Data flows are essential for understanding how information moves through a system. They help in identifying bottlenecks, optimizing processes, and ensuring data integrity.
Our data solutions can be deployed both on-premises and in cloud environments. Utilizing cloud infrastructure offers significant benefits, including scalability and flexibility, particularly for data storage and processing capabilities.
Data flows refer to the movement of data between different systems, applications, and platforms. This movement can involve various transformations, aggregations, and processing steps, making it crucial for organizations to understand and manage their data flows effectively. By doing so, they can ensure data quality, optimize their data assets, and make informed business decisions.
Data flows can range from simple transfers between two systems to complex interactions involving multiple applications and data sources. These flows can be triggered by various events, such as user interactions, scheduled tasks, or real-time data feeds. Additionally, data flows can encompass different data formats, including structured, semi-structured, and unstructured data. Understanding these flows is essential for maintaining data integrity and supporting organizational goals.
Identify, and catalog the flow of personal and sensitive data, without time-consuming data mapping.
Cataloging data flows offers numerous benefits to organizations, including:
Better Decision-Making: Analyzing data flows provides insights into business processes, helping organizations identify areas for improvement and make data-driven decisions. This leads to more efficient and effective operations.
Maintain a centralized catalog of all compliance-relevant data flows in one place. Business metadata plays a crucial role in enhancing data discoverability and usability by providing useful context through labels and descriptions written by data users.
Visually trace data from policy to implementation through intuitive graphs and charts.
Organizations previously needed to manually analyze code and maintain static documents to track changes and their impacts on data systems, emphasizing the inefficiencies that automated lineage discovery can eliminate.
Choose from 100s of predefined flow & discover your flows from applications and systems. For example, you can use a predefined flow configuration to automatically sync customer data from Salesforce to an Amazon S3 bucket.
Data assets are the valuable data that an organization collects, stores, and uses to support its operations, decision-making, and strategic initiatives. Effective data management is critical to ensuring that these data assets are accurate, complete, and accessible to authorized users.
Data management encompasses various activities, including data ingestion, data validation, data profiling, data storage, data lineage, and metadata management. These activities help ensure that data is properly managed, secured, and compliant with regulatory requirements. By effectively managing their data assets, organizations can maximize the value of their data and support their business objectives.
A data catalog is a centralized repository that stores metadata about an organization’s data assets. Creating a data catalog involves several key steps:
Data flow specification refers to the process of defining and documenting data flows, including the data sources, data transformations, and data destinations. This specification provides a clear blueprint of how data moves through the organization.
Data flow lineage, on the other hand, involves tracking the origin, processing, and movement of data. Understanding data lineage is critical for identifying data errors and inconsistencies, ensuring data quality, and maintaining compliance with regulatory requirements. By specifying and tracking data flows, organizations can gain a comprehensive understanding of their data ecosystem, enabling them to manage their data assets more effectively.
Data flow mapping highlights storage of sensitive data, the security gaps, and compliance risks. It also maps the data transfers between systems, applications, and storage. This allows identifying who and what processes data at each stage of the workflow. SQL queries are essential for extracting and interpreting the data lineage from various sources, as well as for querying datasets within platforms like Amazon Athena. Data flow mapping is a critical element of security & privacy compliance. And, it is a focused asset management mechanism for your compliance needs.
Data discovery is a deeper scan of actual content within all systems. It scans to catalog, classify, and index the data. This enables understand the types of data that exist. Data discovery provides details on sensitive information, associations between data, and how data is structured. It gives visibility into dark data.
The following table showcases account records from Salesforce, serving as a source reference for a flow that facilitates data transfer between Salesforce and Amazon S3.
A key requirement of security & privacy regulations is accurate and timely disclosures and public notices. While data mapping may have a useful purpose, Essert’s data flow catalog function gets you the most accurate consolidated information for policy disclosures and notices. Your business is able to update your policies and notices automatically and deploy them using Essert’s API-ready policy management.
Essert also provides a comprehensive ROPA application. Whether you have 250 or more employees, process special data, or process data periodically, ROPA becomes a checkmark compliance activity. Essert helps you auto-generate an ROPA assessment periodically, avoiding fines and lawsuits.
