Dataconomy

MAY 26, 2025

Big data management refers to the strategies and processes involved in handling extensive volumes of structured and unstructured data to ensure high data quality and accessibility for analytics and business intelligence applications.

Big Data 91

Big Data Big Data Apache Hadoop Data Quality 91

Smart Data Collective

MAY 20, 2019

GDPR helped to spur the demand for prioritized data governance , and frankly, it happened so fast it left many companies scrambling to comply — even still some are fumbling with the idea. Professionals adept at this skill will be desirable by corporations, individuals and government offices alike. The Rise of Regulation.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

IBM Journey to AI blog

JUNE 29, 2023

This may involve consolidating data from enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, supply chain management systems, and other relevant sources. Implementing advanced analytics and business intelligence tools can further enhance data analysis and decision-making capabilities.

Analytics 94

Analytics Analytics Business Intelligence Business Intelligence 94

IBM Journey to AI blog

MARCH 14, 2024

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

AWS 93

AWS Database ETL AI 93

ODSC - Open Data Science

SEPTEMBER 27, 2023

Data Warehouses and Relational Databases It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

JULY 3, 2023

What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

AWS Machine Learning Blog

JUNE 9, 2025

Kip AI demonstrates how we’ve comprehensively integrated AI capabilities with our existing data infrastructure. We use multiple data sources, including Amazon S3 for our storage needs, Amazon QuickSight for our business intelligence requirements, and Google Drive for team collaboration.

AI 79

AI AI AWS Business Intelligence 79

ODSC - Open Data Science

JUNE 12, 2023

Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Pickl AI

OCTOBER 10, 2024

Inconsistent or unstructured data can lead to faulty insights, so transformation helps standardise data, ensuring it aligns with the requirements of Analytics, Machine Learning , or Business Intelligence tools. This makes drawing actionable insights, spotting patterns, and making data-driven decisions easier.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 15, 2023

On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. Business intelligence (BI) platforms. The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

phData

NOVEMBER 9, 2023

The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance. What is a Data Lake?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Dataversity

FEBRUARY 1, 2021

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].

Data Warehouse 59

Data Warehouse Business Intelligence Business Intelligence Data Profiling 59

IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Lakes 73

Data Lakes AI AI Data Governance 73

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

ODSC - Open Data Science

JANUARY 18, 2024

This means that not only do the proper infrastructures need to be created, and maintained, but data engineers will be at the forefront of data governance and access to ensure that no outside actors or black hats gain access which could spell compliance doom for any company.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 11, 2024

To handle sparse data effectively, consider using junk dimensions to group unrelated attributes or creating factless fact tables that capture events without associated measures. Ensuring Data Consistency Maintaining data consistency across multiple fact tables can be challenging, especially when dealing with conformed dimensions.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

IBM Journey to AI blog

JULY 1, 2024

Data lineage is the discipline of understanding how data flows through your organization: where it comes from, where it goes, and what happens to it along the way. Often used in support of regulatory compliance, data governance and technical impact analysis, data lineage answers these questions and more.

ETL 40

ETL Data Lakes Database Data Pipeline 40

IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

Data Quality 45

Data Quality AI AI Database 45

The Data Administration Newsletter

MARCH 20, 2024

Organizations often struggle with finding nuggets of information buried within their data to achieve their business goals. Technology sometimes comes along to offer some interesting solutions that can bridge that gap for teams that practice good data management hygiene.

Data Governance 105

Data Governance ETL Big Data Big Data 105

Pickl AI

DECEMBER 10, 2024

A unified data fabric also enhances data security by enabling centralised governance and compliance management across all platforms. Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes.

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

phData

JULY 11, 2023

Power BI Datamarts provides a low/no code experience directly within Power BI Service that allows developers to ingest data from disparate sources, perform ETL tasks with Power Query, and load data into a fully managed Azure SQL database. Why are Power BI Datamarts Important?

Power BI 40

Power BI SQL Azure ETL 40

phData

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

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. Data Vault Automation Working at scale can be challenging, especially when managing the data model.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data. However, merely knowing what it consists of isn’t enough.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

phData

JULY 10, 2024

Sigma Computing is a cloud-based business intelligence and analytics tool for collaborative data exploration, analysis, and visualization. Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. Choose your desired data source type (e.g.,

SQL 52

SQL Data Warehouse Analytics Analytics 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69