What is Data Management?

Data management enables consistent accessibility, delivery, governance, and security of data to meet an organization's requirements using tools including master data management, data virtualization, data catalog, and self-service data preparation and wrangling.

While most businesses today have put together a documented data strategy, a majority of those businesses have yet to become truly data-driven. Most still don't treat data as a business asset to help them successfully compete in the marketplace. As a result, there's a massive opportunity for organizations that recognize the importance of creating a holistic data infrastructure. By implementing a combination of data virtualization, master data management (MDM), metadata management, and other essential data management technologies, businesses can better meet business objectives and place data at the center of their business.

When it comes to managing your organization's data efficiently, a unified and holistic approach is crucial in order to establish a strong data infrastructure. But what does that mean, exactly? To begin with, your data management program implementation should have the following characteristics:

• Established data governance controls that provide security by limiting access to data to only authorized users, making it easy to identify the data you're looking for with clear metadata.
• Easily accessible data, including streaming, transactional, structured and unstructured data.
• An infrastructure that can evolve as business needs change.
• The ability to work with existing and legacy technologies without having to go through the expensive task of "ripping and replacing."
• Consistent and controlled data sharing across business domains, allowing for data use in operations, analytics, and governance.

Finally, it should have data quality that measures up in these six key areas:

• Validity: The data conforms to the syntax (range, format, type) of its definition.
• Consistency: When comparing two or more representations of an object or event, there are no differences.
• Uniqueness: No copied data records.
• Accuracy: The data is able to correctly describe the "real-world" object or event in question.
• Completeness: All relevant data is included.
• Timeliness: The data is up to date and represents reality from a very recent point in time.

• Data Quality: Data is considered high quality when it accurately represents real-world constructs and fits the purpose for which it was intended.
• Data Virtualization: Data virtualization allows you to break down data silos and create one unified place to access, understand, and consume all your business's data, either on-premise or in the cloud.
• Data Governance: End-to-end support for your data governance program allows you to balance data demands while still adhering to regulations and internal controls.
• Master Data Management: MDM creates a single place for all the aspects of your enterprise, including customers, assets, locations, suppliers, products, accounts, reference data, and more. Master Data Management is essential to keeping your data accurate and consistent for operational, analytical, and governance processes.
• Metadata Management: With metadata management, you can harvest and manage all of your data dictionaries. You can also document rules, policies, and business glossaries, as well as grant access to critical data assets for easy searching and collaborating.
• Data Catalog: Data scientists, analysts, and other consumers want access to all of your data assets to uncover insights. A data catalog solution makes it easy for your analytics teams to find the assets they need while adhering to the governance required by your enterprise.

• Data-as-a-Service (DaaS): Data-as-a-service (DaaS) provides your business with the flexibility to address the data service needs of your internal and external customers.
• Virtual data layer: A virtual data layer allows you to access, combine, and provision all the data your enterprise needs. Deploying a virtual data layer solves the issues of delivering and securing data across siloed repositories.
• Logical data warehouse: This architecture can evolve to support your business's evolving data and analytics needs. Unlike fit-for-purpose data management approaches, a logical data warehouse can meet changing requirements without creating data silos.
• Multi-domain MDM: With multi-domain master data management, you can manage, model, and govern your master data domains all across your organization. Having consistent and accurate master data can streamline your processes and increase your analytics and reporting quality.
• Anything 360: Whether you're trying to provide an excellent customer experience, optimize your supply chain, or accelerate a new product innovation, you need the understanding provided by a unified 360° view of all your customer information. Getting a 360° view of any entity in your business that aligns with your master, reference, streaming, and transactional data is critical for digital business success.
• Reference data management: Reference data is a subset of master data used to classify postal codes, cost centers, or financial hierarchies. Reference data management allows you to manage classifications and hierarchies across your systems and business lines.

A data fabric is a modern distributed data architecture that includes shared data assets and optimized data fabric pipelines that you can use to address today's data challenges in a unified way.

A data fabric supports:

• Data for All Users and Use Cases: Provides timely, consistent, and trusted data for your wide range of analytic, operational, transactional, and governance use cases, as well as business self-service users.
• Data from Any and All Sources: Accesses, combines, and transforms both in-motion and at-rest data from across your diverse, distributed data landscape using metadata, models, and pipelines.
• Data that Spans Any Environment: Flexibly spans your distributed on-premises, hybrid, and multi-cloud environments.