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How Data Profiling Improves Data Quality – 3 Important Steps | Saras Analytics
Data Engineering

How Data Profiling Improves Data Quality – 3 Important Steps

4 minutes read


Table of Contents

Data profiling is the process of evaluating the quality and structure of data sources to get a complete and consolidated picture. Data profiling confirms that data columns are filled with the required types of data. If a profile reveals data issues, you have to set a few steps in your data quality project to fix those issues. Data profiling supports good data governance.

Why Do You Need Data Profiling

Data security should not be endangered before starting a project. Studies say that data integration projects like any other IT projects are prone to the same problems such as time and budget overruns, outright project failures, and tradeoffs between quality and deadlines.

Databases are complicated, so it is difficult to analyze large volumes of data. The process of interpreting the source data can also be laborious and prone to error. Before you use data in integration, cloud data warehouse, CRM, ERP, and business analytics applications; try to understand its content, quality, and structure.

Most data integration enterprises depend on third-party sources to provide knowledge of the data. The information from these sources such as documentation, source programs, and existing data models, are incorrect, or outdated. Invalid data require several iterations to fix. You need to develop methods to ensure improvements and validate the accuracy of source data. As a result, a lot of labor and budget may be wasted on manual data analysis techniques.

The IT team might build a new cloud data warehouse, or company management needs relevant information for strategic and trusted decision making; a thorough data profiling is required to assess the quality and characteristics of the source data.

3 Important Steps of Data Profiling to Preserve Data Quality

The data profiling process aims to provide accurate and complete metrics for analyzing the data. Since its initial design, there will be several alterations to the original data, and the existing documentation may no longer be relevant. Following steps are mentioned below to discover the true quality of the source data and quickly take necessary actions to preserve data quality that is fit for your business.

Step 1: Data Preparation

The preliminary step in data profiling and structuring is the preparation of source data. Daton, our automated data pipeline can access millions of rows of data for analysis. This enables users to profile data from almost any data sources like Amazon S3, Snowflake, Google Big Query, Oracle, and Salesforce.

Step 2: Profiling of Data

The second stage is where the actual data profiling process takes place. It is an interactive process of analyzing data between the user and the software to discover the true structure and quality of the data. The generated by the Cloud Data Profiling, allow them to arrive at a model that is compatible with the source data and also relevant for business. Data analysts can perform various analysis models by combining data profiling with data quality rules, address verification, cleansing and standardization rules.

Step 3: Monitoring Data Quality

Several companies use data profiling to improve data quality within applications and business processes as a whole. But this process fails to achieve long-term data quality improvement as the entire organization might not be employing it properly. Data quality issue requires an enterprise-wide approach. By empowering business analysts, and Managers, Daton allows ownership of the data quality process so that the business can maximize the return on trusted data.

Enterprises that are involved in application modernization, data integration, data migration, or data consolidation methods, should have a thorough understanding of the data sources. The major benefits of Data profiling are:

  • Accurate source system knowledge
  • Improves enterprise data accuracy and quality
  • Facilitates dynamic application modernization, data integration, and migration.
  • Promotes the integration of multiple data sources
  • Minimizes risk and cost in data management projects
  • Reduces overruns in business applications projects
  • Enhances the productivity of data management projects
  • Decreases development costs by thoroughly analyzing data content, quality, and structure.

Good Data Profiling Will Let You Answer Important Business Questions

An effective data profiling will let you answer the following questions correctly:

  • Does your company have access to the necessary data required for the completion of a project on time and a budget?
  • Does the data have consistency and accuracy for your business requirements?
  • Will the relationship among the data elements support the business needs?
  • Will you be able to integrate, consolidate and pivot the data for usable reports?
  • Which data needs to be cleaned?
  • Which data needs to be transformed?
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