In the phases of preliminary data preparation, it is sometimes difficult to see the difference between Data Preparation and Data Exploration. However, data preparation and data exploration require a different approach. Here are the main differences between Data Preparation and Data Exploration.
The emergence of big data
With the development of Big Data, many phases of preliminary data preparation are implemented. A whole terminology has developed, and it becomes difficult for non-specialists to detect the nuances between different terms. Among the preliminary phases, Data Preparation and Data Exploration occupy a large place. This is the way in which raw data is integrated and processed in BI software.
BI is Business Intelligence, which, as the name suggests, is used by business managers and those who are generally referred to as decision-makers.
BI represents all the means by which data is collected and modelled to assist in decision-making. Ultimately, BI provides an overview of an activity.
Data Preparation
Data Preparation is the very first phase of a business intelligence project. It is the phase of transforming raw data into useful information that will later be used for decision-making. Data sources are merged and filtered. They are finally aggregated, and the raw data are subject to the calculation of additional values.
Data Preparation is mainly the phase that precedes the analysis. A graphical user interface that makes the preparation usable is preferably required. Data Preparation is mainly used for an analysis of business data. This involves the collection, cleaning, and consolidation of data. All this takes place in a file that can then be used for the analysis.
This phase is of course essential for filtering unstructured and disordered data. Data Preparation also makes it possible to connect data from different sources, all in real time.
Another important advantage of Data Preparation is that it allows you to manage the data collected from a file and to obtain a quick report of this data.
The various data preparation procedures include data collection, which is the initial process for any organization or business. It is at this stage that data is collected from a variety of sources. These sources can really be of any type.
The next step is data discovery. It is then important to understand the data collected in order to classify it into different sets. As the data is often very large, filtering the data can be very time consuming.
It is then equally important to clean and validate the data (data cleansing) in order to remove and discard anything that is not useful for later steps when decision-making is required. Unnecessary or aberrant data should be removed at this stage. Appropriate models should be used to refine the data set. A lock should be used to protect sensitive data.
Once the data has been cleansed, it must go through the test team who will perform all necessary checks. The next step is to define the format of the value entries in order to make the set accessible and understandable to decision-makers. Once all these procedures have been carried out, the data remains to be stored. The analysis tools can then be implemented.
Preparation Data has many advantages. Among other things, it allows a quick response to correct possible errors. The quality of the data is improved, allowing for a more efficient and faster analysis.
Data Exploration
Data Exploration is the stage following the preparation phase. The prepared data is then analysed to enable the questions arising from the data preparation to be answered. The data provided is explored interactively. They are reorganized in such a way that they are presented in an understandable way and used by decision-makers. It is therefore a question of exploring data that has not yet been transformed.
Exploration is necessary for decision-makers, who thereby obtain information on data that was previously difficult to perceive. Data mining is in fact the first step in data analysis. It is from this phase that it becomes possible to plan appropriate decisions for the organization or company. This involves identifying and summarizing the main characteristics of a set of data.
A team of experienced analysts is needed to handle visual analysis tools and statistical management software. Sometimes it is necessary to use both manual and automated tools.
Data can be explored manually or automatically. Automated methods are, of course, popular because of their accuracy and speed. Data visualisation tools are particularly effective. Manual data mining allows you to filter and explore data in files such as Excel. Scripting is also used to analyse raw data.
Among the techniques used for Data Exploration is univariate analysis, which is the simplest technique, since only one variable is present in the data. The data is analysed one by one. The analysis here depends on the type of variables, which can be categorical or continuous as the case may be.
Bivariate analysis involves the analysis of two variables. The empirical relationship between each of them is calculated. An analysis that includes more than one variable can be called multivariate analysis. There is also principal component analysis, based on the conversion of correlated variables into a smaller number of uncorrelated variables.
After the exploration comes the discovery of the data. This is an inspection of trends and events to create visualizations to present to the sales managers to be met. Several tools exist to facilitate data exploration and visualisation. Tableau and Power BI are frequently used.
The quality of the input during the exploration process will determine the quality of the output. It is therefore important to apply a very versatile input value so that the output remains constant.
In order for Data Exploration to lead to the construction of a valid predictive model, it is necessary to proceed in stages. First, it is important to identify the variables. The input and output variables must first be identified. Next, the type of data and the category of variables must be identified.
The next step can be either univariate or bivariate analysis. Then the specialists proceed with the processing of missing values and the treatment of outliers. After the variable transformation, the creation of variables is the last step.
Different processes
Data Preparation and Data Exploration are therefore distinct and complementary steps.
Both processes result in a set of exploration tools through which decision-makers come to understand the database in real time. Once the exploration is complete, the data structure and values become clearer in a very short period of time.
If you wish to optimise the processing of your data, Ryax can accompany you throughout the process.
The Ryax Team.