Data Analytics Data Modelling Data Quality Monitoring 
Astera
APRIL 28, 2023
To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights. AI is a powerful tool that goes beyond traditional data analytics.
Astera
JANUARY 25, 2024
Operationalizing insights from stored data and making them actionable in day-to-day business operations. Use Cases Data warehousing, business intelligence, reporting, and data analytics. Data enrichment for CRM, targeted marketing campaigns, real-time customer interaction, and personalized experiences.
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Astera
JANUARY 23, 2023
Evaluate medical practitioners’ performance Data-based performance management involves evaluating the performance and attitudes of doctors and healthcare personnel to ensure standards for quality care are met. Ensuring Data Quality Medical errors are the third leading reason for death in the US. to analyze data.
Marutitech
JANUARY 21, 2022
Data science management has become an essential element for companies that want to gain a competitive advantage. The role of data science management is to put the data analytics process into a strategic context so that companies can harness the power of their data while working on their data science project.
Analysts Corner
JUNE 18, 2023
Python, Java, C#) Familiarity with data modeling and data warehousing concepts Understanding of data quality and data governance principles Experience with big data platforms and technologies (e.g., Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,
Tableau
APRIL 18, 2022
Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Data quality and lineage. Data modeling.
Tableau
APRIL 18, 2022
Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Data quality and lineage. Data modeling.
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