Data Management Data Modelling Healthcare Monitoring 
Astera
JANUARY 23, 2023
The healthcare industry has evolved tremendously over the past few decades — with technological innovations facilitating its development. Billion by 2026 , showing the crucial role of health data management in the industry. What is Health Data Management ? The global digital health market is expected to reach $456.9
Smart Data Collective
JANUARY 26, 2021
Big Data Analytics News has hailed big data as the future of the translation industry. You might use predictive analysis-based data that can help you analyse buying trends or look at how the business might perform in a range of new markets. Using a Translation Company with Your Big Data Strategy.
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Astera
APRIL 28, 2023
Data management can be a daunting task, requiring significant time and resources to collect, process, and analyze large volumes of information. By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models and creating data visualizations.
Astera
APRIL 23, 2024
Data that meets the requirements set by the organization is considered high-quality—it serves its intended purpose and helps in informed decision-making. Such a detailed dataset is maintained by trained data quality analysts, which is important for better decision-making and patient care.
Astera
FEBRUARY 14, 2024
Data Vault 101: Your Guide to Adaptable and Scalable Data Warehousing As businesses deal with larger and more diverse volumes of data, managing that data has become increasingly difficult. These examples show the high level of flexibility and adaptability provided by data vault.
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.
Astera
JANUARY 23, 2024
Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. Traditional data warehouses with predefined data models and schemas are rigid, making it difficult to adapt to evolving data requirements.
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