datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

AWS Big Data

DECEMBER 4, 2024

SageMaker still includes all the existing ML and AI capabilities you’ve come to know and love for data wrangling, human-in-the-loop data labeling with Amazon SageMaker Ground Truth , experiments, MLOps, Amazon SageMaker HyperPod managed distributed training, and more. Having confidence in your data is key.

Data Analytics 85

Data Analytics Analytics Data Lake Data Quality 85

AWS Big Data

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 82

Data Quality Data-driven Data Lake Metrics 82

Alation

AUGUST 9, 2022

But in the four years since it came into force, have companies reached their full potential for data integrity? But firstly, we need to look at how we define data integrity. What is data integrity? Many confuse data integrity with data quality. Is integrity a universal truth?

Data Integration 52

Data Integration Data Quality Measurement Data-driven 52

DataKitchen

SEPTEMBER 21, 2023

.’ It’s not just about playing detective to discover where things went wrong; it’s about proactively monitoring your entire data journey to ensure everything goes right with your data. What is Data in Place? There are multiple locations where problems can happen in a data and analytic system.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataKitchen

MAY 10, 2024

The Second of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

Data Quality 130

Data Quality Testing Software Dashboards 130

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

Data Quality 52

Data Quality IT Data Governance Sales 52

DataKitchen

MAY 10, 2021

Make sure the data and the artifacts that you create from data are correct before your customer sees them. It’s not about data quality . In governance, people sometimes perform manual data quality assessments. It’s not only about the data. Data Quality. Location Balance Tests.

Testing 214

Testing Manufacturing Data Quality Statistics 214

O'Reilly on Data

DECEMBER 19, 2018

Anomaly detection is well-known in the financial industry, where it’s frequently used to detect fraudulent transactions, but it can also be used to catch and fix data quality issues automatically. The history of data analysis has been plagued with a cavalier attitude toward data sources.

Machine Learning 279

Machine Learning Software Metadata Testing 279

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

Data Governance 109

Data Governance Unstructured Data Metadata Data Lake 109

Ontotext

JUNE 10, 2021

This is a graph of millions of edges and vertices – in enterprise data management terms it is a giant piece of master/reference data. They should be able to continuously integrate data across multiple internal systems and link it to data from external sources. open-world vs. closed-world assumptions).

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

DataKitchen

DECEMBER 9, 2022

The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes. ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

erwin

JULY 17, 2019

Outsourcing these data management efforts to professional services firms only delays schedules and increases costs. With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders. Digital Transformation Strategy: Smarter Data.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Ontotext

JULY 29, 2021

We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. The two distinct threads interlacing in the current Semantic Web fabrics are the semantically annotated web pages with schema.org (structured data on top of the existing Web) and the Web of Data existing as Linked Open Data.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

BI-Survey

FEBRUARY 17, 2020

It is therefore vital that data is subject to some form of overarching control, which should be guided by a data strategy. This is where data governance comes in. . Data governance refers to the individuals, processes and technology required to manage and protect enterprise data assets.

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

CIO Business Intelligence

MARCH 22, 2022

Set up unified data governance rules and processes. With data integration comes a requirement for centralized, unified data governance and security. Refer to your Step 1 inventory of data resource ownership and accessibility. Ready to evolve your analytics strategy or improve your data quality?

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders. Basic formatting and readability of the data is standardized here.

Optimization 95

Optimization B2B Data Quality Sales 95

AWS Big Data

AUGUST 15, 2024

With Amazon DataZone, individual business units can discover and directly consume these new data assets, gaining insights to a holistic view of the data (360-degree insights) across the organization. The Central IT team manages a unified Redshift data warehouse, handling all data integration, processing, and maintenance.

Data Lake 108

Data Lake Data Warehouse Data Governance Publishing 108

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections. 2 GB into the landing zone daily.

Data Architecture 96

Data Architecture Optimization Data Warehouse Metadata 96

AWS Big Data

DECEMBER 21, 2023

Movement of data across data lakes, data warehouses, and purpose-built stores is achieved by extract, transform, and load (ETL) processes using data integration services such as AWS Glue. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 107

Analytics IT Data Lake Visualization 107

datapine

FEBRUARY 22, 2022

A business intelligence strategy refers to the process of implementing a BI system in your company. IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Clean data in, clean analytics out. Indeed, every year low-quality data is estimated to cost over $9.7

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

Cloudera

APRIL 14, 2021

Refer to the following cloudera blog to understand the full potential of Cloudera Data Engineering. . Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud. References: [link]. Why should technology partners care about CDE?

Data Warehouse 84

Data Warehouse Data Processing Machine Learning Data Quality 84

Octopai

JUNE 19, 2024

By having these elements clearly defined, data producers can ensure they are providing data that meets the needs of the consumers, while consumers can trust the data they receive, knowing it adheres to the agreed-upon standards.

Data Quality 52

Data Quality Data-driven Data Integration Risk 52

Octopai

JUNE 19, 2024

By having these elements clearly defined, data producers can ensure they are providing data that meets the needs of the consumers, while consumers can trust the data they receive, knowing it adheres to the agreed-upon standards.

Data Quality 52

Data Quality Data-driven Data Integration Risk 52

Wayne Yaddow

MARCH 5, 2025

First, we look at how unit and integration tests uncover transformation errors at an early stage. Then, we validate the schema and metadata to ensure structural and type consistency and use golden or reference datasets to compare outputs to a recognized standard. Key Tools & Processes Schema enforcement frameworks (e.g.,

Testing 52

Testing Data Transformation Statistics Metadata 52

Wayne Yaddow

FEBRUARY 11, 2025

While many tools exist for basic data validationssuch as null checks, referential integrity, and common schema compliancemany advanced or domain-specific transformation scenarios remain insufficiently served by commercial and open-source testing solutions. real-time checks, AI-based anomaly detection, and nested JSON validation.

Testing 52

Testing Data Transformation Data Quality Statistics 52

AWS Big Data

JULY 25, 2024

Migrating workloads to AWS Glue AWS Glue is a serverless data integration service that helps analytics users to discover, prepare, move, and integrate data from multiple sources. Apache Airflow brings in new concepts like executors, pools, and SLAs that provide you with superior data orchestration capabilities.

Visualization 104

Visualization Management Data Integration Testing 104

AWS Big Data

JUNE 12, 2023

Acting as a bridge between producer and consumer apps, it enforces the schema, reduces the data footprint in transit, and safeguards against malformed data. AWS Glue is an ideal solution for running stream consumer applications, discovering, extracting, transforming, loading, and integrating data from multiple sources.

Management 86

Management Metadata Internet of Things Testing 86

Cloudera

AUGUST 31, 2021

Leveraged delivery accelerators as well as a Data Quality framework customized by the client. The centralized complete views of verified and data-quality validated source system data within the Data Fabric helped the client streamline both security and data integration efforts across their internal application footprint.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

erwin

AUGUST 31, 2023

Improved Decision Making : Well-modeled data provides insights that drive informed decision-making across various business domains, resulting in enhanced strategic planning. Reduced Data Redundancy : By eliminating data duplication, it optimizes storage and enhances data quality, reducing errors and discrepancies.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Ontotext

JANUARY 10, 2024

And each of these gains requires data integration across business lines and divisions. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

Alation

FEBRUARY 13, 2020

To draw up the ShortList, Constellation Research’s Vice President and Principal Analyst Doug Henschen evaluated more than a dozen of the industry’s best data cataloging solutions, judging companies based on a combination of client inquiries, partner conversations, customer references, vendor selection projects, market share and internal research.

Machine Learning 52

Machine Learning Big Data Data Governance Metadata 52

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Ontotext

JANUARY 26, 2023

These 30 layers can be split into two kinds: a location-reference layer and a topic layer. The authors address the challenge of interoperability in the digitalization of mobility systems and introduce a reference architecture for the Shift2Rail Interoperability Framework (IF). The current graph release (called Vienna ) contains 12.5B

Interactive 52

Interactive Metadata Data Integration Data-driven 52