Remove Business Intelligence Remove Data Governance Remove Data Modelling Remove Data Warehouse
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The Complete Guide to Reverse ETL

Astera

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

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Information Marts: Enabling Agile, Scalable, and Accurate BI

Astera

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. What are Information Marts?

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. The program manager should lead the vision for quality data and ROI. 2 – Data profiling.

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All You Need to Know About Data Aggregation

Astera

It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Enhance Data Quality Next, enhance your data’s quality to improve its reliability.

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What Is Data Management and Why Is It Important?

Astera

Businesses, both large and small, find themselves navigating a sea of information, often using unhealthy data for business intelligence (BI) and analytics. Relying on this data to power business decisions is like setting sail without a map. This is why organizations have effective data management in place.

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Unifying Data from Multiple Sources: Data Integration and Data Consolidation in Data Preparation 

Astera

This process includes moving data from its original locations, transforming and cleaning it as needed, and storing it in a central repository. Data integration can be challenging because data can come from a variety of sources, such as different databases, spreadsheets, and data warehouses.

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Business Analysis vs Business Analytics

MindsMapped

Business Analytics. Business Analytics mostly work with data and statistics. They primarily synthesize data and capture insightful information through it by understanding its patterns. Business Analytics specialists sometimes also switch to data scientist job profiles.