Dataconomy

SEPTEMBER 4, 2023

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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Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses.

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ETL Data Quality Data Warehouse Database 105

Data Science Blog

AUGUST 22, 2024

This shift not only saves time but also ensures a higher standard of data quality. Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed. Foster a Data-Driven Culture Promote a culture where data quality is a shared responsibility.

Data Preparation 130

Data Preparation Data Quality AI AI 130

phData

SEPTEMBER 19, 2023

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.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Tableau

JANUARY 27, 2021

This week, Gartner published the 2021 Magic Quadrant for Analytics and Business Intelligence Platforms. I first want to thank you, the Tableau Community, for your continued support and your commitment to data, to Tableau, and to each other. Francois Ajenstat. Kristin Adderson. January 27, 2021 - 4:36pm. February 18, 2021.

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Tableau Business Intelligence Business Intelligence Analytics 52

The Data Administration Newsletter

DECEMBER 20, 2022

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.

Data Governance 52

Data Governance Data Quality Data Modeling Data Models 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

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.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Dataversity

FEBRUARY 19, 2021

Enterprises are modernizing their data platforms and associated tool-sets to serve the fast needs of data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service-embracing business and technology personnel.

Data Analyst 59

Data Analyst Data Scientist Business Intelligence Business Intelligence 59

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Pickl AI

AUGUST 9, 2024

Real-world examples illustrate their application, while tools and technologies facilitate effective hierarchical data management in various industries. One of the key components of dimensional modelling is the concept of hierarchies. Support for Business Processes Many business processes are inherently hierarchical (e.g.,

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

Data Quality 52

Data Quality Data Warehouse Data Profiling Data Governance 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming. In Summary.

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

The Data Administration Newsletter

JANUARY 17, 2024

Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations.

AI 122

AI AI Data Quality Data Governance 122

Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

The Data Administration Newsletter

JUNE 21, 2022

Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].

Data Quality 78

Data Quality Data Governance Data Modeling Data Models 78

Pickl AI

JANUARY 16, 2024

Data Analyst career path Level Requirements Roles Responsibilities Skills Development Entry-Level Positions (Starting the Journey) Foundational skills in Data Analysis tools Data Analyst Translate raw data into meaningful insights Training in Data Analysis tools Proficiency in programming languages (e.g.,

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Data Analyst Data Analysis Data Analysis Analytics 52

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? They don’t fit into tables with attributes where you see an organized structure.

AI 52

AI AI Data Lakes Database 52

Data Science Blog

JULY 23, 2023

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. It advocates decentralizing data ownership to domain-oriented teams. Each applications has its own data model.

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phData

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. It should also enable easy sharing of insights across the organization. Read more here.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

phData

NOVEMBER 13, 2023

Efficient Data Retrieval: Quick access to metric datasets from your data platform is made possible by MetricFlow’s optimized processes. Seamless Integration with Downstream Tools: The setup process is tailored to enable consistent metric access across a variety of analytics and business intelligence tools.

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Database Business Intelligence Business Intelligence Data Silos 52