- Data Architecture - Home
- Data Architecture - Introduction
- Data Architecture - Big Data
- Data Architecture - Types of Data Architecture
- Data Architecture - Design Session
- Data Architecture - Relational Data Warehouse
- Data Architecture - Data Lake
- Data Architecture - Data Storage Solutions
- Data Architecture - Data Storage Processes
- Data Architecture - Design Approaches
- Data Architecture - Data Modeling Approaches
- Data Architecture - Data Ingestion Approaches
- Data Architecture - Modern Data Warehouse
- Data Architecture - Data Fabric
- Data Architecture - Data Lakehouse
- Data Architecture - Data Mesh Foundation
Data Architecture - Data Storage Processes
Data process are how organizations handle and use their data effectively. In this chapter , we will look at their importance, challenges, best practices, and specific methods like Master Data Management (MDM), data virtualization, data catalogs, and data marketplaces.
Data Storage Processes in Data Architecture
While storage solutions deal with where and how data is kept, data processes are important for managing and using this data effectively in the system. Now, we will focus on data processes, exploring ways to handle, manage, and make the most of data.
Master Data Management (MDM)
Master Data Management (MDM) is about using tools and processes to keep key data, like customer, product, and supplier information, consistent and accurate. By merging data from different sources into a single record known as a "golden source", organizations can improve their reporting and analysis. MDM tools also clean up data, remove duplicates, and create clear structures for better insights.
Use Case of MDM
For example, in retail chain like ShoesForLess, MDM helps remove duplicate customer records that come from different stores. Without MDM, reports could show incorrect numbers of customers, which makes it hard to trust the data.
Data Visualization anf Federation
Data virtualization, sometimes called a logical data warehouse, allows you to access data from different sources without having to physically move the data to one location. This means you can view and use data from various places as if it were all in one place. It provides a single view of the data, enabling real-time integration and simplifying traditional methods like ETL.
Data federation also provides a single view of data, but it focuses on working together with different organizations. This means that multiple organizations can share and manage their data in a way that allows them to collaborate effectively while still keeping their data separate.
Data Catalogs
A data catalog is a central place that organizes information about an organization's data assets, like tables, schemas, and reports. It acts as a reliable source of information, making it easier to find and manage data. Key features often include tracking where data comes from, governance details, and search tools, which help teams work together and make better decisions while ensuring data quality.
Some popular data catalog products are Informatica's Enterprise Data Catalog and Microsoft Purview.
Data Marketplaces
A data marketplace is an online platform where people can buy, sell, and exchange datasets. It usually includes a catalog that guarantees the quality and usability of the data. These marketplaces often have tools for cleaning and integrating data, making it easier for users to analyze it.
As the demand for insights from data increases, these platforms have become popular. Data providers can earn money from their data, while consumers can access valuable datasets. Popular data marketplaces include the Snowflake Marketplace and Datarade.
Importance of Data Processes
Data processes are important for organizations because they
- Help Make Better Decisions: They provide accurate information to support smart choices.
- Ensure Data Quality: They keep data clean and reliable.
- Increase Efficiency: They save time by cutting down on repetitive tasks.
- Improve Teamwork: They create a shared understanding of data, making it easier to work together.
- Ensure Compliance: They help organizations follow rules and protect data.
- Support Growth: They make it easier to handle more data as the organization grows.
- Encourage Innovation: They help find new ways to improve.
Challenges in Implementing
Organizations face several challenges when implementing data processes, including.
- Managing separate data sources and older systems
- Balancing data rules with the need for flexibility and innovation
- Scaling processes to handle large amounts of data and real-time analysis
- Ensuring data privacy and security in all processes
Best Practices for Data Processes in Architecture
To make data processes work well in your organization, follow these best practices.
- Take a complete view when designing data processes
- Focus on maintaining high data quality at every stage
- Clearly define who is responsible for managing data
- Regularly check and improve data processes
- Offer continuous training and support to your data teams