Data preparation enables companies sanitize, enrich, and structure raw data so that it is in the desired format and can support decision-making. Following processing, the information can be used to generate reports, take internal or external actions, make grounding decisions, and more.
There was a time when data preparation entailed many roadblocks or problems owing to lack of proper technology. However, it has become easier with the introduction of new tools that allow users cleanse, analyze, and qualify data on their own.
Data preparation is the process of cleaning and transforming data before processing and analysis. It is an important step that involves reformatting data, allowing corrections to data and merging data sets simultaneously.
Though a majority of data professionals or business users find the process of data preparation as tedious, it serves as a prerequisite to put data in context for extracting actionable insights and eliminating bias caused due to poor data quality.
The intricacies of data preparation vary based on organization, need, and industry, but the original framework remains the same. The common steps are:
Collect data: The data preparation begins with collecting the right data. Data can be collected from an existing data catalog or can be added ad-hoc.
Discover and evaluate data: Followed by the data collection process, this step involves discovering each dataset. Data discovery requires skills in understanding data relationships along with data modeling in addition to employing data analysis and guided advanced analytics functions to extract insights.
Cleanse and validate data: Data cleaning is the most time-consuming step of data preparation, however, this step is necessary for removing faulty data, filling in gaps, conforming data to a standardized pattern, and masking private or sensitive data entries. After data cleaning, it must be validated to test for errors.
Transform and enrich data: In this step, data is transformed by updating format or value entries of data for achieving a well-defined outcome or reaching a wider audience. After transforming, data is enriched that helps companies extract deeper insights.
Storage: When data is prepared, it can be stored or channelled into a third-party application including a business intelligence platform for further analysis.
As per a report, data scientists spend 60% of their time on cleaning and organizing their data to make precise business decisions.
Data preparation is responsible for:
Fixing errors quickly: Data scientists can determine errors or fallacies before processing with the help of data preparation. Once the errors are detected, they can be removed right away.
Generating superior data: Cleaning and transforming data streams prior to processing improves the quality of data substantially. When data is of supreme quality, it helps to drive organizational success due to reliance on fact-based decisions instead of habit, convenience, or human intuition.
Making better business decisions: When the data incoming data is cleaned properly and then processed efficiently, it helps organizations make high-quality business decisions. This fast and efficient decision-making capability enables companies make an everlasting impression on their customers and partners.
Data preparation offers even greater benefits as data and data processes move to the cloud, such as:
Amplified Scalability: Data preparation in the cloud grows at the pace of business and companies need not worry about the underlying infrastructure or their evolutions.
Improved data usage and collaboration: Cloud data preparation does not need to go through an installation process that allows business teams collaborate for better and faster outcomes.
Sustained future: Since cloud data preparation scales automatically, it can adapt new capabilities or fix issues proactively. This helps enterprises stay ahead of the innovation curve, avoiding delays and added costs.
Data preparation requires immense investment of resources. It is also time-consuming and difficult. But, with the right kind of self-service data preparation tools, data analysts or data scientists can easily execute data preparation. These tools have machine learning capabilities that help simplify the data preparation process.
However, not all the tools are efficient, and so selecting one out of so many can be daunting. To help you find the best data preparation tool, we’ve made a list of features:
Contact Adeptia’s professionals to get started with data preparation.