Different ways of analyzing data

Data analysis is the art of extracting information from a large amount of data to support decision-making. As the amount of data increases, its analysis becomes more complex. Data has become the new raw material for creating value for the organization. If an organization manages to understand and draw a conclusion from its data, it will certainly be a great competitive advantage. However, it can also be a disadvantage if competitors are more successful with their data. To succeed, the organization needs both an analytical maturity and a digital infrastructure that enables the analysis. In addition to understanding how to use data to create value, the organization also needs to ensure the availability and quality of the data. 

Below is a brief overview of the most common methods of data analysis. There are different methods used for different purposes. It is a good start to understand what your organization wants to achieve and what processes and/or business goals you are aiming for.

Regression analysis

The fact that we are becoming more data-driven has hardly escaped anyone, but how do you go from collecting and storing large amounts of data to actually extracting insights and knowledge from it? One tool you can use is regression analysis.

Regression analysis helps you to easily see relationships in your data, making it a useful tool for making informed decisions. According to Statistics Sweden, regression analysis is used when you want to find out which underlying factors are driving a particular result.

What is Regression Analysis? 

Regression analysis is a statistical method used to identify and analyze the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps us to predict the value of the dependent variable based on the values of the independent variables. This analysis can be used to predict future trends, evaluate effectiveness and to determine the strength and direction of relationships between variables. 

Regression analysis is an important tool in many fields, such as economics, psychology, medicine and marketing, where it can be used to predict how different factors affect a measure of interest.

There are several types of regression analysis, such as simple linear regression, multiple linear regression and logistic regression. Here is a description of the different types:  

• Simple linear regression: this involves analyzing the relationship between two variables, where one variable is considered the independent variable and the other variable is considered the dependent variable. The aim is to find a linear relationship between the two variables. 
• Multiple regression: This involves analyzing the relationship between a dependent variable and two or more independent variables. The aim is to find a linear relationship between the dependent variable and the independent variables.
• Logistic regression: This type of regression is used when the dependent variable is binary (i.e. it can only take two values, such as 'yes' or 'no'). It is used to predict the probability of an event occurring based on the values of the independent variables.

When and how is regression analysis used? 

To do a regression analysis, whether it is linear or not, you must have a data set with at least one independent variable and one dependent variable. Here is an explanation of the different variables:

Dependent variable (independent variable or response variable) is a variable that is assumed to depend on the value of one or more other variables, known as independent variables.

Independent variable (dependent variable or predictor variable) is a variable whose value is assumed not to depend on the value of any other variable in the analysis.

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You could say that the analysis helps to answer the questions: Which factors are most important? Which ones can we ignore? How do these factors interact with each other? And, perhaps most importantly, how sure are we of all these factors?

The following steps are taken to perform a regression analysis:

1. Collecting and organizing data: Start by collecting data on the dependent variable and independent variable(s). Pay close attention to the quality of your data.
2. Sketch data: Draw the data to visually inspect the relationship between the dependent variable and independent variable(s).
3. Choose the appropriate regression model: There are several different types of regression analysis. Choose the regression model that suits you best, based on the type of data and the purpose of the analysis.
4. Estimating the parameters: Use statistical techniques to estimate the coefficients (i.e. the slope and intercept) of the best fitting line or curve. There are several tools and programs for this.
5. Evaluate the model: Evaluate the model by using static tests. These help to check the fit of the data and assess the importance of the factors. 
6. Use the model for prediction: Once the model is deemed satisfactory, use it to see relationships in your data and try to make predictions about the dependent variable based on new values of the independent variable.
7. Fine-tune the model: If the model does not fit your data well, you may need to go back and adjust the model and/or collect more data.

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There are several software packages available that can be used for regression analysis, such as Excel and Python.

Example

Here is an example of when you can use a linear regression analysis. 

Let's imagine an estate agent who wants to predict the selling price of a house. The price will be based on the house's size, number of bedrooms, location and year of construction. A linear regression analysis is used as follows: 

Dependent variable:

• Selling price of the house

Independent variables:

• Size
• Number of bedrooms
• Location
• Year of construction

The real estate agent can now perform the regression analysis by determining the strength of the relationship between the sales price and the other variables. This information can be used by the agent to make new predictions about the sale price of similar houses in the future. Thus, a regression analysis can show how much impact, for example, the number of bedrooms has on the sales price of the house. 

Monte Carlo method 

Another method that can be used to collect data to gain insights and knowledge is the Monte Carlo method. This analysis was named after the famous casino city of Monaco, as it involves the use of random numbers in a manner similar to gambling. The Monte Carlo method is good for doing different types of risk analysis, where you analyze the probability of a certain thing happening. 

What is the Monte Carlo method? 

The Monte Carlo method is a numerical technique for solving problems by using random sampling. This method is used to estimate the probability distribution of an event. It involves generating a large number of random simulations and analyzing the results to make predictions or solve problems.

The Monte Carlo method can also be used to estimate the behavior of complex systems or models that are difficult or impossible to solve analytically. It involves generating random samples from a probability distribution and using these samples to approximate the solution to a problem.

When and how to use it? 

Here is a basic overview of how the Monte Carlo method is used:

1. Define the problem: Start by defining the problem to be solved or the outcome you want to predict. This could be anything from estimating the probability of winning a game to predicting trends in the stock market.
2. Create a model: Next, create a mathematical or computational model that represents the problem or system being studied. This model should include all the variables that affect the outcome.
3. Generate random samples: Use a random number generator to generate a large number of random samples. The number of samples needed depends on the complexity of the problem and the accuracy of the results you want to achieve.
4. Analyze the results: Run each random sample through the model and analyze the results. For example, if you are trying to predict trends in the stock market, you can use each sample to simulate the performance of different stocks over time.
5. Calculate the results: Once all the random samples have been analyzed, use statistical techniques to calculate the results. This may involve calculating the mean, median or mode of the outcomes, or using other statistical methods to estimate the probability of different outcomes.
6. Evaluate the results: Finally, evaluate the results to determine how accurate they are and whether they meet the requirements of the problem or application. You may need to adjust the model or generate more random samples for the method.

Example

An example of the use of this method is if you want to know the probability of achieving your sales targets this year. Here is a more practical example of when you can use the Monte Carlo method:

Let us imagine that you want to estimate the probability of flipping the heads of a coin. Then the challenge can be defined as follows. What is the probability of getting heads when you flip a fair coin? You can define the probability distribution as a Bernoulli distribution with p=0.5, which means that there is an equal probability of getting heads or tails. You can then generate a large number of random samples by flipping the coin and recording the result (heads or tails) for each flip. Then you can analyze the results to estimate the probability of getting heads.

Cohort analysis

Another method of data analysis called cohort analysis. This analysis involves dividing the dataset into relevant groups and then analyzing them. For example, it may involve segmenting the customer database into smaller groups to see how these different groups behave over time.

What is Cohort Analysis? 

Cohort analysis is a method used in business analysis and marketing that helps to understand how different groups of customers behave over time. It is an effective way to track customer behavior, retention and acquisition, and can provide insights on how to optimize marketing and sales strategies.

It is a powerful way to analyze the impact of changes in your business or marketing strategy. The analysis can also help you identify trends and patterns that may not be obvious when looking at overall data.

There are many tools to use for cohort analysis, such as spreadsheets or specialized analysis software. It is important to note that cohort analysis is an ongoing process, and companies should regularly review and update their cohorts to ensure they are capturing relevant insights.

How to use it? 

The next step is how to perform a cohort analysis:

1. Define the cohorts: A cohort is a group of people who share a certain characteristic, make sure to determine and define your cohorts based on the characteristic you want to study. 
2. Choose metrics: Then decide which metrics are important and which you will measure over time. This could be revenue or engagement level, for example.
3. Set time ranges: Decide which time ranges you want to use, e.g. weeks, months or years. 
4. Collect data: Collect data on the cohorts and their behavior over time. This data should include the metric(s) to be measured.
5. Analyze data and behavior: Calculate the metrics for each cohort at each time interval and compare them to the metrics of other cohorts. Look for patterns and trends in the data that can help you understand the behavior of your cohorts over time. This may include, for example, recurring purchase frequency or their lifetime value.
6. Draw conclusions and take action: Use the insights you gain from your analysis to make informed decisions about your business or marketing strategy.

Example

An example of an application of cohort analysis: 

Imagine the customer journey, without cohort analysis you can gain insight into how different segments behave at different stages of the customer journey. The basic idea behind cohort analysis is to group customers into cohorts based on a specific characteristic or behavior. An example of a cohort could be the month they first purchased, their location or their age. Once customers are grouped, the behavior of the cohort can be tracked over time, allowing you to see where they are, but also trends and patterns in how customer behavior changes over time.

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Below are some more ways to analyze data: 

Factor analysis

What is factor analysis? 

Factor analysis is a statistical method used to find underlying patterns in a large number of seemingly unrelated variables. This is done in the analysis by measuring a number of underlying factors. It is thus a matter of identifying these underlying factors or latent variables that explain the pattern of correlations within a set of observed variables. The basic idea of the analysis is to identify a smaller number of unobserved or latent factors that can explain the covariance between a larger number of observed variables. 

Performing factor analysis usually requires the use of statistical software such as SPSS, SAS or R. The specific steps and procedures may vary depending on the software package used and the research question being addressed. It is important to have a solid understanding of the underlying theory and assumptions of factor analysis before attempting to apply the method to your data.

When and how is it used? 

These steps are used in a Factor Analysis: 

1. Develop a thesis: Define the research question and select the set of observed variables to be analyzed.
2. Select the method: Start by selecting the method with a statistical algorithm to identify the underlying factors. Some common methods are principal component analysis and maximum likelihood estimation.
3. Identify the factors: Identify the number of factors to be extracted, which can be done using a variety of techniques such as scree plots, eigenvalues and parallel analysis.
4. Review the solution: Create an interpretation of the factor solution, which means identifying the underlying constructs represented by each factor and giving them meaningful labels.
5. Validation of the factor solution: Evaluate the reliability and validity of the factor solution using different statistical measures such as factor loadings, communities and factor correlations.

Example

Here is an example of a situation where you can use factor analysis: 

Imagine that your company has sent out a survey to measure customer satisfaction. Then you will need an easy-to-understand scoring of the answers. And the analysis will help you find the correlation between underlying factors. 

Cluster analysis

What is cluster analysis? 

Cluster analysis is a data analysis technique that involves grouping similar objects or data points according to certain criteria. This analysis is used to identify structures within a dataset. The goal is to sort data points into groups (clusters) to gain an understanding of how data is distributed in a particular dataset. It is a form of unsupervised learning, meaning that no prior knowledge of the data is required to group them. 

Cluster analysis can be used to identify patterns in large data sets, to segment customers or markets, and to explore relationships between variables, among other things.

There are different types of cluster analysis techniques:

• Hierarchical clustering: This involves grouping data points into a hierarchy of clusters based on their similarity. This can be done using either agglomerative (bottom-up) or divisive (top-down) approaches.
• K-means cluster: This means dividing data points into a predetermined number of clusters based on their distance to a set of cluster centers.
• Density-based clustering: This involves identifying areas of high data density and grouping points within these areas into clusters.

How to use it? 

The process of performing cluster analysis usually involves the following steps:

1. Data preparation: Select the variables to be analyzed and prepare the data for analysis by cleaning, scaling and transforming it if necessary.
2. Select a clustering algorithm: Next, select a suitable clustering algorithm based on the type of data and the research question being investigated.
3. Select the number of clusters: This involves determining the optimal number of clusters to use, which can be done using various techniques such as the elbow method or silhouette analysis.

Example

Example of use: In your company, you want a better understanding of your customers' buying behavior. Cluster analysis identifies groups that share similar traits, data points and patterns. To then be able to analyze each group's buying behavior based on a selected common denominator such as how often they make a purchase.

Time series analysis

What is time series analysis? 

Time series analysis is a statistical method used to analyze, identify and understand data that vary over time. In other words, it is the analysis of patterns in data that are dependent on time. This type of analysis is used in many fields, such as economics, finance, engineering and environmental science, to name a few. 

How to use it? 

In order to identify different trends and cycles using time series analysis, it is important to collect data and organize them in chronological order. Once the data is in place, it is possible to start analyzing it. The following steps are involved in the process of conducting a time series analysis: 

1. Visualize data: It is always a good idea to visualize data before analyzing it. You can use different types of graphs and charts, such as line charts, dot plots and histograms, to gain insight into the data.
2. Break down the data: Time series data can be broken down into four components: trend, seasonality, cyclical and random. Decomposing the data into these components can help you identify patterns and trends in the data.
3. Model the data: There are several models that can be used to analyze time series data, such as ARIMA, SARIMA and VAR. These models use statistical techniques to analyze the data and make predictions.
4. Validating the model: Once you have developed a model, it is important to validate it. This can be done by using different validation techniques, such as holdout validation and k-fold crossing.

Example

Here is an example of when you can use time series analysis: 

Let's imagine that in your company you want to know how your sales are distributed throughout the year. For example, do you sell about the same amount every month or does it differ based on the season? Here you can analyze your sales using a time series analysis. By first collecting data, breaking down the components and then analyzing with different static techniques.   

Sentiment analysis

What is Sentiment Analysis? 

Sentiment analysis, also known as opinion polling, is a process of analyzing text to then determine the feeling or emotional tone that the text conveys. The goal of sentiment analysis is to identify and classify the opinions or emotions expressed in a text. The opinions and emotions can be perceived as positive, negative or neutral. It is often used in social media monitoring, market research and customer feedback analysis.

Text is classified as unstructured data and thus cannot be analyzed by any of the above methods/analysis. Therefore, it is useful to use sentiment analysis to understand different patterns in written text, such as attitudes, emotions and opinions. There are several methods for sentiment analysis, including rule-based methods, machine learning techniques and deep learning models. 

Overall, sentiment analysis can provide valuable insights into how people feel about a product, service or topic. It can then help companies make informed decisions based on customer feedback.

When and how to use it? 

Here is a brief overview of the process:

• Data collection: Collect data with text data that you want to analyze, such as customer reviews, social media posts or news articles.
• Text pre-processing: Clean text data by removing noise, stop words, punctuation and converting the text to lower case.
• Machine learning model: Convert text data into numerical features that can be fed into a machine learning model. Common extraction techniques include bag of words, TF-IDF, and word embeddings.
• Model selection: Choose a machine learning algorithm suitable for sentiment analysis, such as Naive Bayes, Support Vector Machines (SVM) or Recurrent Neural Networks (RNN).
• Training and testing: Divide the dataset into training and test sets and train the machine learning model on the training set. Evaluate the model's performance on the test set using evaluation metrics such as accuracy, precision, recall, and F1 score.
• Prediction: Once the model is trained and tested, use it to predict the sentiment of new text data.

Example

An example of when sentiment analysis is used: 

Let's say your company has sent out a survey with free text responses, and the software your company uses supports sentiment analysis. This allows the algorithm to calculate whether the incoming responses have expressed positive or negative opinions. You can then draw a conclusion about the success of the mailing. 

Summary/conclusion 

Now we have mentioned some of the most common data analytics. When a data analysis is carried out with high data quality, the company has a good basis for achieving greater understanding and supporting important business decisions. But just like other processes that involve analysis, there is a risk that the work becomes too manual and difficult to take further in the business. Some companies and organizations choose instead to work with their analysis, follow-up and reporting in a broader software that offers more possibilities. But in reality, it is about starting to act on the insights from the various analyzes. So remember that the only thing that actually leads to change in the company is to ensure that the insights are transformed into concrete actions in the company.