For most government agencies, businesses and financial sector institutions the lack of data is not a problem area.
In fact, it’s the opposite in most cases.
There exists abundant amount of data and in some cases there in an over of data that makes evidence based decision making a difficult thing to execute.
With so much data to sort through, you need the following from your data:
In short – you can say you require better data analysis. With the right data analysis and software solutions, what once was an overwhelming volume of non-uniform data will now help you in many the correct business decisions that will have a multiplier effect on your business metrics.
To understand it simply, data analysis is the process of collecting and then organizing data and information to help draw conclusions from it.
You do data analysis to collect data, analyze it, and then extract helpful insights into what you were researching with an aim to support decision-making and come to data-driven conclusions.
The method or techniques you adopt to collect data and perform data analysis depend on the aim of the research as well as the industry you are in.
While your research may lead you to collect a variety of data, all data can be categorized into two broad types, that is qualitative and quantitative data.
The method you adopt to analyze the data depends on the type of data you and your team have collected.
Therefore, before we move on to discuss the techniques you can adopt for your data analysis methodology it is important to understand both qualitative and quantitative data and what they include.
We will be discussing the data analysis methods for both in turn.
Qualitative data describes characteristics or qualities. Such data concerns those aspects of a business or organization that are comparatively subjective and interpretive.
Qualitative data is collected using information collection tools such as questionnaires, interviews, and observations.
Qualitative data typically refers to qualities rather than quantities. This is why data analysis methods of such data are fairly less structured. We will discuss these in detail below.
Quantitative data is the second broad category of types of data and when compared to qualitative data it is statistical and structured.
This type of data involves quantities and hard numbers and thus is seen to be easier to count and measure objectively.
This type of data is usually derived for research that is trying to answer “what” or “how many” aspects of certain research.
Finally, before we move on to the data analysis methods it is beneficial to understand the potential behind analyzing data and what taking the time to analyze data can do for your company or organization.
There are numerous benefits to be gained from data analysis. These are just a few of those benefits:
And these are just a few of the benefits that are to be gained.
As mentioned above, we will divide the different types of data analysis and data measuring methodologies in light of the two types of data, qualitative data and quantitative data.
First, let’s look at qualitative data measuring methodology.
Qualitative data is subjective and thus requires less structured methods of analysis.
Since qualitative data is harder to quantify it is important to employ the right methodology to gain significant insights for your company or organization.
Here are the most commonly used data analysis methods for qualitative data:
Narrative analysis involves analyzing data from various sources. These sources often include observations from the field, interviews, and surveys. Narrative analysis focuses greatly on the way stories and ideas are communicated.
Thus, such analysis is a great source to understand your company’s organizational culture.
It is often adopted by company’s when they are considering instigating change to the corporate culture or implementing new marketing strategies.
Grounded theory is the use of qualitative data to determine and explain why a specific phenomenon occurred.
This type of analysis uses a number of similar cases happening in different settings to collect data and then derive causal explanations from the happenings.
When using grounded theory, the explanations may vary and alter. It is the responsibility of the researchers to come to a conclusion that would adequately fit and explain all the cases involved.
Amongst the most common of data analysis methods, content analysis allows you to understand any overall themes that emerge in the qualitative data you gathered.
This type of analysis is great when you are working with data collected from interviews, open-ended surveys, user feedback, and similar such collection methods.
Content analysis can help you analyze documented information in various forms including physical items, media, and texts.
Discourse analysis and narrative analysis both are used to analyze interactions with people, whether that be customers or employees.
Discourse analysis in comparison to narrative analysis focuses more on the social context the interaction between the researcher and respondents was when the communication took place.
Discourse analysis also involves considering the respondent’s environment in his or her day-to-day life when coming to conclusions and includes this information in the analysis.
The method you choose greatly affects your ability to extract useful data and therefore should be chosen wisely to be able to provide you with insights into what you are looking for and fulfilling your demands.
Up next we have data analysis methods for quantitative data.
Quantitative data is more straightforward. The analysis methodologies for quantitative data are aimed at accurately calculating and interpreting data based on hard facts.
Here are the most commonly used data analysis methods for quantitative data:
Monte Carlo Simulation also referred to as the Monte Carlo method is a computerized technique that helps generate all possible outcomes and distributions.
This method to analyze data involves considering possible outcomes of an event and then calculating the likelihood of each outcome. This method is primarily used for advanced risk analysis.
By being able to forecast events in the future your organization can make better and more informed decisions.
Next, we have regression analysis. This type of analysis is great when you are looking to forecast future trends or make predictions.
With regression analysis, you are measuring the relationship between dependent and independent variables.
That is to say, regression analysis measures the relationship between what you want to measure and the data you use to predict that dependent variable.
This analysis involves the measurement of one dependent variable but can include a number of independent variables.
Such analysis is great to highlight trends and relationships between factors and to determine areas in your organization that could use optimization and improvement.
Finally, we have hypothesis testing. This type of analysis lets you determine data against the assumptions you’ve made or hypotheses you have created.
Hypothesis testing lets you compare two variables to find a correlation and a base decision on the findings.
This type of analysis also helps you forecast how changes or decisions you make could affect your company or organization.
As you can observe by now, there are multiple data analysis techniques at your disposal. In order to convert data into actionable insights, it’s essential to understand what kind of data you have i.e. qualitative versus quantitative.
In addition, you need to begin with the end in mind i.e. what kind of insights will be useful within the given context. In this post we have introduced you to the 7 most pertinent techniques to take your data analysis methodology to the next level.
In the meantime, we would also recommend the following resources in case you want to further expand your horizon: