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

JUNE 12, 2024

Data Governance Data lineage, data provenance , and data governance are all crucial concepts in data management, but they address different aspects of handling data. Data Provenance captures metadata describing the origin and history of data, including inputs, entities, systems, and processes involved.

Data Governance 52

Data Governance Government Data Quality Monitoring 52

Astera

APRIL 24, 2024

A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.

Data Governance 52

Data Governance Government Data Architecture Data Quality 52

Astera

FEBRUARY 27, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

FEBRUARY 27, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

AUGUST 30, 2024

Data Migrations Made Efficient with ADP Accelerator Astera Data Pipeline Accelerator increases efficiency by 90%. Try our automated, data model-driven solution for fast, seamless, and effortless data migrations. Automate your migration journey with our holistic, data model-driven solution.

Data Governance 52

Data Governance Government Data Quality Documentation 52

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Business Intelligence 102

Business Intelligence Business Intelligence Government Visualization 102

Astera

MARCH 7, 2024

Primary purpose is to ensure that the data being tested is moving as it’s supposed to. Aims to ensure that all data follows the data model’s predefined rules. Checks for duplications in the loaded data. During data movement and transformation. Generally performed on transactional systems.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Modelling 52

Astera

FEBRUARY 28, 2024

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

FEBRUARY 14, 2024

Data vault is an emerging technology that enables transparent, agile, and flexible data architectures, making data-driven organizations always ready for evolving business needs. What is a Data Vault?  A data vault is a data modeling technique that enables you to build data warehouses for enterprise-scale analytics.

Data Warehouse 52

Data Warehouse Data Modelling Agile Data Quality 52

Astera

JULY 3, 2024

It organizes data for efficient querying and supports large-scale analytics. Data warehouse architecture defines the structure and design of a centralized repository for storing and analyzing data from various sources. Each type of data architecture—centralized or distributed—has unique strengths and use cases.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

AUGUST 22, 2023

update is the cutting-edge AI capabilities, enabling data extraction at unprecedented speeds. With just a few clicks, you can effortlessly handle unstructured documents. This new AI feature accelerates and simplifies document processing. Specify the data layout and the fields you want to extract.

Innovation 52

Innovation Documentation Data Analytics Data Analytics 52

Astera

MAY 13, 2024

Besides being relevant, your data must be complete, up-to-date, and accurate. Automated tools can help you streamline data collection and eliminate the errors associated with manual processes. Enhance Data Quality Next, enhance your data’s quality to improve its reliability.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Astera

DECEMBER 29, 2023

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. Free Download What is a Data Warehouse? Data is organized into two types of tables in a dimensional model: fact tables and dimension tables.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

DECEMBER 29, 2023

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. Free Download What is a Data Warehouse? Data is organized into two types of tables in a dimensional model: fact tables and dimension tables.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Business Analysts

APRIL 5, 2023

Data analysis and modelling : AI projects require large amounts of data to train machine learning models. Business analysts can help organizations identify the data sources needed for AI projects, perform data analysis, and develop data models.

Artificial Intelligence 52

Artificial Intelligence Business Analysis Business Analysis Requirements Gathering 52

Astera

MARCH 28, 2024

Additionally, data catalogs include features such as data lineage tracking and governance capabilities to ensure data quality and compliance. On the other hand, a data dictionary typically provides technical metadata and is commonly used as a reference for data modeling and database design.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

Astera

JANUARY 2, 2024

Securing Data: Protecting data from unauthorized access or loss is a critical aspect of data management which involves implementing security measures such as encryption, access controls, and regular audits. Organizations must also establish policies and procedures to ensure data quality and compliance.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Astera

JANUARY 23, 2023

Healthcare Data Management Challenges Consolidating data from disparate sources Healthcare data comes from various sources, including EHRs, EMRs, and unstructured documents. This data must be accurate, complete, formatted correctly, and stored in a centralized data repository for consumption.

Data Management 52

Data Management IBM Maintenance Healthcare IBM cost 52

MindsMapped

JULY 18, 2020

Business Analytics mostly work with data and statistics. They primarily synthesize data and capture insightful information through it by understanding its patterns. Business Analysts and Business Analytics – Differences. Business Analyst. Business Analytics.

Business Analysis 46

Business Analysis Business Analysis Requirements Gathering Documentation 46

Astera

NOVEMBER 30, 2023

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52

Astera

JULY 13, 2023

Unlike a data warehouse, a data lake does not limit the data types that can be stored, making it more flexible, but also more challenging to analyze. One of the key benefits of a data lake is that it can also store unstructured data, such as social media posts, emails, and documents.

Data Warehouse 40

Data Warehouse Data Quality Data Governance Master Data Management 40

Astera

JUNE 3, 2024

These databases are suitable for managing semi-structured or unstructured data. Types of NoSQL databases include document stores such as MongoDB, key-value stores such as Redis, and column-family stores such as Cassandra. These databases are ideal for big data applications, real-time web applications, and distributed systems.

Data Warehouse 52

Data Warehouse Documentation E-Commerce Data Management 52

Astera

MAY 26, 2023

It stores data in dynamic JSON-like documents and supports easy query, manipulation, and storage of data. Its key features include: Automatic sharding: MongoDB’s automatic shading feature allows for horizontal scaling of data across multiple servers.

E-Commerce 40

E-Commerce Documentation Data Quality Data Security 40

Astera

FEBRUARY 14, 2024

Data Mapping: Create a mapping between source and target data fields in Salesforce. Specify how data will be transformed and mapped during the migration process. Ensure alignment with Salesforce data models and consider any necessary data cleansing or enrichment. What about the rows with missing data?

IBM Support 52

IBM Support IBM Maintenance IBM cost Data Quality 52

Astera

JUNE 14, 2023

Transformation: Converting data into a consistent format for easy use. Aligning external and internal data formats. Handling inaccurate and abnormal data. Ensuring data quality and consistency. Loading/Integration: Establishing a robust data storage system to store all the transformed data.

Procurement 40

Procurement Data Security Monitoring Retail 40

Cprime

MAY 4, 2023

Replication can occur in bulk, in batches on a scheduled basis, or in real time across data centers and/or the cloud. Big Data Integration Moving and managing the massive volume, variety, and velocity of big data requires advanced tools and techniques. Developers may use SQL to code a data integration system by hand.

Data Warehouse 52

Data Warehouse Big Data Finance Data Quality 52

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Business Intelligence 52

Business Intelligence Business Intelligence Government Visualization 52

Astera

MAY 30, 2024

Variety : Data comes in all formats – from structured, numeric data in traditional databases to emails, unstructured text documents, videos, audio, financial transactions, and stock ticker data. Veracity: The uncertainty and reliability of data. Veracity addresses the trustworthiness and integrity of the data.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 52

Astera

OCTOBER 27, 2023

Practical Tips To Tackle Data Quality During Cloud Migration The cloud offers a host of benefits that on-prem systems don’t. Here are some tips to ensure data quality when taking your data warehouse to the cloud. The added layer of governance enhances the overall data quality management efforts of an organization.

Data Warehouse 52

Data Warehouse Data Quality Big Data Predictive Analytics 52

Astera

APRIL 24, 2024

Enterprise-Grade Integration Engine : Offers comprehensive tools for integrating diverse data sources and native connectors for easy mapping. Interactive, Automated Data Preparation : Ensures data quality using data health monitors, interactive grids, and robust quality checks.

IBM Support 52

IBM Support IBM cost IBM Maintenance Visualization 52

Astera

AUGUST 28, 2024

Pros Robust integration with other Microsoft applications and services Support for advanced analytics techniques like automated machine learning (AutoML) and predictive modeling Microsoft offers a free version with basic features and scalable pricing options to suit organizational needs. Amongst one of the most expensive data analysis tools.

IBM Support 40

IBM Support IBM Maintenance IBM cost Data Mining 40

Analysts Corner

JANUARY 6, 2025

For example, professions related to the training and maintenance of algorithms, data quality control, cybersecurity, AI explainability and human-machine interaction. Other strategies: Be ready to face new forms of digital threats: protect your assetssoftware, data, models and algorithms from cyberthreats.

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Government Digital Artificial Intelligence Healthcare 130

Insight Software

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. What are the steps of data mapping?

IBM Support 52

IBM Support IBM Maintenance Data Warehouse IBM cost 52

Insight Software

SEPTEMBER 1, 2023

These include data privacy and security concerns, model accuracy and bias challenges, user perception and trust issues, and the dependency on data quality and availability. Data Privacy and Security Concerns: Embedded predictive analytics often require access to sensitive user data for accurate predictions.

Predictive Analytics 52

Predictive Analytics IBM Support IBM Maintenance Embedded Analytics 52