Data Analysis Steps – A Basic Guide
Your business problem or research question will be answered
using data. There are many ways of collecting data.
Many ways of collecting data
You can generate data yourself and have an eye on the data requirements from scratch, e.g. with a survey data analysis. Or you can use historical sources. Depending on the research question, you should choose the right data. For example, it makes no sense to study covid contagion in closed rooms if some data are of the alpha variety and others of the delta variety. The data analysis process is always similar, no matter if you do a customer experience study or a party data analysis. Which tools could be helpful depends on the type of data. Qualitative or quantitative research etc.
Step in the data: Here are the 8 data analysis steps!
Step 1: Preparation of the data
Data preparation is usually the most important step for the data analyst and requires the most effort. Different data sources have to be merged, the structure of the data has to be clarified and understood. It often makes sense to transfer the data to a database, so that subsequent additions to the data are also more successful. ATLAS.ti is a powerful tool to help you along the way.
Step 2: Cleaning data
Duplicate, incorrect or incomplete raw data records should be identified, corrected or removed. The errors can be caused by incorrect input or, for example, by transmission errors. Errors can also occur when several data sources have to be merged: “record matching”, also called “record linking”, is a sometimes quite complex search for units when one-to-one identifiers are missing.
Step 3: Data validation
For each single variable, it can be checked if the given range of values is kept and if the distribution of the values is plausible. For pairs or groups of variables that are dependent on each other, it can be checked whether expressions of one variable are plausible in dependence on another variable. Corresponding validation rules must be formulated. In this way, further incorrectly recorded data values can be found and corrected or removed.
Step 4: Data selection
This step is important, for example, for data from field studies. For experimental data, you must not perform any selection. Only data that are meaningful for the research question should be considered. Thus, depending on the goal of the study, selection must be made, or an appropriate grouping of the data must be done.
Step 5: Analyze Data: Exploratory data analysis
This is the analysis of a data set without a pre-formulated statistical model. The finding of new hypotheses should be facilitated in this way. These hypotheses must then be tested on a new data set. For large data sets, it is useful to randomly split them into several parts to use one subsample for data exploration and another for hypothesis testing. Analysis tools can be very useful. There is open-source software or professional tools.
Step 6: Model and interpretation
First, a conceptual database schema is created that differentially structures the observable world into objects, properties, and relationships. This model is illustrated with text and graphics. Subsequently, a logical database schema can be created. Here, the objects are defined in more detail; for example, field structures, field contents, and field formats are specified that emphasize the detailed nature of the objects.
Step 7: Data visualization
A successful visualization enables an in-depth understanding of the main results of the data analysis. The spectrum of visualization ranges from static illustrations to elaborately constructed, dynamic, interactively controllable illustrations.
Step 8: Data presentation
The final step is to record the analysis in a written report. Figures and tables are used to visualize the results. It is often also good to provide the data in the form of a database with individual query modules. This makes it possible to add newer data, update existing analyses, or perform your own analyses. The results should be prepared differently depending on the target group. Social media results for the general public require a different form than a presentation to a data science expert target group.