5 Techniques to Take Your Data Analysis Methodology to Another Level

• Quantitative vs. Qualitative
• Measuring Quantitative
• Measuring Qualitative

Implementing a Business Intelligence suite in your organization is about more than simply collecting additional data — it’s about converting this data into actionable insights. The amount of data an organization can collect today from a variety of sources offers the ability to see under the hood, understand which processes are working, and help teams prepare for future trends. However, without properly analyzing and comprehending the data you collect, all you have is figures and numbers with no context.

More importantly, there isn’t one right way to analyze data. Depending on your needs and the type of data you collect, the right data analysis methodology will shift. This also makes it necessary to understand each type of data, and which methodology can deliver the best results. Even so, there are some common techniques that come included in most data analytics software because they’re effective. These five data analysis methods can help you create more valuable and actionable insights.

Quantitative and Qualitative data — What’s the difference?

The first step in choosing the right data analysis technique for your data set begins with understanding what type of data it is — quantitative or qualitative. As the name implies, quantitative data deals with quantities and hard numbers. This data includes sales numbers, marketing data such as click-through rates, payroll data, revenues, and other data that can be counted and measured objectively.

Qualitative data is slightly harder to pin down as it pertains to aspects of an organization that are more interpretive and subjective. This includes information taken from customer surveys, interviews with employees, and generally refers to qualities over quantities. As such, the analysis methods used are less structured than quantitative techniques.

Measuring quantitative data

Quantitative analysis methods rely on the ability to accurately count and interpret data based on hard facts. Our first three methods for upping your analysis game will focus on quantitative data:

1. Regression analysis

Regression studies are excellent tools when you need to make predictions and forecast future trends. Regressions measure the relationship between a dependent variable (what you want to measure) and an independent variable (the data you use to predict the dependent variable). While you can only have one dependent variable, you can have a nearly limitless number of independent ones. Regressions also help you uncover areas in your operations that can be optimized by highlighting trends and relationships between factors.

2. Hypothesis testing

Also known as “T Testing,” this analysis method lets you compare the data you have against hypotheses and assumptions you’ve made about your operations. It also helps you forecast how decisions you could make will affect your organization. T Testing lets you compare two variables to find a correlation and base decisions on the findings. For instance, you may assume that more hours of work are equivalent to higher productivity. Before implementing longer work hours, it’s important to ensure there’s a real connection to avoid an unpopular policy.

3. Monte Carlo simulation

As one of the most popular ways to calculate the effect of unpredictable variables on a specific factor, Monte Carlo simulations use probability modeling to help predict risk and uncertainty. To test a hypothesis or scenario, a Monte Carlo simulation will use random numbers and data to stage a variety of possible outcomes to any situation based on any results. This is an incredibly useful tool across a variety of fields including project management, finance, engineering, logistics, and more. By testing a variety of possibilities, you can understand how random variables could affect your plans and projects.

Measuring qualitative data

Unlike quantitative data, qualitative information requires moving away from pure statistics and toward more subjective approaches. Yet, you can still extract useful data by employing different data analysis techniques depending on your demands. Our final two techniques focus on qualitative data:

4. Content analysis

This method helps to understand the overall themes that emerge in qualitative data. Using techniques like color coding specific themes and ideas helps parse textual data to find the most common threads. Content analyses can work well when dealing with data such as user feedback, interview data, open-ended surveys, and more. This can help identify the most important areas to focus on for improvement.

5. Narrative analysis

This kind of analysis focuses on the way stories and ideas are communicated throughout a company and can help you better understand the organizational culture. This might include interpreting how employees feel about their jobs, how customers perceive an organization, and how operational processes are viewed. It can be useful when contemplating changes to corporate culture or planning new marketing strategies.

There is no gold standard for statistical analysis or right way to do it. The method you choose should always reflect the data you’ve collected, and the type of insights you want to extract. Matching the right data and analysis helps uncover better insights to optimize your organization.