SAS Data Management Description
Regardless of the location of your data—whether in cloud environments, traditional systems, or data lakes such as Hadoop—SAS Data Management provides the tools necessary to access the information you require. You can establish data management protocols once and apply them repeatedly, allowing for a consistent and efficient approach to enhancing and unifying data without incurring extra expenses. IT professionals often find themselves managing responsibilities beyond their typical scope, but SAS Data Management empowers your business users to make data updates, adjust workflows, and conduct their own analyses, thereby allowing you to concentrate on other initiatives. Moreover, the inclusion of a comprehensive business glossary along with SAS and third-party metadata management and lineage visualization features ensures that all team members remain aligned. The integrated nature of SAS Data Management technology means you won't have to deal with a disjointed solution; rather, all components, ranging from data quality to data federation, operate within a unified architecture, providing seamless functionality. This cohesive system fosters collaboration and enhances overall productivity across your organization.
SAS Data Management Alternatives
DataHub
DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.
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AnalyticsCreator
Accelerate your data journey with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, or blended modeling approaches tailored to your business needs.
Seamlessly integrate with Microsoft SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline creation, data modeling, historization, and semantic layer generation—helping reduce tool sprawl and minimizing manual SQL coding.
Designed to support CI/CD pipelines, AnalyticsCreator connects easily with Azure DevOps and GitHub for version-controlled deployments across development, test, and production environments. This ensures faster, error-free releases while maintaining governance and control across your entire data engineering workflow.
Key features include automated documentation, end-to-end data lineage tracking, and adaptive schema evolution—enabling teams to manage change, reduce risk, and maintain auditability at scale. AnalyticsCreator empowers agile data engineering by enabling rapid prototyping and production-grade deployments for Microsoft-centric data initiatives.
By eliminating repetitive manual tasks and deployment risks, AnalyticsCreator allows your team to focus on delivering actionable business insights—accelerating time-to-value for your data products and analytics initiatives.
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Centralpoint
Gartner's Magic Quadrant includes Centralpoint as a Digital Experience Platform. It is used by more than 350 clients around the world, and it goes beyond Enterprise Content Management. It securely authenticates (AD/SAML/OpenID, oAuth), all users for self-service interaction. Centralpoint automatically aggregates information from different sources and applies rich metadata against your rules to produce true Knowledge Management. This allows you to search for and relate disparate data sets from anywhere. Centralpoint's Module Gallery is the most robust and can be installed either on-premise or in the cloud. Check out our solutions for Automating Metadata and Automating Retention Policy Management. We also offer solutions to simplify the mashup of disparate data to benefit from AI (Artificial Intelligence). Centralpoint is often used to provide easy migration tools and an intelligent alternative to Sharepoint. It can be used to secure portal solutions for public sites, intranets, members, or extranets.
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Segment
Twilio Segment’s Customer Data Platform (CDP) provides companies with the data foundation that they need to put their customers at the heart of every decision. Using Twilio Segment, companies can collect, unify and route their customer data into any system. Over 25,000 companies use Twilio Segment to make real-time decisions, accelerate growth and deliver world-class customer experiences.
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Integrations
Company Details
Company:
SAS Institute
Year Founded:
1976
Headquarters:
United States
Website:
www.sas.com/en/software/data-management.html
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
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In Person
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Online Support
SAS Data Management Features and Options
Master Data Management Software
Data Governance
Data Masking
Data Source Integrations
Hierarchy Management
Match & Merge
Metadata Management
Multi-Domain
Process Management
Relationship Mapping
Visualization
Data Management Software
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
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