Data Modelling Data Security Healthcare 
Astera
APRIL 7, 2023
In the world of medical services, large volumes of healthcare data are generated every day. Currently, around 30% of the world’s data is produced by the healthcare industry and this percentage is expected to reach 35% by 2025. The sheer amount of health-related data presents countless opportunities.
Domo
DECEMBER 9, 2020
That’s the challenge faced by organizations that are already heavily invested in data lakes and warehouses, or are in highly regulated industries—like healthcare or finance—that require their data be kept in their infrastructure at rest for security or compliance reasons. Getting started with federated data in Domo.
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Sisense
APRIL 16, 2019
We support embedding the full Sisense application, including the data modeling, analytics and administration areas, or embedding specific OEM dashboards and widgets using IFrames. Option 1: Shared ElastiCubes with row-based data security. Typically Best for: Tenants with identical data models and dashboard requirements.
Astera
MAY 25, 2023
Big Data Security: Protecting Your Valuable Assets In today’s digital age, we generate an unprecedented amount of data every day through our interactions with various technologies. The sheer volume, velocity, and variety of big data make it difficult to manage and extract meaningful insights from.
Astera
NOVEMBER 30, 2023
With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0
Astera
MAY 13, 2024
Government: Using regional and administrative level demographic data to guide decision-making. Healthcare: Reviewing patient data by medical condition/diagnosis, department, and hospital. Documenting the sensitivity analysis process to gain insights into the aggregated data’s reliability.
Astera
APRIL 24, 2024
Many organizations face challenges with inaccurate, inconsistent, or outdated data affecting insights and decision-making processes. The data governance framework enhances the quality and reliability of the organization’s data. The adoption of data governance practices among individuals within the organization.
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