Data preparation is the process of cleaning and processing the collected data in a way that is easy to understand and used for analytical and operational purposes. The amount of data handled by enterprises are constantly growing. This makes it incredibly difficult to gain real-time insights from various operational use cases. Data preparation results to be very time consuming for data engineers, analytics and business users. But data preparation is highly important as it can format the raw data and provide useful insights. Data professionals have found that 80% of their work involves preparing data and 76% of data scientists believe that data preparation is a monotonous task but efficient business decisions can be drawn only by organizing the data.
The need for data preparation
Data preparation helps enterprises in so many ways. Some of the important ones are as follows.
After the data has been collected and moved from its source, the errors become much harder to spot, understand and rectify. Data preparation helps in identifying the errors at an early stage before processing all of the collected data.
Increased quality of data
Cleaning, formatting and categorizing data ensures that the quality of data remains high when it is being used to analyze. High-quality data, in turn, will provide high-quality and accurate results.
Arrive at better business decisions
Categorized high-quality data can be processed and analyzed effectively and efficiently. This will offer businesses enough time to arrive at high-quality and necessary business decisions.
Implementation of cloud-based data preparation tools makes the process of data preparation easy and efficient. Additionally, these cloud-native tools can expand equally at the rate of growth of the business. Companies need not be concerned about the infrastructure or their evolutions when it comes to adopting cloud-based data preparation tools.
Improved data usage and collaboration
Cloud-based data preparation tools ensure that the data is always present and available and can be easily accessed without the need for installing additional software. The continuous availability of data allows teams to work effectively by collaborating on a common platform. There are various well-organized online data preparation tools that are available these days. For instance, K2View offers a self-service, automated data preparation tool that can be used for all enterprise use cases, online or offline.
With a defined digital entity schema, the data preparation hub captures all of the attributes for a business entity, such as information about a customer or product across all source systems. The collected data is then cleansed, enriched, masked and transformed according to predefined rules.
K2View’s data preparation hub provides trusted up-to-date and timely insights. These data are quickly analyzed and accessed by everyone in the organization. Additionally, this tool is compliant with the regulatory requirements and is secure, fast and cost-effective.
Steps involved in data preparation
The business intelligence teams and data professionals carefully gather data from various sources like operating systems, data warehouses, and other data sources. The collected data is then analyzed if it is relevant to the business.
Here, the collected data is thoroughly checked to understand the information it contains and the steps needed to prepare it for intended purposes are carried out. The data is further analyzed to find patterns, inconsistencies, missing data and other issues that have to be addressed.
After identifying the issues with the data sets, the identified errors are corrected to create accurate and error-free data sets that can be processed and analyzed. For instance, the faulty data will be removed from the data set and missing values will be filled. The error-free data sets are now formatted, structured and organized in a format that is required for analyzing the data.
The structured data has to be transformed to make it consistent and convert into useful information. Then, the data will be enriched and optimized to provide desired business insights. Finally, to validate the consistency and accuracy of data, automated test routines will be run against the data.
The prepared data is stored in third-party applications like business integration tools where the data will be processed and analyzed. The prepared data can also be stored in data warehouses and repositories that are being used by the enterprise.
Data preparation was initially focused on predictive analytics. At present, it has evolved to be an enterprise-grade tool that enables collaboration between various teams. It also helps in addressing broader business use cases while being easily accessible.