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Building Automation Into Your Data Quality Initiatives
Data Quality
With the growing volume and complexity of datasets entering organizations, automation is required to scale data quality practices. Data and analytics technical professionals should use this research to learn how AI and metadata will support the automation of data quality processes.
Assessing Cloud Data Integration Options
Data Integration
The proliferation of data across on-premises and cloud environments has prompted organizations to fortify their cloud data integration strategy. Data and analytics technical professionals need to craft an adaptable CDI architecture that optimizes integration of heterogeneous and multifaceted data.
2021 Planning Guide for Data Management
Data Management
Data and analytics technical professionals are struggling to understand, prioritize and apply innovations to deliver business results. This document introduces the technology, platform and process trends in data management for 2021, and guides organizations toward the right technical decision.
The Definitive Master Data Management Toolkit
Become a Master Data Management pro with 25+ curated guides, templates and whitepapers.
Demystifying the Data Fabric
Data Fabric
In a world where global events can suddenly affect the need for new data, data management agility becomes mission-critical. Data and analytics technical professionals can use this research to determine whether a data fabric can be used to provide such agility in their organization.
Data Quality Fundamentals for Data and Analytics Technical Professionals
Data Quality
As the volume and variety of data continue to grow, data and analytics technical professionals continue to struggle with delivering quality data to consumers. Use this research to understand what quality means and how data management tools work together to achieve high-quality data.
Solution Comparison for 7 Data Fabric Offerings
Data Quality
Data fabric is quickly becoming the next major data management and data integration pattern to consider. This document establishes a framework to be used by data and analytics technical professionals when evaluating data fabric offerings and evaluates seven representative offerings.
Market Guide for Active Metadata Management
Data Quality
The increased demand for orchestrating existing and new systems has rendered traditional metadata practices insufficient. Organizations are demanding “active metadata” to assure augmented data management capabilities.
Cool Vendors in Data Management
Data Quality
Augmented capabilities are becoming the major differentiators for today’s data management solutions. These Cool Vendors offer data and analytics leaders ways to connect, ingest, analyze and share data more quickly and at a lower cost.
How to Activate Metadata to Enable a Composable Data Fabric
KYC - Know Your Customer
Existing data management solutions contain an abundance of metadata that most organizations find difficult to identify, much less analyze. Data and analytics leaders can reuse existing metadata to build the data fabric for the enterprise and simultaneously get real value from enterprise metadata.
How to Monetize Data Assets With Your Data and Analytics Service Provider
Data Fabrics
Data monetization is a significant opportunity for digital business success, yet data and analytics leaders struggle to create value from their data assets. Partnering to develop a co-creation model with data and analytics service providers can accelerate the initiatives for business value creation.
Smart Data Sharing Requires Mapping Use Cases to Architectures and Vendor Solutions
Data Foundation
Without data sharing, organizations will not maximize the value of their data and analytics strategies. Data and analytics leaders should be deliberate in their choice of data sharing architectures, and ensure that the selected vendor offerings serve the desired business purpose.
Modern Data and Analytics Requirements Demand a Convergence of Data Management Capabilities
Let's talk data
Data fabric is an important emerging trend that requires a combination of multiple established and emerging data and analytics technologies. Product leaders must ultimately offer complete data fabric solutions via a combination of their own products and strategic partnerships.
Emerging Technologies: Data Fabric Is the Future of Data Management
Let's talk data
Data fabric is an important emerging trend that requires a combination of multiple established and emerging data and analytics technologies. Product leaders must ultimately offer complete data fabric solutions via a combination of their own products and strategic partnerships.
Data Fabrics Add Augmented Intelligence to Modernize Your Data Integration
Let's talk data
Data management teams are under constant pressure to provide faster access to integrated data across increasingly distributed landscapes. Data and analytics leaders must upgrade to a data fabric design that enables dynamic and augmented data integration in support of their data management strategy.
Market Guide for Active Metadata Management
Data Management
Active metadata management is an emerging set of capabilities across multiple data management markets resulting from continuous metadata management innovation. Data and analytics leaders must consider the market evolution as transformational in all data-enabling technologies.
Deploying Effective Metadata Management Solutions
Data Management
Metadata management is a critical component of a holistic data management solution. Data and analytics technical professionals must combine existing metadata management systems with emerging active metadata to enable automation, insight and engagement across the data pipeline.
Emerging Technologies and Trends Impact Radar: Hyperautomation
Let's talk data
Automating manual work is creating significant services, software, network and hardware opportunities. Product leaders must decide which newer hyperautomation-enabling technologies to offer, and when the technologies will be sufficiently mature to support clients.
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Build a Business Case for MDM
Discover how to build a business case for Master Data Management that delivers business outcomes.