Remove Data Architecture Remove Data Modelling Remove Document
article thumbnail

Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Below we’ll go over how a translation company, and specifically one that provides translations for businesses, can easily align with big data architecture to deliver better business growth. How Does Big Data Architecture Fit with a Translation Company? That’s the data source part of the big data architecture.

article thumbnail

Why Your Business Needs Data Modeling and Business Architecture Integration

Dataversity

In the contemporary business environment, the integration of data modeling and business structure is not only advantageous but crucial. This dynamic pair of documents serves as the foundation for strategic decision-making, providing organizations with a distinct pathway toward success.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

What is Data Architecture? A Look at Importance, Types, & Components

Astera

What is Data Architecture? Data architecture is a structured framework for data assets and outlines how data flows through its IT systems. It provides a foundation for managing data, detailing how it is collected, integrated, transformed, stored, and distributed across various platforms.

article thumbnail

Enterprise Data Management — Driving Large-Scale Change in Your Organization

Sisense

Its main purpose is to establish an enterprise data management strategy. That includes the creation of fundamental documents that define policies, procedures, roles, tasks, and responsibilities throughout the organization. These regulations, ultimately, ensure key business values: data consistency, quality, and trustworthiness.

article thumbnail

None Shall Pass! Are Your Database Standards Too Rigid?

The Data Administration Newsletter

Database standards are common practices and procedures that are documented and […]. Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard.

article thumbnail

Why Your BI and Analytics Platform Should be Cloud-Agnostic

Sisense

As markets consolidate and acquisitions are made, incorporating multiple data architectures shouldn’t necessitate the consolidation of new data sources and data models with a single cloud vendor. Conclusion.

article thumbnail

How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

Low data discoverability: For example, Sales doesn’t know what data Marketing even has available, or vice versa—or the team simply can’t find the data when they need it. . Unclear change management process: There’s little or no formality around what happens when a data source changes. Data modeling.