Nowadays, the vastness and abundance of data and information has made it difficult to distinguish the correct data from the incorrect data among this large amount of data. To perform strong analysis, data scientists and analysts more than anything need to ensure the health and quality of data and information, so the need a mechanism that can help them identify the right data and its quality.
This issue has made the preparation of data and information before using them to be an important and vital aspect to perform strong analysis. Methodical data preparation eliminates the problems that may cause errors during data processing and provides the obtained data to the user for the next steps.
Data preparation in Bigpro1
Various sections of pre-processing and data preparation are designed in such a way that the user can easily apply the relevant operations on his project without the need to have specialized knowledge in this field.
To prepare the data in the Bigpro1 dashboard, an option has been placed in the data mining dashboard menu. In this section, after selecting the file with the correct format specified in Bigpro1, you will have access to all the following operations:
- Management of missing values
- Managing outlier data manually
- Managing outlier data with algorithms
- Data conversion
- Reducing data dimensions
- Feature selection
- Unbalanced data management
* In addition, there are two options for error values and data tabular display in the data preparation and pre-processing section for the user.
What is data preparation?
Data preparation is the process of cleaning, aggregating, transforming and enriching raw data, including unstructured and big data, before data processing and analysis.
Accurate data preparation is an important and very key part of successful data analysis; which mostly includes data modification (data correction), formatting and combining data sets and ultimately leads to data enrichment. Although this is very time-consuming for business owners, the data obtained is used in business analytics projects.
Advantages of data preparation
For data scientists, the data preparation process is the worst part of their job, and they have to spend a lot of time finding and cleaning data. But the advantage of data preparation is that they end up spending less time finding and structuring data, therefore, they have more time to focus on data mining and data analysis.
Although the presence of software such as the online data preparation tool in Bigpro1 can improve the slowness of the process, when data is of excellent quality it can be easily processed. Quality data leads to insights that help the organization make better, more accurate and efficient business decisions.
Data preparation process
The data preparation process consists of several separate steps, which include the following:
- Data collection
- data discovery
- data cleaning
- data transformation and data enrichment
1- Data collection:
To prepare and pre-process the data, in the first step, the required data and their sources must be identified, the obtained data may be structured or semi-structured. This data should be collected in such a way that it can be used for various business purposes. In order to enter the next stage, it is necessary to integrate these data. Finally, a consistent and constant access should be created to use the data so that with their help, powerful and accurate analyzes can be performed.
2- Data discovery:
The second step in data preparation is data discovery and exploration. Data experts at this stage should examine and explore the obtained data to understand how to analyze it. Discovered data profiling helps to identify features of the data set such as identifying patterns, anomalies and missing data.
3- Data cleaning:
In the data cleaning stage, which is the third step of data preparation, data errors are identified and cleaned. If done traditionally data cleaning takes a lot of time in data preparation, but it is very important to remove bad data and fill in missing data.
Data cleaning creates a complete and accurate data set to provide valid answers when analyzed. This step can be done manually for small data but requires a mechanized method for real data sets.
Data cleaning includes the following: removing duplicate and outlier data, removing extra charges, correcting input errors, removing or filling in missing values, matching data to a standardized pattern, masking private or sensitive data such as names or addresses.
After the data cleaning stage, the process of data preparation and pre-processing up to this stage should be tested for errors so that if an error is found in this stage, it can be fixed before entering the next stage.
4- Data transformation and enrichment:
Data have different forms and structures, in order to reach a unified and usable structure, changes must be made in their structure. This variation varies according to the language or software that analysts use to analyze their data. Enriching and optimizing data is adding and connecting data with other relevant information to create deeper business insights. Data preparation is a key piece of valid and strong analytics.
Importance of data preparation
Business owners and leaders can only make as many decisions as the data they have and support. Analysts can trust their data and perform quality and accurate analyzes only with comprehensive and accurate preparation.
Accurate and meaningful analysis in the data preparation process leads business owners and leaders to deeper insights, which leads to better results. Data preparation examines and solves various issues related to data (such as: inconsistent, incomplete, low-value data, etc.). This will allow accurate and high-quality data to be obtained and leads to correct predictions.
Data preparation strategy
Accessibility: Anyone, regardless of skill level, should be able to access data securely from an authentic source.
Transparency: Anyone should be able to clearly see, review and modify each stage of data pre-processing.
Repeatability: The process of data preparation is a tedious process because it takes a lot of time, which is why successful preparation strategies invest in solutions that are built for repeatability.
With the right solutions, analysts and business owners can simplify the data preparation and pre-processing process and spend more time getting valuable business insights and results.
Data preparation software
Bigpro1 dashboard is a collection of several very important tools in the field of data science, artificial intelligence and machine learning, data analysis and several other important tools. One of the most important tools of bigpro1 is the data preparation and preprocessing tool.
This data preparation tool is one of the most popular online data preparation and analysis software. This bigpro1 tool allows users to analyze massive amounts of data and gain deep insights wherever they are.
