Big data governance
Big data governance is a framework encompassing processes, policies, metrics, and standards for large data volume management. Big data governance determines how to store, process, and use big data to ensure its high quality, availability, usability, integrity, and security throughout its lifecycle.
Core components of a big data governance framework
Big data governance frameworks differ across companies as they are usually adapted to companies’ specific data management requirements and applicable industry standards and regulations. Here are the key elements of a typical big data governance framework.
Data cataloging & classification
To facilitate efficient discovery of enterprise data, companies create a data catalog, which is a detailed inventory of data assets that organizes and classifies them using metadata and data governance tools. Data is typically categorized based on its sensitivity and importance to regulate its further use. Businesses can also implement a data glossary with commonly used business terms to ensure their consistent usage within the organization.
Data stewardship & ownership management
This is the practice of assigning specialists, such as data stewards and data owners, responsible for ensuring big data privacy, quality, and accessibility across the organization, developing big data governance strategies, and ensuring employee adherence to the big data governance framework. It also involves establishing data contracts to define rules for using data from trusted sources by different stakeholders.
Master data management
Master data management includes practices for creating a consistent view of key enterprise data assets, or master data, including product, customer, employee, and supplier information. The goal is to provide all business units with a single source of truth to prevent data redundancy and information silos.
Data security & sharing control
This component encompasses various data security measures, such as data encryption, masking, tokenization, granular access controls, and more. These measures are intended to ensure data safety during usage and sharing and prevent sensitive data from being exposed to unauthorized parties.
Data quality management
The activity involves ensuring the high quality of big data, including its accuracy, completeness, consistency, timeliness, validity, and uniqueness. For this, companies leverage tools for data profiling, cleansing, validation, and quality monitoring, as well as metadata management.
Data lineage
This is the process of tracking data flows across systems to determine data origins, transformations, and how it’s used. This allows stakeholders to get an end-to-end view of the data lifecycle for streamlined data audit and root cause analysis of any data issues.