Remove Business Intelligence Remove Data Governance Remove Data Models Remove Data Quality
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Data architecture strategy for data quality

IBM Journey to AI blog

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. So why are organizations not able to scale governance? Meet Governance Requirements.

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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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Data is Risky Business: Data is a Communications Skill

The Data Administration Newsletter

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression.