ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business. Data Volume, Transformation and Location.

Data Management 130

Data Management Data Warehouse Government Data Governance 130

Analysts Corner

AUGUST 13, 2023

As organizations increasingly rely on data to drive their operations, strategy, and innovation, ensuring data integrity and usability has never been more important. This is where data profiling comes into play. It’s akin to conducting a “health check” on your data to assess its quality, integrity, consistency, and usability.

Data Quality 130

Data Quality Data Warehouse Data Discovery Innovation 130

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Agile 130

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Agile 130

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Agile 130

BizAcuity

APRIL 1, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Mining 130

BizAcuity

JANUARY 31, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Mining 130

Astera

APRIL 7, 2023

However, managing reams of data—coming from disparate sources such as electronic and medical health records (EHRs/MHRs), CRMs, insurance claims, and health-tracking apps—and deriving meaningful insights is an overwhelming task. Given the critical nature of medical data, there are several factors to be considered for its management.

Data Warehouse 52

Data Warehouse Healthcare Data Management Data Quality 52

Astera

OCTOBER 27, 2023

What is a Cloud Data Warehouse? Simply put, a cloud data warehouse is a data warehouse that exists in the cloud environment, capable of combining exabytes of data from multiple sources. A cloud data warehouse is critical to make quick, data-driven decisions.

Data Warehouse 52

Data Warehouse Data Quality Big Data Predictive Analytics 52

Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

APRIL 23, 2024

To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements.

Data Quality 52

Data Quality Monitoring Data Modelling Data Warehouse 52

Astera

NOVEMBER 13, 2023

D ata is the lifeblood of informed decision-making, and a modern data warehouse is its beating heart, where insights are born. In this blog, we will discuss everything about a modern data warehouse including why you should invest in one and how you can migrate your traditional infrastructure to a modern data warehouse.

Data Warehouse 52

Data Warehouse Real-time Data Planning Agile 52

Astera

OCTOBER 28, 2024

Now, imagine if you could talk to your data warehouse; ask questions like “Which country performed the best in the last quarter?” Believe it or not, striking a conversation with your data warehouse is no longer a distant dream, thanks to the application of natural language search in data management.

Data Warehouse 52

Data Warehouse Documentation Healthcare Data Management 52

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 megabytes of data every second?

Big Data 130

Big Data Data Analytics Data Analytics Data Management 130

Astera

JANUARY 1, 2024

What Is Data Quality? Data quality is the measure of data health across several dimensions, such as accuracy, completeness, consistency, reliability, etc. In short, the quality of your data directly impacts the effectiveness of your decisions.

Data Quality 52

Data Quality Data Governance Government Data Warehouse 52

Astera

JANUARY 1, 2024

What Is Data Quality? Data quality is the measure of data health across several dimensions, such as accuracy, completeness, consistency, reliability, etc. In short, the quality of your data directly impacts the effectiveness of your decisions.

Data Quality 52

Data Quality Data Governance Government Data Warehouse 52

Business Analysis Knowledge Share

OCTOBER 24, 2023

Data Warehousing is the process of collecting, storing, and managing data from various sources into a central repository. This repository, often referred to as a data warehouse , is specifically designed for query and analysis. Data Sources Data Warehouses collect data from diverse sources within an organization.

IBM Maintenance 75

IBM Maintenance Data Warehouse IBM cost Data Quality 75

Astera

APRIL 3, 2024

What is a data quality framework? A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. It’s not a magic bullet—data quality is an ongoing process, and the framework is what provides it a structure.

Data Quality 52

Data Quality Data Governance Government Monitoring 52

The BAWorld

NOVEMBER 22, 2021

Completeness is a data quality dimension and measures the existence of required data attributes in the source in data analytics terms, checks that the data includes what is expected and nothing is missing. Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Astera

SEPTEMBER 20, 2023

Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks. Enforces data quality standards through transformations and cleansing as part of the integration process.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

SEPTEMBER 20, 2023

Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks. Enforces data quality standards through transformations and cleansing as part of the integration process.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Data Pine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality? Who are they?

Data Visualization 126

Data Visualization Data Analytics Data Analytics Data Quality 126

Data Pine

SEPTEMBER 27, 2022

Here is an excerpt from one: “I use SQL daily, and this was a great reference towards using advanced SQL to get analytics insights. It’s something you should have on your desk for reference at all times and the best book on SQL if you want to step outside the box while fine-tuning your technical skills. Viescas, Douglas J.

IBM cost 117

IBM cost Data Warehouse Business Intelligence Business Intelligence 117

The BAWorld

APRIL 21, 2021

It refers to the methods involved in accessing and manipulating source data and loading it into the target database. This inconsistency in data can be avoided by integrating the data into a data warehouse with good standards. The data restructuring and cleaning are done in the transformation stage.

Data Warehouse 52

Data Warehouse Data Visualization Visualization Banking 52

Astera

JANUARY 25, 2024

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.

E-Commerce 52

E-Commerce Data Warehouse Retail Customer Experience 52

Astera

JANUARY 11, 2024

Understanding the Shift from Siloed Data to Centralized Data Many organizations still operate with siloed financial data, limiting their ability to harness analytics’ power fully. In such cases, data isn’t easily accessible or shared across the organization.

Data Warehouse 52

Data Warehouse Finance Visualization Planning 52

Astera

JUNE 5, 2024

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream.

Real-time Data 52

Real-time Data Data Warehouse Healthcare Monitoring 52

Astera

SEPTEMBER 28, 2023

Data Quality: ETL facilitates data quality management , crucial for maintaining a high level of data integrity, which, in turn, is foundational for successful analytics and data-driven decision-making. Reverse ETL is a relatively new concept in the field of data engineering and analytics.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

FEBRUARY 20, 2023

This data is cleansed and transformed during the process to be usable for reporting and analytics, so healthcare practitioners can make informed, data-driven decisions. This data may include, but is not limited to, a patient’s medical history, EHR records, insurance claims data, demographic data, lab results, and imaging systems.

Healthcare 52

Healthcare Data Warehouse Data Mining Data Quality 52

Astera

MARCH 20, 2024

Enterprise data management (EDM) is a holistic approach to inventorying, handling, and governing your organization’s data across its entire lifecycle to drive decision-making and achieve business goals. It provides a strategic framework to manage enterprise data with the highest standards of data quality , security, and accessibility.

Data Management 52

Data Management Master Data Management Data Quality Government 52

Astera

MARCH 26, 2023

Manual data entry can be time-consuming, resulting in delays in processing claims and increased costs. Siloed Data Data silos refer to the separation of data into isolated and disconnected systems or repositories.

Documentation 52

Documentation Data Quality Data Warehouse Data Management 52

Astera

MARCH 11, 2024

It eliminates the need for complex infrastructure management, resulting in streamlined operations. According to a recent Gartner survey, 85% of enterprises now use cloud-based data warehouses like Snowflake for their analytics needs. What are Snowflake ETL Tools? Snowflake ETL tools are not a specific category of ETL tools.

Data Warehouse 52

Data Warehouse Documentation Monitoring Data Quality 52

Data Pine

OCTOBER 25, 2022

Providing advice on how to foster an analytical culture in your organization so that every team member will find data relevant and actionable, is an excellent resource that describes how to align your BI strategy with your company’s business goals, improving data quality and monitoring its maturity across various factors.

Business Intelligence 117

Business Intelligence Business Intelligence Big Data Data Mining 117

Astera

MARCH 31, 2023

Within the realm of data management, a single source of truth is a concept that refers to a centralized repository containing an organization’s most accurate, complete, and up-to-date data. This data serves as the organization’s master data and is accessible by anyone who needs it. What is a Single Source of Truth?

Data Quality 52

Data Quality Data Security Data Warehouse Monitoring 52

Astera

AUGUST 12, 2024

Financial data integration faces many challenges that hinder its effectiveness and efficiency in detecting and preventing fraud. Challenges of Financial Data Integration Data Quality and Availability Data quality and availability are crucial for financial data integration project, especially detecting fraud.

IBM Support 52

IBM Support Data Quality IBM Maintenance IBM cost 52

Astera

NOVEMBER 30, 2023

With its foundation rooted in scalable hub-and-spoke architecture, Data Vault 1.0 provided a framework for traceable, auditable, and flexible data management in complex business environments. Building upon the strengths of its predecessor, Data Vault 2.0 Business Vault: This component of Data Vault 2.0 Data Vault 2.0

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52