15 Minute Read
Data comes in many forms, which makes the challenge of analyzing data unique to each dataset and its purpose. There isn’t a “one size fits all” approach for analyzing data. Knowing how to analyze, interpret, and apply findings to your data analysis can be the difference between successful and accurate insights… and leaving information on the table, still hidden in raw data but just out of reach.
By delving into data analysis techniques, we can begin to build a toolkit to reliably call upon to find meaning in data no matter the occasion or objective.
In this beginner’s guide, we’ll tackle the basics of data analysis for beginners and answer some of the most common questions people have, like how to analyze data and ways to apply data in research.
Let’s start with the fundamentals.
Data analysis is the process of gathering, organizing, and interpreting information. This can be anything from open-ended survey responses, transcripts, or notes. Data analysis goes a step beyond simply putting information in one place and organizing it. An analysis process helps you create models to visualize the information, find patterns, see tension, draw stronger conclusions, and even forecast potential outcomes.
All data analysis starts with “raw data.” This is unfiltered, uncategorized information. It can be something a person wrote, feedback they provided, or comments made in a remote user interview.
Data analysis helps you highlight the most relevant details, find similarities among data clusters (groups) and even break it down into different categories based on classifications using tags.
There are numerous ways to analyze data, but the easiest method is to use a flexible platform that centralizes all of the steps. With greater insight and statistical evidence, you minimize risks that inherently accompany the decision-making process. Rather than hope you’ve gotten the best information, you can weigh both sides of a hypothesis and make the best call based on factual conclusions.
Research platforms like Notably help you import and analyze data in both structured and visual ways. With an open and spatial canvas, shifting data visually gives you the context of your entire dataset to see connections and identify relationships you may not have considered without a bird’s eye view of your data.
Before starting with the determined process for analyzing data, let’s review the most common types of data analysis, which are:
Quantitative data deals with numbers, so it reflects any information pertaining to figures. This could be calculating the average age of a group, looking at grades and GPAs, discerning averages, and so on.
Quantitative data analysis is strictly mathematical, but its actual numbers can be derived from factual data. For example, let’s imagine you wanted to identify key differences between a target group. Rather than measure each criteria individually, you could assign a number to each factor, e.g. language, location and gender could each be given their own number.
U.S. visitors would fall under “1” while someone from Europe may fall under “2.” These numbers allow you to easily group information without losing any of the important meaning behind them. Rather than dilute information into statistics, you use number-based operations to draw more concrete, clear conclusions about your audience.
Qualitative data spans beyond numbers to look more closely at experiences, emotions, and human characteristics. It allows you to delve deeper into a research question, explore the implications of your hypothesis, and even form new hypotheses based on the data you gather.
Notably features for data analysis are rooted in qualitative data collection; by drawing key points from video transcripts or notes, you can unpack all sorts on inquiries through interview analysis, user testing, and more.
Let’s look at an example that differentiates quantitative vs. qualitative data.
Quantitative data: A company sent out an email to 500 subscribers and 150 readers opened the email. This represents a 30% open rate out of their mailing list.
Qualitative data: In an open-response survey, participants revealed that they often did not have time to read long emails during their usual active hours. Some reported that they felt emails often pertained to irrelevant information, or they found the subject headings misleading.
While quantitative data can capture immediate facts based on numbers, qualitative data gives a deeper level of understanding into a person’s experience. You learn, through their own narrative, what their core challenges are and what problems they’re facing. It allows researchers to incorporate feelings and wants into analysis without becoming too subjective.
Evaluative analysis, or evaluative research, helps businesses determine whether their end goals have been met and if a product or application is delivering its desired results. It’s a fundamental aspect of UX, where the end-user’s experience ultimately determines an effort’s success.
At Notably, we allow our curiosity to guide us, and evaluative research helps us build new questions after we’ve completed a project’s lifecycle. Evaluate analysis is integral to innovation and ongoing improvement. It’s what helps companies separate the distance between themselves and their consumers to tap into core needs and deliver more effective solutions in the future.
Rather than asking “Did this work?”, evaluative data helps you answer questions that reach deeper, like “Did this work as well as we intended, and did it create new and unexpected challenges that we can grow from?”
Proper data analysis ensures you only get the most relevant information from your audience. It helps remove your ego and bias from the equation to look more closely at the real experience behind human users.
While many misconstrue data analysis as a cold, detached process, in reality, it ensures that people, their wants, needs, or ethics, are always put first in research.
Once you’ve answered, “How do you analyze data?”, it’s time to go a step further and ask, “What can I do with what I’ve gathered?”
Data collection and analysis is just one part of the picture. Analysis is really a tool that propels companies forward through educated and personalized insight. Using data analysis, businesses can run hypothetical scenarios, test hypotheses, and lower risks by taking the most educated path toward their goals.
Moreover, data allows you to prioritize objectives and refine strategies to reflect user needs. It can make the shift from business-centric to customer-centric more practical without sacrificing the bottom line.
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