Methods of Analysing Data

Jul 14, 2019
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Implementing a business intelligence software in your company or organisation is more than simply about collecting additional data; it is about making this data work for you in the form of actionable information. The amount of data an organization can collect today from a variety of sources offers is staggering. The ability to see what’s behind the curtain, understand what campaigns or actions are working can help a business owner prepare for future trends.

However, without having a proper understanding of the data that is collected, all you those figures, numbers and statistical social insights become substance with no context.

It should be noted that there isn’t anyone correct method for analysing data. It is primarily dependent on the needs of a business and the data they aim to collect that will dictate which methods of analysis will best suit and even at that, techniques can be fluid to deliver the best results. In saying that, there are some tried and tested methodologies that are built into different software because they do work.

The first step in choosing the right data analysis technique for a data set begins with understanding what type of data it is. It can either be quantitative or qualitative data. Someone may ask what exactly is the difference between the two? Quantitative data deals mostly with the volume of information. It is the cold, hard facts that can only be brought in by the numbers. For example, the number of sales, click-through rates (CTR) on online marketing campaigns, return on investments (ROI) and other such measurable figures that can be scrutinised objectively.

Qualitative data is a little more ambiguous and nebulous, but no less valuable. It deals more with being subjective, needs a degree of introspection and very much open to interpretation. This sort of data would apply to things like customer reviews, surveys and even watching for customer and staff interactions with a product or service. It is about the quality of how a product or service is being perceived and this makes methods of analysing this sort of data a bit more difficult since it can often be less structured.

Below are three different methods that can be used to measure quantitative data and two that deal with qualitative data to hopefully might help a business begin to categorise and breakdown the data they may already hold.

Measuring Quantitative Data

Regression Analysis

In order to understand regression analysis fully, it is imperative to understand the following terms:

Dependent Variable: This is the main factor trying to understand or predicted.

Independent Variables: These are the factors that can be hypothesised have an impact on the dependent variable.

Whereas you can only have one dependent variable, you can have a myriad of independent variables. Regression analysis is the measurement of the independent variables to the dependent variable and when understanding the relationship to the dependent variable can be used to help a company make a prediction about future trends with a good degree of confidence. Regression analysis is a reliable method of identifying which variables have an impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.

Hypothesis Testing

Also known as “T Testing”, is used to infer the result of a hypothesis performed on sample data from a larger population. The test tells the analyst whether or not his primary hypothesis is true. For instance, a business owner may assume that more hours of work are equivalent to higher productivity. How exactly would they know this to be factually the case? Before implementing longer work hours, it’s important to ensure there’s a real connection to avoid spending money on wages or even whether the longer hours would even be received well by staff. A business decision made without knowing how it may affect staff may have the opposite effect where productivity may drop.

Monte Carlo Simulation

Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, logistics and other forecasting models. A Monte Carlo simulator helps one visualize most or all of the potential outcomes to have a better idea regarding the risk of a decision. 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. It will allow the analyst to understand what random variables can throw a monkey wrench in a company’s project or strategy.

Measuring Qualitative Data

Content Analysis

Content analysis is a research technique used to make replicable and valid inferences by interpreting and coding textual material. By systematically evaluating texts (e.g., documents, oral communication, and graphics), qualitative data can be converted into quantitative data.

Narrative Analysis- Narrative analysis is a genre of analytic frames whereby researchers interpret stories that are told within the context of research and/or are shared in everyday life. 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.


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