Digital acceleration, with the emergence of new technologies, has placed data analysis as a protagonist in decision-making, strategy and action in any business.
A powerful tool, data analysis plays a crucial role in organizations.
In this guide you will understand the concept of data analysis, what types it is, what tools are used and what their benefits are.
What is data analysis?
Data analytics is the process of collecting, modeling, and analyzing data to produce knowledge and insights that support decision-making or problem-solving.
In general, this data is gathered in the Big data, technological basis for storing a high volume of data that is present throughout data-driven business, like iFood.
There are several methods and processes for carrying out data analysis and each one is applied according to the objective to be achieved.
Data analysis methods are based on qualitative and quantitative analyses.
Data analysis: how does it work?
Data analysis works by summarizing and organizing collected data into tables, graphs and numerical measurements so that they can be interpreted.
The interpretation is made using qualitative or quantitative methods based on the study of the variables in the data set.
The characteristics of what is analyzed are called variables, which are divided into qualitative, or categorical, and quantitative, numerical.
A Census survey, which analyzes a population, for example, uses several variables, such as gender, age, education, among others.
What are the benefits of data analysis for companies?
The benefits of data analysis for companies are varied and range from knowing the customer better to reducing operational costs. See some of them below:
- Improved customer segmentation: Through data analysis, it is possible to develop advertising and marketing campaigns, targeted at demographic groups that are interested in the company, saving time and resources.
- Right decisions: data analysis will help in making the right decisions based on facts and figures, and not from an intuitive perspective. It's a way to know where to invest, what are the best business opportunities, predict profits and track unusual situations before they become a problem.
- Cost reduction: Data analysis can show which areas of the company need more resources and which are not producing well, for example. This way, the company can reorganize its budget, invest in new business opportunities and promote more profitable and lower-cost strategies.
Types of Data Analysis
There are five types of data analysis: descriptive analysis, diagnostic analysis, prescriptive analysis, predictive analysis, and exploratory analysis. Understand how each of them works.
Descriptive analysis
It is the starting point of any analytical reflection to answer the question about what happens in the target object of the research.
It is widely used in academic research, but also in companies that seek to understand the market in which they operate.
Descriptive analysis follows the following steps: problem identification, data collection, data structuring, data presentation, analysis and interpretation.
Diagnostic analysis
It is the analysis that will answer why a certain phenomenon or behavior happened. It is she who helps to understand the causes and find existing patterns.
Once the patterns are found, it is possible to create solutions to current problems.
Predictive analytics
It is used to assess risks, predict trends and discover what could happen in the future, based on previously obtained data.
If the diagnostic analysis serves to understand what happened previously, the predictive analysis will anticipate events.
Prescriptive analytics
This type of analysis is carried out based on information obtained by other types of analysis, in particular predictive analysis.
It aims to predict situations and events, helping to visualize a future scenario in order to fix, improve or modify issues in different areas of the company.
Exploratory analysis
Correlates the analyzed data and its variables. In other words, it can help find direct associations between researched facts.
It encompasses the discovery of new relationships or facts that were, until then, unknown.
How to implement data analysis in companies?
To implement data analysis in companies, it is necessary to clearly know what the objectives are to be achieved, that is, what the company wants to know with that data.
It is also important to know the KPI (Key Performance Indicator) or the company's key performance indicator.
This tool measures the performance of one or more shares using numbers or percentages. It could be the number of active users on the site or percentage of new users, for example.
Then, it’s time to study the structure of the website, product, application, etc. An effective way to do this is to list all features and divide them into actions taken by users during use.
Creating a tracking plan with everything you want to measure, choosing a tool, implementing that tool to the product or website, are the next steps.
How is data analysis done?
The data analysis process works in six steps:
- Objective identification: Why do data analysis, what type of data analysis should I use, and what data do I plan to analyze are questions to ask in this first step.
- Collect: Once the objectives of the analysis have been identified, it is time to search for data in tables, interviews, surveys, databases, questionnaires, case studies, among other sources.
- Debug: With the data in hand, the process of debugging or cleaning the data begins, which consists of eliminating errors, duplicate numbers and inconsistent information. It is a fundamental step before analyzing the data.
- Analysis: At this stage, software and other data analysis tools are used to interpret and understand the data and obtain conclusions.
- Interpretation: This step consists of interpreting the data and producing the best lines of action based on what was found.
- View: At this stage, it's time to graphically show the results of data analysis in a way that people understand using graphs, maps, lists, or other methods.
5 data analysis tools to know
Data analysis tool is the term used to describe software and applications used by data analysts.
The main data analysis tools are: Excel, Tableau, Power BI, Google Analytics and ThoughtSpot.
- Excel is a simple yet highly effective spreadsheet program for collecting and analyzing data. It is part of the Office suite.
- Tableau is a free platform – with paid features – for visual data analysis, which helps to see and understand data within the concept of Business Intelligence (BI).
- Power BI, from Microsoft, is one of the most used (and one of the first) data analysis tools. It has Machine Learning functionality for data analysis.
- Google Analytics is a set of data analysis tools especially used in digital marketing. It gives information about traffic on a website and user behavior.
- ThoughtSpot It is one of the most modern platforms when it comes to data analysis as it understands natural language through the use of Artificial Intelligence.
Methodologies (methods) for data analysis
The methods for data analysis are as follows: cluster analysis; cohort analysis; regression analysis; neural networks; factor analysis; data mining; text analysis; time series analysis; decision trees; joint analysis.
Quantitative and qualitative data analysis: what’s the difference?
The difference between quantitative and qualitative data analysis lies in the nature of the variables, that is, the characteristics of the data set.
Quantitative or numerical variables are divided into two groups: continuous and discrete.
Continuous variables are those values that can, over a period of time, change.
Length, weight, height, time, proportions, percentages, are some examples. They can be integer or fractional values.
Discrete variables are those that only accept integer values.
Examples include: counting people, number of yes and no answers, number of goals in a championship, among others.
Qualitative variables are divided into two groups: nominal and ordinal.
Nominal variables are those in which the categories do not have a natural order. For example, names, colors and gender.
Ordinal variables refer to categories that can be ordered, such as size (small, medium and large) or level of education (elementary, secondary and higher).
5 common data analysis mistakes not to repeat
Some errors are very common in data analysis and can be easily avoided.
A Junior Stat, Unicamp's junior statistics company (University of Campinas), highlighted some errors in data analysis so as not to repeat them. We selected 5. See below:
- Do not clean data, looking for duplications and errors;
- Not organizing the database, creating tables and establishing a relationship between them;
- Not observing metrics in context;
- Interpreting a pattern incorrectly;
- Using meaningless metrics and not those that really matter to the company.
