![]() This kind of transformation could be something as commonplace as encrypting data transfers. Modifying data can improve the security of data moving between systems. Security and complianceĭata transformation is not limited to ingestion pipelines or the preparatory phases of analytics projects. For example, they will work with data scientists to define how to transform data for machine learning algorithms. Data warehousing requires models to ensure the data pool meets quality and consistency standards.Įngineers also rely on data models when working with their customers to develop data projects. These models characterize the system’s data sources, define the destination’s format and other data standards, and document the system’s transformation requirements.Ī data model is an essential reference for all transformation processes. ![]() Data modelingĭata models are visualizations of data flows within an information system. Data becomes more accessible, empowering more people to use data and creating a data-driven decision-making culture. Ingesting raw data through transformation pipelines results in clean, consistent data values and metadata that any analyst can use. Few users have the skills to clean and enhance data from relational database management systems, much less real-time clickstreams. Combining disparate data sources is a data engineering challenge. Data analysisĭata transformation promotes the democratization of data access. Whether built upon a data lake or an on-premises or cloud data warehouse, this source system combines different sources into a holistic resource for generating business insights. Integrating an enterprise’s dispersed and varied data sources creates a central repository for systems and people to access in support of the business. Standardizing the schema, metadata, and other data properties improves the navigability of data infrastructure and speeds discovery. Something as simple as competing date formats throws obstacles in users’ paths.Įnsuring consistency among the datasets in the company’s data warehouses reduces the time users spend preparing data so they can focus on generating insights.ĭata transformation also makes data about the data more consistent. Data science relies on the aggregation of big datasets. Business intelligence projects usually draw on multiple sources. Data consistencyĪpplying company-wide data standards boosts analysis productivity. Supplemental data sources can add context that improves downstream use cases. Other sources may replace data missing from the original. Missing values, duplicate data, outliers, and corrupted values are fixed or mitigated to improve data quality.ĭata enrichment offers further quality improvements. ![]() The process of transforming data from multiple sources to meet a single standard improves the efficiency of a company’s data analysis operations by delivering the following benefits: Data quality improvementĬleaning raw data values is a basic function of transformation processes. This project-by-project approach consumes resources, risks variations between analyses, and makes decision-making less effective. Without data transformation, data analysts would have to fix these inconsistencies each time they tried to combine two data sources. Organizational format, structure, and quality variations occur as business domains and regional operations develop their own data systems. Each application and storage system takes slightly different approaches to formatting and structuring data. Data transformation remains indispensable because enterprise data ecosystems remain stubbornly heterogeneous despite decades of centralization and standardization initiatives.
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