Data Analysis Methods
Big Data is the use of different methods to process large volumes of data.
Key Data Analysis Techniques
- Descriptive Analysis: this technique summarizes historical data to identify patterns and trends, providing insight into past performance.
- Example: a retail company reviews monthly sales data to understand seasonal buying behaviors.
- Source: Harvard Business Review – Data Analysis for Business Decisions
- Predictive Analysis: using historical data and algorithms, predictive analysis forecasts future outcomes, helping companies prepare for changes.
- Example: an airline uses predictive models to estimate future ticket sales, adjusting pricing based on demand predictions.
- Source: IBM – Predictive Analytics for Business
- Prescriptive Analysis: this approach suggests actions based on data insights to achieve desired outcomes. Often combined with AI and machine learning, it is instrumental for complex decision-making.
- Example: a logistics company uses prescriptive analysis to optimize routes, reducing delivery times and costs.
- Source: McKinsey & Company – Advanced Analytics for Business
- Diagnostic Analysis: examines the causes of past performance issues, enabling businesses to address root problems and avoid repeating mistakes.
- Example: a manufacturing firm analyzes defect rates in production to identify inefficiencies in the process.
- Source: DataScience.com – Diagnostic Analytics
- Exploratory Data Analysis (EDA): EDA helps identify relationships and patterns in data, often through visualization, to generate insights for further analysis.
- Example: a marketing team uses EDA to explore demographic data, identifying target customer segments for a new product.
- Source: Tableau – Exploratory Data Analysis
The following are different techniques or data analysis methods that can be used to make decisions in the organization of a process:
Method |
Type |
Characteristics |
Use |
|---|---|---|---|
| Correlation analysis | statistical | Determines whether a relationship exists between two different quantitative variables. If it exists, measure what that relationship is | It is usually used when it is suspected that two variables follow or have a similar evolution. For example, the benchmark stock market index like IBEX 35, CAC 40 PR Index / DAX Index / FTSE 100 Index. |
| Regression analysis | statistical | Investigate the relationship between different variables. | It is used when it is suspected that one of the variables may be affecting (independent variable) the behavior of the other (dependent variable) or others. |
| Data visualization | Graphic or image | It is one of the most demanded and appreciated data analysis techniques today due to the ease with which it is possible to detect patterns in the analyzed data through a graph or image. | It is especially useful when we seek to understand large volumes of data quickly and in a simplified way. |
| Data mining | Massive data | Data analysis process better known as Big Data designed to work with large volumes of data. | It is used to detect patterns, relationships or relevant information that can improve the performance of customer and Internet related operations. |
| Sentiment analysis | Subjective aspects | It determines the attitude of an individual or group towards a particular subject. | It is used when seeking to understand the opinion of the different agents who interact in an industry. The great difficulty with this type of analysis is that they are based on very difficult to measure subjective aspects that have to do with human emotions. |
| Semantic analysis of texts | Large volumes of texts | It tries to extract value through the semantic analysis of large volumes of texts. | Related to the previous technique, it looks for computers to be able to understand what they index and obtain data from unstructured texts. |
| Analysis of patents and scientific literature | Extract information about trends and relationships between studies, authors, or an intellectual property. It is one of the most used techniques in the monitoring of technological trends. | It is used when we have meta data from scientific publications and patents. | |
| Monte Carlo simulation | Mathematical probability and computational algorithms | It measures the approximate risk that a certain event will occur. | It is very useful to understand the implications that may have a certain course of action derived from a decision. |
| Programming and mathematical optimization | Optimization attempts to solve a number of problems. These are characterized by the fact that they look for the maximum and/or minimum of a function, assuming there is one. | It identifies what is the best possible outcome given specific constraints on a situation. | It is very useful to understand the implications that may have a certain course of action derived from a decision. |
| Mathematical prediction | statistical | Predict what is the most likely outcome that can occur in the near future. The basis of these data analysis measures is to look at what has happened in the past to know what will happen in the future. | It is widely used in macroeconomic projections. |
| Neural networks | Mathematician | More complex data analysis techniques that exist. They try to simulate the decision and information process of the brain or groups of neurons | |
| Experiments AB | Also known as AB tests or split testing, they are one of the most used techniques in digital marketing to check the reaction of users to a message and see which one works best | It is mainly used to test hypotheses in the launch of a new product, an advertising campaign or a message in an advertisement. |
Companies have access to ever more data. Due to the large amount of information available, it can be very difficult to understand huge volumes of structured and unstructured data in order to implement improvement projects for the entire company. If not properly addressed, this challenge can limit the promise of this data.
The digital data collected is only useful if it is analyzed and acted upon.
Data Analytics for Business Growth | How Data-Driven Can Fuel Your Business Success?
Steps
Identify needs
Define your questions, start by selecting the right questions. Questions should be measurable, clear and concise. Design your questions to qualify possible solutions to your problem
Collect and process data
Data generally comes from four methods: databases, third-party data statistics tools, reports from professional research institutions, and market surveys . Data processing mainly includes: data filtering , data conversion, data extraction, data fusion and data calculation, and process all kinds of original data according to the style required for analysis of data.
Analyze the data
Once you’ve collected the data correctly, it’s time to do a deeper analysis of the information. Find relationships, trends, sort and filter information according to variables.
Interpret the results
The usefulness and reliability of the result is evaluated and its performance is estimated, usually the data is presented in tables and graphs.
Read also: Success of Your Business Depending on Finance, Sales Marketing and Operations
Classification
Descriptive analysis
Describe what has happened in a given period. For example: Did the number of views increase? Number of sales is greater this month than last?
Descriptive analysis is used when the organization has a large set of data about past events or historical events. For this data to be useful, it must be simplified and summarized so that it is understandable to the audience to whom it is intended to communicate. Descriptive analysis is present in the vast majority of organizations and is usually where it starts. In this type of analysis it is common to see dashboards, bar graphs….
Diagnostic analytics
Focused more on why something happened, when evaluating descriptive data, diagnostic analysis tools will help analysts gain insights to solve the root problem.
Predictive analytics
The amount of data we produce today has made it possible to popularize certain mathematical or statistical techniques and models that have been around for many years. By using them with this large mass of data, we can predict with some probability what could happen.
Predictive analysis is, then, the application of these mathematical and statistical techniques and models to the historical data held by the organization. Although predictive analyzes do not try to predict the future 100%, because this type of analysis is probabilistic , if they predict what could happen. This is how the correlations between variables are understood and how they might behave in the future.
Prescriptive analysis
Prescriptive analytics uses information about what happened, why it happened, and a variety of “possible” situations to help users determine the best course of action.
Prescriptive analytics is actually a combination of other analytics models.
A good example is a traffic app that helps you choose the best way to get home, taking into account the distance of each route, the speed of each road and the current traffic restrictions.
Types of data analysis methods
These are just a few examples of data analysis methods, and they are often used in combination with each other to answer complex research questions or to inform decision-making.
There are different types of data analysis methods, each with its own characteristics and uses. Here are some of the most common types:
Network analysis
This method involves analyzing relationships between entities, such as social networks, transportation networks, or communication networks. Network analysis is used to understand the structure and behavior of complex systems, and to inform policy decisions in fields such as public health and transportation.
Descriptive analysis
This method involves summarizing and describing the main features of a dataset, such as the mean, median, mode, standard deviation, and range. Descriptive analysis is used to understand the basic characteristics of the data and to identify patterns and trends.
Inferential analysis
This method involves making predictions and drawing conclusions about a population based on a sample of data. Inferential analysis is used when it is not feasible or practical to measure an entire population, so a representative sample is taken instead.
Exploratory analysis
This method involves analyzing data to identify patterns, relationships, and trends that were previously unknown or unexpected. Exploratory analysis is used to generate hypotheses and to guide further research.
Predictive analysis
This method involves using statistical or machine learning algorithms to make predictions about future events or outcomes based on historical data. Predictive analysis is used to forecast trends and to inform decision-making.
Prescriptive analysis
This method involves using data and mathematical models to recommend a course of action based on a set of objectives or constraints. Prescriptive analysis is used to optimize business processes and to make informed decisions.
Causal analysis
This method involves determining the cause-and-effect relationship between variables. Causal analysis is used to establish whether one variable is affecting another variable, and to understand the mechanisms underlying the relationship.
These are just a few examples of data analysis methods, and they are often used in combination with each other to answer complex research questions or to inform decision-making.
Factor analysis
This method involves identifying underlying factors that explain the variation in a dataset. Factor analysis is used to reduce the complexity of large datasets, to identify latent variables that are not directly observable, and to inform research in fields such as psychology and sociology.
Spatial analysis
This method involves analyzing data that is linked to a physical location, such as geographic information systems (GIS) data. Spatial analysis is used to understand patterns and relationships in data that are related to geography, and to inform decision-making in fields such as urban planning and environmental science.
Text mining
This method involves analyzing text data, such as customer reviews, social media posts, or news articles. Text mining is used to extract insights from unstructured data, such as sentiment analysis, topic modeling, and named entity recognition.
Conclusion
Implementing effective data analysis techniques can transform raw data into actionable insights, driving strategic decisions and long-term success. By leveraging the right methods, businesses can improve accuracy in decision-making and maintain a competitive edge.
What Does a Data Analyst Do? Why data analytics is booming? Example of utilisations in enterprises
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