionable insights, and adapt to new market demands will be the calling card of any successful business in today's global world - all in the blink of an eye.
Business Dashboards are the digital age's tools for Big Data. Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analysis, they are ideal for the rapid, data-driven market decisions that drive today's industry leaders to sustainable success. Through the art of streamlined visual communication, data dashboards enable companies to make informed decisions in real time and are important tools for data interpretation. Let's first find a definition to understand what lies behind the importance of Data Interpretation .
What is data evaluation?
Data interpretation is the process of using various methods of analysis to examine data and reach relevant conclusions. Data interpretation helps researchers categorize, process, and summarize the information to answer critical questions.
The importance of data interpretation is obvious, and therefore it must be done properly. It is very likely that data will come from a variety of sources and enter the analysis process with an arbitrary arrangement. Data analysis tends to be extremely subjective. That is, the nature and goal of interpretation varies from company to company, likely related to the nature of the data being analyzed. While there are different types of procedures used depending on the nature of the data, the two most common categories are "quantitative analysis" and "qualitative analysis."
However, before a serious investigation of the data evaluation can begin, it should be understood that the visual representation of the data results is irrelevant until an informed decision has been made about the measurement scales. Before any serious data analysis can begin, the measurement scale for the data must be determined, as this will have long-term implications for the profitability of the data analysis. The various scales include:
Nominal scale: non-numerical categories that cannot be quantitatively classified or compared. The variables are exclusive and exhaustive.
Ordinal scale: Exclusive categories that are exclusive and exhaustive, but have a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, moderate, etc. OR agree, strongly agree, disagree, etc.).
Interval: a measurement scale in which data are grouped into categories with ordered and equal intervals between categories. There is always an arbitrary zero point.
Ratio: Contains characteristics of all three categories.
How to evaluate data?
When interpreting data, an analyst must try to identify the differences between correlation, causality, and chance, as well as many other biases - but must also consider all of the factors involved that may have led to a result. There are several methods of data interpretation that can be used.
Data interpretation is designed to help people understand numerical data that has been collected, analyzed, and presented. A basic data interpretation method (or methods) provides structure and consistency for your analysis teams. After all, when multiple departments take different approaches to interpreting the same data, even though they share the same goals, there can be a disconnect between objectives. Different methods lead to duplication of effort, inconsistent solutions, wasted energy, and inevitably, wasted time and money. In this part, we will look at the two main methods of data analysis: qualitative and quantitative.
Qualitative data evaluation
Qualitative data analysis can be summed up in one word: categorical. In qualitative analysis, data are described not by numerical values or patterns, but by the use of descriptive context (i.e., text). Typically, narrative data are collected through the use of a variety of person-to-person techniques. These techniques include:
Observations: Recording patterns of behavior that occur within an observation group. These patterns can be the time spent in an activity, the type of activity, and the type of communication.
Focus groups: Group people and ask them relevant questions to stimulate a collaborative discussion about a research topic.
Secondary research: similar to how behavioral patterns can be observed, different types of documentation resources can be coded and subdivided according to the type of material they contain.
Interviews: one of the best collection methods for narrative data. Respondents' answers can be grouped by topics, themes, or categories. The interview approach allows for highly focused data segmentation.
A key difference between qualitative and quantitative analysis is evident in the interpretation phase. Qualitative data are highly interpretable and must be "coded" to facilitate grouping and labeling the data into identifiable themes. Because person-to-person data collection techniques can often lead to disputes over proper analysis, qualitative data analysis is often summarized in three basic principles: Perceiving things, collecting things, thinking about things.
Quantitative data evaluation
If you could sum up the interpretation of quantitative data in one word (and you really can't), that word would be "numerical." There are few certainties when it comes to data analysis, but you can be sure that if the research you are involved in does not involve numbers, it is not quantitative research. Quantitative analysis refers to a set of procedures used to analyze numerical data. Most often, this involves the use of statistical models such as standard deviation, mean, and median. Let's briefly review the most common statistical terms:
Mean: The mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), the mean represents a central value of a particular set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe this concept are arithmetic mean, average, and mathematical expectation.
Standard deviation: This is another statistical term that occurs frequently in quantitative analysis. The standard deviation provides information about the distribution of responses around the mean. It describes the degree of consistency within the responses; together with the mean, it provides information about the data sets.
Frequency distribution: This is a measure of the frequency of occurrence of a response in a data set. For example, in a survey, the frequency distribution can determine the frequency of occurrence of a particular response on an ordinal scale (e.g., agree, strongly agree, disagree, etc.). The frequency distribution is extremely useful in determining the degree of agreement between data points.
Quantitative data is usually measured by visually displaying correlation tests between two or more significant variables. Different procedures can be used together or separately, and comparisons can be made to ultimately reach a conclusion. Other characteristic interpretation procedures for quantitative data are:
Regression Analysis: Essentially, regression analysis uses historical data to understand the relationship between a dependent variable and one or more independent variables. By knowing which variables are related and how they have performed in the past, you can predict possible outcomes and make better decisions for the future. For example, if you want to predict your sales for the next month, you can use regression analysis to understand what factors will affect sales, such as product sales, the launch of a new campaign, and more.
Cohort analysis: This method identifies groups of users who share common characteristics during a specific time period. In a business scenario, cohort analysis is typically used to understand different customer behaviors. For example, a cohort could be all users who signed up for a free trial on a given day. It analyzes how these users behave, what actions they take, and how their behavior differs from that of other user groups.
Predictive analytics: As the name suggests, predictive analytics aims to forecast future developments by analyzing historical and current data. Using technologies such as artificial intelligence and machine learning, predictive analytics enables companies to identify trends or potential problems and plan informed strategies in advance.
Prescriptive analytics: Prescriptive analytics, also based on predictions, uses techniques such as graph analysis, complex event processing, and neural networks to determine the impact of future decisions and adjust them before they are actually made. This helps companies develop responsive, practical business strategies.
Conjoint analysis: The conjoint approach is typically used in survey analysis to analyze how individuals value different attributes of a product or service. This helps researchers and companies determine prices, product features, packaging, and many other attributes. A common application is menu-based conjoint analysis, in which individuals are given a "menu" of options from which to build their ideal concept or product. In this way, analysts can understand which attributes they would prefer over others and draw conclusions.
Cluster Analysis: Last but not least, cluster analysis is a method to group objects into categories. Since there is no target variable in cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context, clustering is used to segment audiences to create targeted experiences, and in market research it is often used to identify age groups, geographical information, income, etc.
Now that we have seen how to interpret data, let's ask ourselves some questions: What are the benefits of data interpretation? Why do all industries engage in data research and analysis? These are fundamental questions, but they are often not given sufficient attention.
Why is data evaluation so important?
The purpose of data collection and interpretation is to gain useful and actionable information and make the most informed decisions possible. From businesses to newly married couples exploring their first home, data collection and interpretation offers limitless benefits to a wide range of institutions and individuals.
Regardless of method and qualitative/quantitative status, data analysis and interpretation may have the following characteristics:
- Identification and explanation of data
- Compare and contrast data
- Identification of data outliers
- Predictions for the future
Data analysis and interpretation ultimately help improve processes and identify problems. Without a minimum level of data collection and interpretation, it is difficult to grow and make reliable improvements. What's the key word? Reliable. Vague notions of performance improvement exist in all institutions and industries. But without proper research and analysis, an idea will likely remain in a stagnant state forever (i.e., minimal growth). So what are some of the business benefits of data analysis and interpretation in the digital age? Let's take a look!
1) Sound decision making: A decision is only as good as the knowledge on which it is based. Sound decision making based on data has the potential to differentiate industry leaders from the rest of the market. Studies have shown that companies in the top third of their industry are, on average, 5% more productive and 6% more profitable when they use sound data decision-making processes. Most decisive actions emerge after a problem is identified or a goal is defined. Data analysis should include identification, thesis development, and data collection, followed by data communication.
If institutions just follow this simple sequence, which should be familiar to all of us from elementary school science fairs, they will be able to solve problems as they arise in real time. Informed decision making has a tendency to be cyclical. This means that there is really no end to it, and new questions and conditions will arise during the process that need to be further explored. Monitoring data results will inevitably lead to the process returning to the beginning with new data and perspectives.
2) Anticipating needs with trend identification: Data insights provide knowledge, and knowledge is power. Insights gained from analyzing market and consumer data have the ability to set trends for other companies in similar market segments. A perfect example of how data analytics can impact trend prediction is the music recognition application Shazam. The application allows users to upload an audio clip of a song they like but can't seem to identify. Every day, users identify 15 million tracks. With this data, Shazam has helped predict future popular artists.
When industry trends are identified, they can serve a larger industry purpose. Insights from Shazam monitoring not only help Shazam meet consumer needs, but also give music managers and record labels insight into the current pop culture scene. Data collection and interpretation processes can enable industry-wide climate prediction and lead to greater revenue streams across the market. For this reason, all institutions should follow the basic data cycle of collection, interpretation, decision-making, and monitoring.
3) Cost efficiency: Proper implementation of data analytics can provide companies with profound cost advantages in their industry. A recent data study by Deloitte vividly demonstrates this by stating that the ROI of data analytics is driven by efficient cost reductions. Often, this benefit is overlooked because making money is usually considered more "attractive" than saving money. However, solid data analytics are able to alert management to opportunities for cost reduction without requiring significant human capital investment.
An excellent example of the potential for cost efficiency through data analytics is Intel. Prior to 2012, Intel performed more than 19,000 functional tests on its chips before they were ready for release. To reduce costs and shorten testing time, Intel introduced predictive data analytics. By using historical and current data, Intel now avoids testing each chip 19,000 times by focusing on specific and individual chip tests. After its introduction in 2012, Intel saved over $3 million in manufacturing costs. Cost reductions may not be as "sexy" as data gains, but as Intel proves, this is one benefit of data analytics that should not be neglected.
4) Clear view of the future: Companies that collect and analyze their data gain better insights into themselves, their processes and their performance. They can identify performance challenges as they arise and take action to overcome them. Data interpretation through visual representations enables them to process their insights faster and make more informed decisions for the future of the business.
Common problems in data analysis and interpretation
The oft-repeated mantra of those who fear data advances in the digital age is "big data equals big problems." While this statement may not be true, it is safe to say that certain data interpretation problems or "pitfalls" do exist and can occur when analyzing data, especially at speed of thought. Below are some of the most common data misinterpretation risks and how they can be avoided:
1) Confusing correlation with causation: Our first misinterpretation of data refers to the tendency of data analysts to confuse the cause of a phenomenon with correlation. It is the assumption that two actions occurred together and one caused the other. This is incorrect because actions can occur together without a relationship between cause and effect.
An example from the digital age: assuming that higher revenue is the result of a larger number of followers on social media, there could be a clear correlation between the two, especially in today's multi-channel buying experience. But that doesn't mean an increase in followers is the direct cause of higher revenue. There could be both common cause and indirect causality.
Workaround: Try to eliminate the variable that you think is causing the phenomenon.
2) Confirmation bias: Our second data interpretation problem occurs when you have a theory or hypothesis in mind, but only want to discover data patterns that support that theory or hypothesis, while discarding those that do not.
Digital age example: Your boss asks you to analyze the success of a recent cross-platform social media marketing campaign. Analyzing the potential data variables from the campaign (which you ran and believe went well), you find that the share rate for Facebook posts was great, but the share rate for Twitter tweets was not. If you use only the Facebook posts as evidence for your hypothesis that the campaign was successful, this would be a perfect manifestation of confirmation bias.
Remedy: Since this pitfall is often based on subjective desires, one remedy would be to analyze the data with a team of objective people. If this is not possible, another solution is to resist the urge to draw a conclusion before the data investigation is complete. Remember that you should always try to disprove a hypothesis, not prove it.
3) Irrelevant data: The third trap of misinterpreting data is particularly important in the digital age. As large amounts of data are no longer stored centrally and they continue to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to solve.
Digital age example: When trying to measure the success of a lead generation email campaign, you find that the number of homepage views directly attributable to the campaign has increased, but not the number of monthly newsletter subscribers. Based on the number of homepage views, you decide that the campaign was a success when in fact it generated no leads.
Workaround: Proactively and clearly define all data analysis variables and KPIs before performing a data review. If you're measuring the success of a lead generation campaign by the number of newsletter subscribers, there's no need to review the number of homepage visits. Make sure you focus on the data variable that answers your question or solves your problem, not irrelevant data.
4) Truncating the axes: When you create a chart to begin interpreting the results of your analysis, it is important to keep the axes true and avoid misleading visualizations. If you start the axes with a value that does not reflect the actual truth about the data, it can lead to false conclusions.
Example for the digital age: In the following figure, we see a chart from Fox News where the Y-axes start at 34%, which makes it appear that the difference between 35% and 39.6% is much larger than it actually is. This could lead to a misinterpretation of the tax rate changes.
5) (Small) sample size: Another common problem in data analysis and interpretation is the use of a small sample size. Logically, the larger the sample size, the more accurate and reliable the results. However, this also depends on the size of the effect of the study. For example, the sample size for a survey on the quality of education will not be the same as for a survey on people playing outdoor sports in a given area.
Example digital age: Imagine you ask 30 people a question and 29 answer "yes", which corresponds to 95% of the total number. Now imagine that you ask the same question to 1,000 people and 950 of them answer "yes," which again corresponds to 95%. These percentages look the same, but they do not have the same meaning, because a sample of 30 people is not meaningful enough to draw a truthful conclusion.
Remedy: To determine the right sample size for truthful and meaningful results, a margin of error must be defined, which indicates the maximum deviation of the results from the statistical mean, according to the researchers. In parallel, a confidence level must be established, which should be between 90% and 99%. With these two values in hand, researchers can calculate an accurate sample size for their studies.
6) Reliability, Subjectivity, and Generalizability: When conducting a qualitative analysis, researchers must consider practical and theoretical limitations when interpreting the data. In some cases, qualitative research may be considered unreliable due to uncontrolled factors that may or may not influence the results. In addition, the researcher plays the main role in the interpretation process, meaning he or she decides what is relevant and what is not, and as we know, interpretations can be very subjective.
Generalizability is also a problem that researchers face in qualitative analysis. As mentioned in the point about small sample size, it is difficult to draw conclusions that are 100% representative because the results may be biased or not representative of a larger population.
These factors occur primarily in qualitative research, but can also affect quantitative analysis. For example, when selecting KPIs to present and how they are presented, analysts may also be biased and present them in a way that benefits their analysis.
Digital age example: Biased questions in a survey are a good example of reliability and subjectivity issues. Imagine you send out a survey to your customers to find out how satisfied they are with your customer service and ask the following question: "How great was your experience with our customer service team?". Here we can see that this question clearly influences the individual's response by tagging it with the word "amazing".
Remedy: One solution to avoid these problems is to keep your inquiries honest and neutral. Keep the wording of the questions as objective as possible. For example, "On a scale of 1-10, how satisfied were you with our customer service?" This will not bias the respondent toward a particular answer, so the results of your survey will be reliable.
Techniques and methods of data evaluation
Data analysis and interpretation are critical to drawing informed conclusions and making better-informed decisions. As we have seen in this article, interpreting data is both an art and a science. To help you with this, we will list here some relevant data interpretation techniques, methods and tricks that you can use for a successful data management process can use.
As mentioned at the beginning of this post, the first step to successful data interpretation is to determine the type of analysis you want to conduct and apply the appropriate methods. Make a clear distinction between qualitative analysis (observe, document, interview, collect, and think about things) and quantitative analysis (you are conducting research with lots of numerical data to be analyzed using various statistical methods).
1) Ask the right questions for data evaluation.
The first technique of data analysis is to define a clear starting point for your work. This can be done by answering some critical questions that will serve as a useful guide to get started. These include: What are the goals of my analysis? What type of data analysis method will I use? Who will use this data in the future? And most importantly, what general question do I want to answer?
Once all this information is determined, you can start collecting data. As mentioned at the beginning of the post, the methods of data collection depend on what type of analysis you are using (qualitative or quantitative). Once you have all the necessary information, you can start analyzing, but first you need to visualize your data.
2) Use the right kind of data visualization
Data visualizations such as business graphs, charts and tables are essential for successful data interpretation. This is because visualizing data through interactive charts and graphs makes the information more understandable and accessible. As you may know, there are different types of visualizations you can use, but not all of them are suitable for every analysis purpose. Using the wrong chart can lead to misinterpretation of your data, so it is very important to choose the right visualization for it. Let's take a look at some use cases for common data visualizations.
Bar Chart: The bar chart is one of the most commonly used chart types and uses rectangular bars to show the relationship between 2 or more variables. There are different types of bar charts for different interpretations, including the horizontal bar chart, the column chart, and the stacked bar chart.
Line Chart: The line chart is most commonly used to show trends, acceleration or deceleration, and volatility, and is intended to show how data changes over a period of time, such as sales figures over a year. A few tips to keep this chart interpretable are to not use too many variables that could clutter the chart, and to keep the axis scale close to the highest data point so the information is not difficult to read.
Pie chart: Although this chart does not add much to the analysis due to its straightforwardness, pie charts are often used to show the proportional composition of a variable. Visually, plotting a percentage in a bar chart is much more complicated than in a pie chart. However, this also depends on the number of variables you are comparing. If your pie chart needs to be divided into 10 parts, it is better to use a bar chart.
Tables: While they are not a special type of chart, tables are commonly used when interpreting data. Tables are especially useful when you want to display data in its raw format. They give you the freedom to easily look up or compare individual values while displaying totals.
As the use of data visualizations becomes increasingly important to the analytical success of organizations, many tools have emerged to help users visualize their data in a coherent and interactive way. One of the most popular tools is the use of BI dashboards. These visual tools provide a centralized view of various graphs and charts that paint a bigger picture about a topic. In the next part of this post, we will learn more about the power of dashboards for efficient data analysis practices. If you want to learn more about different types of data visualizations, take a look at our complete guide on this topic.
3) Remain objective in interpretation
As mentioned earlier, it is essential that you remain objective in your interpretation. As the person closest to the study, it is easy to become subjective when looking for answers in the data. A good way to stay objective is to show the information to others involved with the study, such as the research partners or even the people who will be using your results once they are finalized. This can help avoid confirmation bias and problems with the reliability of your interpretation.
4) Mark your results and draw conclusions.
Findings are the observations you have made from your data. They are the facts that will help you draw deeper conclusions about your research. Findings can be, for example, trends and patterns that you discovered during the interpretation process. To put your findings in perspective, you can compare them to other sources that have used similar methods and use them as benchmarks.
Reflect on your own thinking and reasoning and be aware of the many pitfalls involved in data analysis and interpretation. Correlation versus causation, subjective bias, misinformation and inaccurate data, etc. Once you are satisfied with your interpretation of the data, you can draw conclusions, verify that your original question has been answered, and make recommendations based on that.
Interpreting data: The Use of Dashboards to Bridge the Gap
As we have seen, quantitative and qualitative methods are different types of data analysis. Both offer different levels of return on investment (ROI) in examining, testing, and making decisions about data. Because of these differences, it's important to understand how dashboards can be used to bridge the gap between quantitative and qualitative information. How do digital data dashboard solutions play a key role in bridging the data gap? Here are some of the possibilities:
1) Linking and merging data . With today's pace of innovation, it is no longer possible (nor desirable) to store mass data centrally. As businesses continue to globalize and boundaries dissolve, it is increasingly important for companies to have the ability to perform various data analyses regardless of location. Data dashboards decentralize data without sacrificing the necessary speed of thought, unifying both quantitative and qualitative data. Whether you want to measure customer trends or business performance, you now have the ability to do both without being limited to a single choice.
2) Mobile data. Related to the term "linked and blended data" is the notion of mobile data. In today's digital world, employees are spending less time at their desks while increasing production. This is made possible by the fact that mobile analytics solutions are no longer standalone. Today, mobile analytics applications seamlessly integrate with everyday business tools. In turn, quantitative and qualitative data is available on-demand, where, when and how it is needed, via interactive online dashboards.
3) Visualization. Data dashboards bridge the data gap between qualitative and quantitative methods of data interpretation through the science of visualization. Dashboard solutions are well equipped "out of the box" to create easy-to-understand data demonstrations. Modern online data visualization tools offer a variety of color and filter patterns, encourage user interaction, and are designed to improve predictability of future trends. All of these visual features make it easy to switch between data methods - you just need to find the right types of data visualization to best tell your data story.
To give you an idea of how a market research dashboard fulfills the need to combine quantitative and qualitative analysis and understand how to interpret data in research thanks to visualization, take a look at the following example. It combines both qualitative and quantitative data that has been expertly analyzed and visualizes it in a meaningful way that anyone can understand, so that any viewer is able to interpret it: