Data democratization refers to a method by which data-driven organizations can obtain the maximum value from their information resources. With the ever-increasing focus on the importance of enterprise data, democratization offers an appealing path for using data resources productively.
Defining Data Democratization
Data democratization describes the ongoing process of allowing everyone in an organization to comfortably – and securely – work with enterprise data assets.
Key Principles of Data Democratization
Successfully democratizing data requires organizations to adopt three main principles:
Empower and encourage employees to ask data-related questions.
Furnish the right tools to allow everyone to comfortably work with data.
View democratization as an ongoing process that could require an organizational culture shift.
Benefits of Data Democratization
The benefits of data democratization ripple through an organization and affect all of its departments and teams. Because of this, making data resources more readily available to every employee – in a manner compliant with data regulations – benefits the entire enterprise.
Key Advantages
Through the democratization of data, organizations can:
Empower employees throughout the enterprise to use data resources productively to improve their performance.
Improve remote work capabilities by making relevant data available regardless of location.
Increase operational efficiency as a result of employees being able to easily access key information.
Make faster decisions by having reliable data available to assist in making the correct choices.
Enhance the customer experience by giving client-facing teams access to contact information and other relevant data.
How a Data Fabric Helps Organizations Democratize Data
A data fabric can be seen as the vehicle that enables an organization to efficiently democratize its data resources within a governance framework. Ideally, it is an all-encompassing analytics architecture that increases data availability while facilitating and securely managing its flow throughout the enterprise.
Components of a Data Fabric
As with most vehicles, it’s made up of multiple components working together to achieve the objective.
Anatomy of a Data Fabric
The architectural components that comprise a data fabric are:
データ・ストアフロント: A platform for users to browse for information and define requirements.
データカタログ: Connecting a ビジネス用語集 with policies, rules, available data sets, and possible source data.
Operational Systems: Systems used to run the business’s daily operations.
Real-time Analysis Platform: Analyzes data streams as they arrive.
Additional Data Sources: Non-standard internal and external structured and multi-structured data sources.
Data Integration Platform: Extracts and transforms data into a standard format for loading into an enterprise data warehouse (EDW).
Data Refinery: Distills data into useful formats for analytics.
Enterprise Data Warehouse (EDW): Serves as the production analytics environment using trusted, reliable data.
Investigative Computing Platform (ICP): A data lake used for データモデリング, exploration, and analysis.
Analytics Tools and Applications: A set of tools to analyze data and create reports.
Technical Requirements for a Data Fabric Architecture
Implementing the complicated architecture of a data fabric requires multiple technical solutions. The technical requirements that must be successfully negotiated include:
Creation of a data catalog, business glossary, and data dictionary
Data modeling used in the design of the EDW (Enterprise Data Warehouse) and ICP (Integrated Content Platform)
Data integration to standardize data for use in the EDW
Data preparation in the creation of the ICP
Data virtualization to make information more easily accessible
Real-time analytics engine
Data analysis and visualization tools
Monitoring what data is being used and who is using it
Appropriate data storage solutions
IDERA Data Fabric Solutions for Data Democratization
Enabling Data Democratization
Democratizing your data provides the means to enable all employees to get the most value from enterprise information resources. IDERA offers multiple solutions that address the specific steps involved in the creation of an enterprise data fabric.
ER/Studio
ER/Studio is a suite of data modeling tools that provide the necessary functionality to accurately model both information and data and create an enterprise-wide data catalog. It enables organizations to undertake a collaborative data modeling approach. The physical and logical models derived from source data are essential when designing the EDW (Enterprise Data Warehouse) and ICP (Information Control Platform).
Qubole is a versatile tool that can help teams perform the data preparation and monitoring necessary for the creation of a data fabric. It also serves as a platform for the real-time analytics that make data fabrics valuable for decision-making.
Key Features of Qubole
Supports data preparation and monitoring
Platform for real-time analytics
Enhances data fabric decision-making capabilities
Try Qubole for free!
Aqua Data Studio
Aqua Data Studio is a versatile tool for data analysis and visualization with support for over 40 different database platforms. It provides a flexible means of extracting additional value from data resources by creating visualizations that make complex information accessible to any audience.
Key Features of Aqua Data Studio
Data analysis and visualization
Supports 40+ database platforms
Makes complex data more accessible through visualizations
Much more information on creating a data fabric can be found in this IDERA whitepaper. It provides an in-depth look into developing a data fabric that can be used to improve data democratization in any organization.
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