4 Steps to Create a Unified, Comprehensive, and Complete Database That Will Serve as the Foundation for Business Operations
Complete, reliable, relevant, and synchronized data is critical for running a successful business. Enterprise Information Management (EIM) systems enable companies and organizations to manage their data across a wide range of departments, professional units, internal and external users, core systems, and business processes. These systems manage structured data, content, and unstructured, distributed information, while helping share it with employees, customers, suppliers, and business partners.
Here are some recommended best practices for building a unified, comprehensive, and complete database using a **Data Management** platform integrated with **Machine Learning (ML)** in your organization:
1. Define a Business Taxonomy
It’s essential to create a foundation for content cataloging that will establish relationships between data elements and improve the speed of finding, accessing, or reusing data—enhancing data management efficiency and collaboration among stakeholders. Machine learning models can help define information categories and build your organization’s taxonomy.
- Focus on a high-value data domain: Start with a specific business area that will deliver the greatest impact.
- Leverage industry standards: Use your industry’s accepted terminology and standards to ensure alignment.
- Demonstrate value to drive engagement: Show the organization the benefits of a single, accurate source of truth.
- Set milestones and commit to them: Establish clear milestones your organization will commit to in order to implement business categories, terms, data classification processes, and proper user role allocation.
2. Know Your Data: Empower Management Teams to Meet Evolving Demands
When setting up a data management system, you need to provide data governance tools and define permissions to comply with regulations, enforce policies and standards, and incorporate metadata management guidelines for data security. Data management must be complete, actionable, and accessible anywhere.
How can a Data Management platform address this?
- Business Glossary: Define common business terms and ensure consistent use across the organization to achieve shared understanding. A Data Management platform enables you to create and manage a unified business glossary as a foundation for a shared organizational language.
- Policy Management: Enable privacy and define data policies that describe how all data—including sensitive and personal information—should be handled, using data protection, data quality, and automation rules.
- Reference Data Management: Establish centralized management of reference data and define standards for common values used across various applications.
- Classification: Communicate the importance of a single, complete data repository. Classifications can describe business terms, data groups, reference data sets, and governance rules.
- Data Lifecycle: Track the lifecycle of organizational data, identify data sources, and monitor data usage to foster greater trust and transparency across the organization.
3. Trust Your Data: Assess Data Quality Across the Organization
Data must be secure, “clean,” and easily discoverable to promote independent access. Users need to understand where data comes from and its quality.
How can a Data Management platform address this?
- Data Discovery: Automatically discover, import, analyze, and classify new data from various sources to facilitate centralized search, management, and use.
- Business Term Suggestions: Automatically associate business terms with data elements while continuously training machine learning models for increasingly accurate metadata updates.
- Data Quality Issue Detection: Leverage automated validation to measure and monitor data quality over time.
4. Use Your Data: Enable Data Consumption and Sharing Across the Organization
It’s crucial to deliver processed, business-ready data to organizational data consumers so they can make better decisions and improve productivity.
How can a Data Management platform address this?
- Policy Enforcement & Data Security: Automatically enforce policies, ensure data security, and mask sensitive data according to permissions—allowing data sharing without risk of unwanted exposure.
- Data Exploration: Reporting and exploration tools enable organizations to discover, clean, and transform data through structured actions.
- Self-Service: Empower users to independently discover data and information by making all relevant data visible and accessible to them.
- Information Sharing: Workflow management tools, built-in interfaces, and data import/export processes allow sharing of information with various consumers tailored to their needs.
SignatureIT Data Management Platform is an innovative, cutting-edge solution for driving digital transformation, helping organizations gain a 360-degree view of their data, manage workflows for organizing information, search and retrieve data from multiple sources, collaborate, and build rich applications and user interfaces.
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