Reading and Interpreting Data: Top Tips and Pitfalls to Avoid

Individuals pursuing tech careers of all kinds need to know how to read and interpret data in order to make informed decisions within their role. Learn the questions you need to ask when interpreting data, major pitfalls to avoid and why this skill is so vital to know.
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In an increasingly digital world, reading and interpreting data has become a vital skill for not only data analysts, but any type of business professionals. The amount of enterprise data points collected by organizations of all sizes must be carefully parsed, visualized and presented to stakeholders to elicit useful insights. 

While the language of data can seem intimidating, the good news is that you don’t need to spend years mastering business dashboards or be a professional data analyst or data scientist to become fluent in data-driven decision-making. Read on if you’re interested in learning more about the ins and outs of data analysis and interpretation methods and how you can level up your career with these vital skills. 


What is Data Interpretation? 

Data interpretation refers to the process of assigning meaning to collected information. It is essentially the process of looking at “raw” numbers and determining their significance and implications. On its face, this process can be inherently subjective, which is why it falls on data professionals and businesses to develop their own processes based on the nature and sources of the data they receive.  

Data interpretation allows for the inference of significance between the relationships of variables and can be used to explain patterns. The two primary techniques available to understand data are qualitative and quantitative. While we’ll go into detail on the pair below, for now, know that the former is used for textual and descriptive data, while the latter is for all types of numerical data.

The typical steps a data analyst undertakes to interpret data can be written out simply as follows:

  1. Assemble the needed information and data
  2. Develop findings by isolating relevant inputs
  3. Develop conclusions
  4.  Provide actionable solutions suggested by conclusions 

This process is repeatable and adaptable to essentially any type of data output.


Questions to Ask when Interpreting Data

Before an analyst even begins to decipher information, they must ask themselves several key questions about their dataset. Determining these factors and deciding on desired outcomes in advance provides structure and direction to the interpretation process. While questions will vary based upon the specific circumstance, some general ones might include:

  • Why was this information collected in the first place? Consider the source of your dataset: is it comprehensive in scope? Do your best to only collect data from authoritative sources that you know are free from any biases. 
  • What are you hoping to find out? Determine the KPIs that are most relevant to the project at hand and identify the outcomes you hope to achieve by conducting the analysis. After all, if there is no change following the interpretation, what was the point of the exercise?
  • Which statistical analysis techniques will you apply? – In a business context, the three most commonly used analysis types are regression, cohort and predictive/prescriptive. Your choice will depend on what you plan on measuring and the relationship between your independent and dependent variables. 
  • What type of visualization will you use? – Collecting your fascinating and actionable insights is only half the battle. You must decide on the best visualization that demonstrates the need to take action for your target audience. Using an effective presentation aid can make all the difference here. 
  • Who makes up the intended audience of your analysis and interpretation? – Alongside your visual presentation, knowing the final audience of your analyses will help you decide on the best way to present and discuss your findings. Depending on their technical expertise, you may need to simplify your findings or present them in terms of their specific effect on budget and revenue. 


Interpreting Qualitative Data

To qualify as qualitative, data must be categorical, or, in other words, not described through numerical values or patterns. Most often, this type of data is collected through person-to-person techniques, such as controlled or uncontrolled observation, documents, focus groups or interviews. 

Non-statistical data like this cannot be scientifically interpreted in its original form, which requires analysts to get creative with their methods of parsing this type of information. In some cases, they might choose to present interviews and focus groups findings anecdotally, as a supplement to more numbers-based surveys or projections. Individual qualitative findings can be used to craft ethnographies and case studies. They might also use tools created to help visualize qualitative results such as word clouds, color-coded phrases and timelines, which provide context to and illustrate participant sentiment. 

In some instances, it is possible to convert text data to numerical data that can be used for modeling and interpretation. Researchers group their data into themes and sort them by category to create structured data that provides additional systemic validity to findings. This can be as simple as grouping binary responses (“does the participant like the product? 1=yes, 2=no”) or consist of a multi-step process that groups findings into smaller and more precise categories. Ultimately, coding qualitative data is helpful in many situations at accurately representing participants and presenting their responses transparently and systematically. 


Interpreting Quantitative Data

The sky’s the limit when it comes to interpreting numerical quantitative data. An analyst will use the questions outlined above to narrow down the best methodology and interpretive technique to use. The two most commonly used quantitative data analysis methods are:

  • Descriptive statistics, which focuses on describing the data features and encompasses all measures of central tendency, like mean, standard deviation and frequency distribution.
  • Inferential statistics, which reaches likely conclusions using experimental analysis outside the immediate data set 

Housed within these statistical categories are various analysis techniques. Some of the most commonly used are:

  • Regression Analysis – Estimates the relationships among variables. Uses past data to understand how the typical value of a dependent variable changes with one independent variable being varied while the other is held fixed.
  • Cohort Analysis – Compares how different groups, i.e. customers, behave over time. This is useful for analyzing spending and retention trends of different types of consumers.
  • Predictive and prescriptive analysis – Uses current and historical datasets to predict future possibilities. Often applicable for analyzing alternative scenarios and future risk assessment. 


Data Reading and Interpretation Pitfalls to Avoid

There are a lot of do’s and don’ts when it comes to data interpretation and analysis. Here are a few of the most common “don’ts” to avoid:

  • Using the wrong chart – Just because a chart is the easiest to use, does not mean it’s the best choice for telling the most coherent story with your data. Avoid bar charts if you’re using multiple data points, pie charts for big datasets and line graphs and scatter plots if the values in your dataset are not correlated. 
  • Mixing up Correlation with Causation – You might have heard this one before but it bears repeating: just because two actions occurred together, does not mean one caused the other. Think carefully and eliminate all confounding variables before you make this assumption. 
  • Failing to toss irrelevant data – In the digital age, datasets are getting larger and larger, leading to an increase in “junk” metrics that distort or obscure the phenomena you are attempting to witness. Proactively frame the variables and KPIs you are engaging with before the collection period and undergo data cleansing prior to performing analysis. 
  • Confirmation Bias – This occurs when the analyzer has a theory or hypothesis in mind, and either actively or subconsciously discovers data patterns that only support their theory. Combat this by sharing your results with a third party and simply being aware of confirmation biases potential negative effects before engaging with your study. 
  • Confusing your Audience – Wonky visualizations, incorrect calculations, displaying too much data, using unfamiliar charts and broken axes. These are all ways to lose the interests of your stakeholders or limit their ability to understand your findings. Sometimes it’s best to stick to the basics, or use trusted visualization tools like the ones we’ve mentioned belows.


Data Analysis vs. Data Interpretation

Data Analysis and Data Interpretation are two steps that often go hand-in-hand, but are in fact distinct processes that follow a chronology in the lifecycle of a dataset. First up is, data analysis; the process of bringing structure to collected data. This methodological approach is used by professionals like Data Analysts, Database Administrators and Software Developers to describe, exhibit and evaluate data. At this step, it’s possible to derive meaningful insights and even form conclusions about hypotheses being tested. 

However, to take this process a step further, one would perform data interpretation. This responsibility often falls to a Data Scientist, Senior Analyst, or Data Engineer and consists of assigning meaning to processed and analyzed data. It can also include presenting these conclusions to internal and external stakeholders. 


Data Analysis and Data Interpretation Tools

The most widely used data analysis tools are spreadsheet softwares such as Microsoft Excel and Google Sheets. Coding languages, like Python and R can also be used to convert and parse data, as can analytics engines like Apache Spark. Popular platforms designed explicitly for data analysis include Sisense, TIBCO Spotfire and Thoughtspot.

On the interpretation and visualization side, professionals most often turn to Business Intelligence software such as Tableau, Microsoft BI and Google Data Studio. These powerful all-in-one tools can develop forecasting models, derive insights from data subsets and create visually appealing views for clients. The ones mentioned here all also incorporate machine learning and Artificial Intelligence technologies that power features like sentiment analysis, key phrase extraction, language detection, and image tagging on various data sets.


Why Data Interpretation is Important

As you can see, bringing order and structure to data is an undeniably essential part of any business’s core operations. Patterns and trends look mindless and random when they don’t have meaning applied to them by skilled professionals. 

Here are just a few reasons businesses put data analysis and interpretation first when it comes to understanding the behavior and actions of their customers. 

  • Make Informed Decisions – When methodical analysis and technique take the place of intuition and guessing, decisions can better align with the needs of the customer and the business. It’s true that “you can’t manage what you can’t measure” and that good data simply leads to more effective decisions across any organization. 
  • Maintain Cost Effectiveness – The cost of an excellent analytics and interpretation team is far less than the potential loss that can be caused by a strategic blunder due to not understanding one’s own customer base. Predictive data analytics use methods like response and risk modeling to reduce costs and identify areas where budgets can be spent most effectively. 
  • Identify Future Trends – As machine learning capabilities advance, predictive analytics is taking more of a central role in impacting corporations and industries. The ability to anticipate users’ future needs is an extremely powerful way to guide the entire direction and purpose of an organization. After all, everyone wants to be an industry leader. 
  • Gain Foresight – More often than you’d think, a company struggles to identify who its core consumers even are and what they want out of their products or services. At a base level, collecting and analyzing data is a great way for these organizations to gain a piece of better knowledge about themselves and adapt to be in a more competitive space. 

We hope this article has served as a useful background into the wonderful world of data analytics and interpretation. If you’re interested in learning more, be sure to check out our listings of Data Analytics and Data Science bootcamps across the country and online or read more about the topic in the articles linked below! 

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