What is Data Preparation?
Today, we will find out more information about the other process related to data sources, format industry, data professionals, and the new cloud-based technology leader - data preparation.
Table of Contents
- Data Preparation for marketers
- Benefits of Data Preparation for ETL
- Beginning with Data Pre Processing
- Frequently asked questions
Data Preparation for marketers
Data preparation is the process of cleaning and converting raw data before processing and analysis. It's an important step before the process that often includes reformatting data, performing data corrections, and combining data sets to improve data.
Data preparation is often a time-consuming process for data practitioners or business users. Still, it is needed as a requirement for putting data in context to transform it into insights and remove bias caused by poor data quality. For example, standardizing data formats, improving source data, and/or reducing outliers are all part of the data preparation process.
Self Service Data Preparation
Data preparation is a critical process, but it also requires a significant expenditure of resources. Data scientists and data analysis estimates that data preparation consumes 80% of their time rather than research.
Is the data team able to devote sufficient time to data preparation? What about companies that don't have any data scientists or analysts on staff? This is where self-service data preparation tools, such as Whatagraph, come in. Data planning is made easier by cloud-native platforms with machine learning capabilities to avoid data issues in the data warehouse. This guarantees that the business users will focus on analyzing data rather than simply cleaning it. However, it also allows business practitioners, who may lack specialized IT skills and expertise to run the process independently, to participate in data preparation. To get the most out of a self-service data preparation tool, search for a framework that includes: Data research and access from every dataset from Excel and CSV files to data centres, data lakes, email addresses, and cloud-based applications.
Also, it involves functions for cleaning and improvement, as well as auto-discovery, standardization, profiling, export functions to files (Excel, Cloud, Tableau, etc.). It includes managed export to data warehouses and business applications, as well as design and productivity features like automatic documentation, versioning, and operationalizing into ETL processes.
Data Preparation For Business Users
Data preparation, which was initially based on analytics, has grown to address a much wider collection of use cases and can be used by a wider variety of users and different types of organizations. It increases the personal productivity of those who use it, and it's developed into an enterprise tool that encourages collaboration between IT professionals, data experts, and business users.
Benefits of Data Preparation for ETL
Although 76 % of data scientists claim that the data prep process is the most difficult aspect of their work, effective and reliable business decisions can only be made with clean data.
Here are the main benefits of Data Preparation for ETL:
Fix errors quickly
Data preparation helps find errors before processing and to bring more value to the work method and industry. After data has been removed from its original source, these errors become more difficult to understand and correct.
Produce top-quality data
Cleaning and reformatting datasets guarantee that all data used in the analysis will be high quality.
Make better business decisions
Higher quality data that can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.
Additionally, as data and data processes move to the cloud, data preparation moves with it for even greater benefits, such as:
Cloud data preparation can grow at the pace of the business. An enterprise doesn’t have to worry about the underlying infrastructure or try to anticipate its evolutions.
Data sources of cloud data preparation upgrade automatically so that new problem fixes can be used as soon as they are released. This allows organizations to stay ahead of the innovation curve without delays and added costs.
Stimulated data usage and collaboration
Doing data prep in the cloud means it is always on, doesn’t require any technical installation, and lets teams collaborate on the work for faster results.
Beginning with Data Pre Processing
Data preparation improves data consistency for analysis and other data management activities by removing errors and normalizing raw data before processing. It usually takes a long time and can require special skills.
However, with the help of a smart data planning platform, the method has become quicker and more available to a broader range of users. Check out some starter guides to learn more about data preparation. When you're set, just signup for a free trial of Whatagraph, which can assist in the preparation of data for presentations and the best decision making for self-service or business purposes.
Now, let's have a look at the FAQ section to receive more knowledge about the four main data preparation steps, to find out what exactly is data preparation tool and why it's important for business insights and fields.
Frequently asked questions
Q: What are the Data Preparation process four main steps?
1. Collect information - Finding the correct data is the first step in the data preparation process. This can be derived from an existing data catalogue or applied ad hoc.
2. Find and evaluate data - It's critical to discover each dataset after collecting the data. This stage is all about getting to know the data and learning what needs to be done before it can be used in a specific way.
3. Data cleansing and validation - Cleaning up the data has historically been the most time-consuming aspect of the data preparation process. Still, it is critical for reducing inaccurate data and filling in gaps. Removing extraneous data and outliers are important tasks here. After the data has been cleansed, it must be checked for errors. During this stage, an error in the system will become visible and must be corrected before the process.
4. Data transformation and improvement - Transforming data is the process of changing the format or meaning entries to achieve a specific result or to make the data more understandable to a wider audience of users. Improving data is the process of adding and linking data with other relevant information to provide deeper insights. Once prepared, the data can be stored or routed through a third-party program, such as a business intelligence platform, to allow for processing and analysis.
Q: What is a data preparation tool?
Data prep tools are a separate class of software that allows business analysts to avoid data warehouses, transfer and prepare data themselves before the analysis. Data preparation tools can scan for and access data within an enterprise. It will combine it with other external data sets and perform necessary data cleansing and conversions before feeding the data back into business intelligence systems.
They also allow users to recognize anomalies and trends, as well as improve and review the data quality of their decisions in a repeatable manner. There are many self-service data prep tools that can take every user one step closer to the world's highest value of success. Every goal is achievable we have the right visualization tools and good support. That's we are happy to announce that Whatagraph has created the perfect solution here.