In the wake of the Big Data age, everyone seems to be talking about data. Data is at the center when it comes to industry news, board meetings, and almost every strategy or new project moving forward. Even job descriptions for non-traditionally data-focused roles are looking for candidates with the ‘data-driven mindset.’ As a result, the way we do business is rapidly evolving and it’s clear that data is here to stay.
Despite all of the talk and enthusiasm surrounding data, though, what are organizations doing with this newfound data-driven focus? How do you go about actually transforming data into actionable insights? How do you determine the right approach when analyzing your data?
There are a number of techniques and methods to choose from when analyzing your data. In this post, we’ll explore a few of the most common and effective data analysis methodologies to help you maximize your approach when working with data.
1. Regression Analysis
Regression analysis is the statistical process of estimating relationships between one dependent variable and one or more independent variables. The focus here is on determining which variables could have a possible impact on the chosen dependent variable. The most common end goal of regression analysis is to identify patterns and predict future trends.
It’s important to note that there are multiple forms of regression analysis, each varying based on the type of data being analyzed and the nature of the variables involved. Overall, regression models remain an effective way to highlight casual relationships and make inferences about those relationships.
2. Monte Carlo Simulation
The Monte Carlo simulation, also known as the Monte Carlo method, is a mathematical technique used to evaluate the probability of certain outcomes and events occurring. Through random sampling and specified parameters, the simulation can be run repeatedly to produce a thorough range of probable results. The more times the simulation is run, the more accurate the range of possibilities will likely be. This methodology is particularly useful when assessing potential risks and to aid the decision-making process.
3. Data Mining
Data mining is an interdisciplinary field that combines a number of machine learning and statistical processes. There are many different techniques that fall under the data mining umbrella, from data preparation to clustering and classification. It is also all about identifying patterns amongst large sets of data from multiple sources to generate new insights. The end goal, though, is to identify areas of improvement, opportunity, and optimize costs.
Sentiment analysis, also referred to as opinion mining or emotional AI, focuses on the analysis of qualitative data. Sentiment analysis is the combination of text analysis, natural language processing, and other computational techniques to determine the attitude or opinions behind data. This method helps analysts easily determine whether the response or viewpoint on a topic is positive, negative, or neutral. Companies commonly use this form of analysis to determine customer satisfaction levels and access their brand reputation. Data collection can be achieved through informal channels such as product reviews or mentions on social media.
For a more in-depth look into sentiment analysis, also read: Modeling Intent & Anticipating Outcomes with Sentiment Analysis
5. Hypothesis Testing
Hypothesis testing is a statistical approach that allows analysts to test assumptions against the parameters of their chosen population. Through testing sample data one can determine the probability that their hypothesis is correct. This method is helpful in making predictions on the effects of decisions before they’ve been made. For example, say you have a theory that increasing your advertising spend will lead to higher sales. Hypothesis testing would allow you to test the validity of your claim, based on your previous sales data or data collected through a generation process, to make a more informed decision. Choices that seem obvious or guaranteed to succeed might not have the desired effect you’d think. This makes the importance of testing and validating your claims all the more important to avoid costly mistakes.