Data Management Data Quality Healthcare Monitoring 
Astera
APRIL 23, 2024
To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements.
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
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Astera
MAY 8, 2024
All three have a unique purpose in organizing, defining, and accessing data assets within an organization. For instance, in a healthcare institution, “Patient Admission” might be “the process of formally registering a patient for treatment or care within the facility.”
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. Continuous Data Quality Monitoring According to Gartner , poor data quality cost enterprises an average of $15 million per year.
Astera
NOVEMBER 27, 2023
Clean and accurate data is the foundation of an organization’s decision-making processes. However, studies reveal that only 3% of the data in an organization meets basic data quality standards, making it necessary to prepare data effectively before analysis. This is where data profiling comes into play.
Astera
OCTOBER 26, 2023
The more data we generate, the more cleaning we must do. But what makes cleaning data so essential? Gartner reveals that poor data quality costs businesses $12.9 Data cleansing is critical for any organization that relies on accurate data. Interactive Data Profiling: Gain insights into your data visually.
Astera
MARCH 31, 2023
In today’s data-driven world, businesses rapidly generate massive amounts of data. Managing this data effectively and timely is critical for decision-making, but how can they make sense of all this data most efficiently? 2. Ensuring data quality Another major challenge is improving data quality.
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