Data misinterpretation comes from deliberate or unintentional false data interpretation. Often different data users will interpret data differently. It depends on the level of data literacy and what exactly users need for the data to show. Various influences determine how we see and use data, and wrong representation can lead to incorrect insights and metrics.
Wrong or incorrect data interpretation can occur when integrating data democratization into business operations. This can lead to making wrong data-based decisions and must be avoided at all costs. But, it can prove to be difficult to minimize the influence of bad data interpretation. It’s hard to avoid it on the individual level. It has to be rooted deep down in individual users in how data should be approached and handled. If a user, an employee in most cases, isn’t educated in data management and analysis, then it’s more likely for them to make mistakes when dealing with data.
What‘s the cause of data misinterpretation?
When looking at what causes users to misinterpret or misrepresent data, there are multiple reasons. Some can be credited to the lack of data literacy and some to psychological aspects. All of these factors influence how data science and data engineering results are perceived, and it’s something that has to be taken into account when jumping into data-based projects.
Inadequate data
Data comes in all forms and not all of it can be analyzed the same way. There are processes needed to be performed before any data is ready for any sort of analysis. Actions like data cleansing, deduplication, identification of bad data, and such precede future methods for data analysis. This is where proper data management systems matter so you can avoid working with data that isn’t ready for data science processes or any other form of analysis, for that matter.
Insufficient or unrepresentative data
Anyone who had any interactions with statistics knows that small samples or insufficient data lead to statistical insignificance and wrong conclusions. Sample size or dataset size determines if the results are relatable to what we observe. Looking at too small sets of data can often skew our findings and present results that are not representative in any way.
Not understanding data
Data literacy probably plays the biggest role in data interpretation. Those that do not understand data, how it’s collected, what it presents, or how to analyze it, will draw wrong conclusions. Before any data analysis actions, users must understand what the data presents. They need to know why something was collected or generated, what are the data sources, what’s the format and what each piece of information presents. If users don’t understand the domain, they have difficulties understanding metrics and insights.
Lack of context
If users aren’t familiar with the reasoning behind data collection and analysis and are presented with just the final results, they’ll have issues with fully understanding what’s going on. How people interpret results depends on how they analyze and contextualize data. Having access to all data doesn’t mean that users should always interpret it without knowing the source, meaning, and intent.
Attribution bias and lack of information
Often, users make snap judgments with limited information. Without deep understanding and knowledge, users make decisions that haven’t taken all the data into account. They make presumptions without the full context. If certain data or variables are omitted, one cannot draw the correct conclusions and it will ultimately lead to faulty decisions.
Faults in aggregation
Data is usually aggregated and observed as a whole set when in most cases it should be observed on more detailed levels. Huge samples of data can lead to the observance of information that can lead to wrong conclusions. Sometimes it is necessary to fragment data to see conclusive patterns or insights. For example, if we view customers or clients as a whole, then we will miss the distinction between segments and different profiles. And each segment will provide different insights and characteristics that can lead to more specific campaigns and actions.
Oversimplifications of findings
If users get some metric or insight based on data, often they see it as it is. They interpret it as a simple result, rather than including all the reasons and influences behind that result. Imagine using multiple strategies for advertising, and you explain the increase in sales based on just one strategy or advertising channel. Omitting other influences and doings and discarding detailed analysis can cause wrong data interpretation.
Correlation doesn’t equal causation
Just because some events are correlated doesn’t mean that one causes the other. Some data can seem to correlate, meaning that one thing stimulated the other, but that’s not always the case. A change in one variable does not automatically induce a change in the other variable.
Misleading data
As often some might misinterpret data, using certain visualizations can also mislead how users perceive information. Choosing one way of visualizing data can distort metrics and change how the viewer understands those figures. Sometimes, misleading visualizations are created intentionally and sometimes they are not if users don’t think it through when choosing visualizations.The causes of misleading data visualizations range from wrong visuals and information manipulation to unclear data sources.
Manipulation of axis and scale
One of the most used methods of misleading visualizations is through axis and scale manipulation. Usually, the axis or scale begins with a zero, but if the intention is to overdramatize results, the scale can start from a greater number and the range between numbers on the scale can be larger or smaller depending on how someone might want to show their data. This is mostly used in bar charts, where depending on the scale, bars can be made to appear taller or smaller.
Data obscuring
Data presenters often obscure results by omitting information that doesn’t serve them. Some might rather point out the positive or negative, depending on their agenda and goal. To achieve that, the data that doesn’t support the objective will be removed from visualizations. This way the focus is on what the presenter wants you to believe. For example, a company can omit some loss to show that they are operating at a profit.
Cherry picking
Cherry picking is when data sources are left out from visualized reports. It’s a selective use of information to support someone’s stance or opinion. This means that only information relevant to that presenter is included in the visualization. It’s a part of data obscuring, but cherry picking is oriented just toward highlighting the best results that align with someone’s strategy. The purpose is to outline cleaner results or to show patterns that don’t actually exist.
Misleading charts
Charts that manipulate data are misleading charts. These charts often don’t correlate to the original data set, question, or hypothesis. Mostly, pie charts are used in these situations, especially when there are multiple findings to one query and they overlap. In pie charts, this information gets distorted and inaccurately represented. Imagine if you have questions with multiple answers where you can choose more than one. If you show it in a pie chart, the percentages won’t correlate to the sample size.
Usage of wrong chart types
Each data or piece of information must be presented through a chart representing data truly to its form. If there is too much information, a pie chart might not be a good idea. Small differences between values can get lost in such charts, and at a glance, they can seem insignificant. Different charts can serve better at distinguishing ranges in value.
Too many variables
If the intention is to confuse those that look at visualizations, many introduce too many variables into one visualization. This way, certain information can be almost hidden and hard to perceive when it’s obscured by other variables. Imagine, introducing way too many variables in a line chart where it’s hard to pinpoint the exact one we need. The important ones get lost in the crowd and impair our understanding of data.
Unconventional visualizations
Sometimes, when presenting data, charts and visualization are personalized and visually adapted to be more attractive. But, there are cases where colors and unconventional visualizations are used to shift focus or overemphasize certain data aspects, metrics or insights. For example, using red colors or unusual graph organization can make something more drastic or more important than it is.
Showing cumulative over fractionated data
When someone wants to hide certain information, they can present it cumulatively. Or even when they want to beautify results. Sometimes, using cumulative isn’t on purpose, but it leads to misinterpreting data since it doesn’t show subsets of data where some insights could be clearer. Let’s look at presenting sales data across markets. In cumulative it can be seen as growth, but when viewed as fragments or individual markets, it can be seen that certain ones have underperformed.
How to avoid data misinterpretation
Intentional or unintentional, data misinterpretation or misrepresentation can cause harm to business operations and start an avalanche of bad decisions. Data is an important part of business analysis and should be handled with care. The starting point of data analysis should be clearly stated, correct data infrastructure needs to be implemented and data democratization should be integrated to avoid miscommunication. To avoid data misinterpretation, one should always start with clean and accurate data that will serve as a base.
Start with clean data
First and foremost, in data analysis one should always start with clean data without errors, duplications, or unfit formats. If data isn’t completely prepared for analysis, the results may be incorrect and valuable insights might not be recognized. Data preparation is an important task and it can determine how data is perceived later on. Data cleaning is a starting process before any data science or data analytics tasks.
Ask insightful questions
The process of data analysis is performed depending on the questions asked and what do users want to get from the data. Probably, the most important part of proper data analysis is to ask the right questions. Without rightly formulating them, users won’t know which data serves them and which objectives can it fulfill. It can also help determine if data support our actions regarding it and if users have enough variables to perform certain methods and actions.
Tell a meaningful story
Data should tell a story and a meaningful one at that. It should explain events, causes, and patterns from which we can draw concussions and conduct actions. Storytelling is an art, and data is one of the tools that help convey important information. In the end, each metric, insight, or value should mean something. These are determining forces behind business decisions.
Be skeptical, curious, and always question data
One should not always take certain aspects of data at face value. Always seek what’s behind each value or visualization. What are the causes and what are the results are some questions you need to ask before interpreting data. Be sure of what you are understanding so you can avoid misinterpretation and potentially bad decisions.
Improve your data literacy
Data literacy is the first step in any data democratization process and data analytics. Without understanding the meaning of data and how it was generated, one cannot analyze it according to best practices. The root cause of data needs to be identified if you want to present it through metrics or visualizations. The bigger the data literacy the bigger the efficiency and lesser data misinterpretation or misrepresentation.
Conclusion
All in all, data is sensitive and it should be treated as such. It can be a powerful tool in any business operation, but it can also be a driving force behind faulty decisions. Its utilization depends on the person analyzing or presenting it. One wrong move can start a chain reaction with wrong results.
Companies and individuals should constantly work on improving their data literacy and data management processes. By fully understanding the extent of data and what to avoid when dealing with it, we can be sure that we’re going in the right direction. As we know, data can be used for good and bad, so if you are presented with something – always question it! Be curious in your quest and try to see the big picture.
