Smart Data Collective

SEPTEMBER 29, 2022

In the last step, the extracted data is structured so that it can be used for further processing. Each data point is linked to its reference. The post Data-Driven Companies Leverage OCR for Optimal Data Quality appeared first on SmartData Collective. You can now save it in your database.

Data-driven 145

Data-driven Data Quality Optimization Insurance 145

DataKitchen

JULY 12, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. Do you know as a data engineer? For example, you can compare current data to previous or expected values. What is a meaningful test for your business?

Data Quality 130

Data Quality Testing Manufacturing Finance 130

AWS Big Data

MAY 23, 2024

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules assess the data based on fixed criteria reflecting current business states. We are excited to talk about how to use dynamic rules , a new capability of AWS Glue Data Quality.

Data Quality 101

Data Quality Metrics Data Lake Sales 101

AWS Big Data

JUNE 6, 2023

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

Data Quality 79

Data Quality Metrics Visualization Dashboards 79

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

Data Quality 97

Data Quality Statistics Data Lake Visualization 97

AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

Data Quality 104

Data Quality Measurement Testing Visualization 104

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

AWS Big Data

OCTOBER 9, 2024

Some customers build custom in-house data parity frameworks to validate data during migration. Others use open source data quality products for data parity use cases. This takes away important person hours from the actual migration effort into building and maintaining a data parity framework.

Data Quality 96

Data Quality Data Lake Data Warehouse Metrics 96

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

JUNE 6, 2023

Alerts and notifications play a crucial role in maintaining data quality because they facilitate prompt and efficient responses to any data quality issues that may arise within a dataset. This proactive approach helps mitigate the risk of making decisions based on inaccurate information.

Data Quality 82

Data Quality Metrics Data-driven Visualization 82

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 104

Data Quality Data Governance Data Lake Testing 104

Jim Harris

JANUARY 1, 2018

So says the folk tale that became an allegory for people accused of being unreasonably afraid, or people trying to incite an unreasonable fear in those around them, sometimes referred to as Chicken Little Syndrome. The Chicken Littles of Data Quality use sound bites like “data quality problems cost businesses more than $600 billion a year!”

Data Quality 77

Data Quality Cost-Benefit Consulting Dashboards 77

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

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 86

Data Analytics Analytics Data Lake Data Quality 86

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

Data Quality 76

Data Quality Data Lake Visualization Data-driven 76

Ontotext

MAY 26, 2021

Without all this background knowledge, before computers can perform like humans, they need a machine-readable point of reference that represents “the ground truth”. One of the main uses of the Gold Standard is to train AI systems to identify the patterns in various types of data with the help of machine learning (ML) algorithms.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 83

Data Quality Data-driven Data Lake Metrics 83

AWS Big Data

DECEMBER 4, 2024

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. For more details, refer to Iceberg Release 1.6.1. Apache Iceberg highlights AWS Glue 5.0

Snapshot 84

Snapshot Metadata Data Lake Optimization 84

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

CIO Business Intelligence

JANUARY 9, 2025

In recognising these challenges, Akeneo has developed the Akeneo Product Cloud, a comprehensive solution that delivers Product Information Management (PIM), Syndication, and Supplier Data Manager capabilities. The platform offers tailored solutions for different market segments.

B2B 105

B2B Data-driven Cost-Benefit Sales 105

Ontotext

NOVEMBER 16, 2023

In the first part of this series of technological posts, we talked about what SHACL is and how you can set up validation for your data. Tacking the data quality issue — bit by bit or incrementally There are two main approaches to validating your data, which would be dependent on the specific implementation.

Data Quality 69

Data Quality Reporting Testing Technology 69

DataKitchen

FEBRUARY 18, 2022

This paper will focus on providing a prescriptive approach in implementing a data pipeline using a DataOps discipline for data practitioners. Data is unique in many respects, such as data quality, which is key in a data monetization strategy. Data governance is necessary in the enforcement of Data Privacy.

Machine Learning 130

Machine Learning Data Quality Business Intelligence Data Governance 130

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

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

TDAN

AUGUST 16, 2022

Data Acumen, Literacy, and Culture Data literacy, or data acumen[1] as we like to call it, is increasingly cited as a critical enabler of being a data-driven organization. We set out to do something about that and developed a data acumen quick reference. Using the quick reference, folks […].

Data-driven 52

Data-driven IT Data Quality Data Governance 52

datapine

MAY 28, 2019

It is of utmost importance to create a compact BI project plan that you can refer to periodically and track your progress. Maximum security and data privacy. To get started in this journey, here are the top 5 tips to successfully create a BI project. Create a solid BI project plan. Reducing the reporting time.

Business Intelligence 317

Business Intelligence KPI Dashboards Reporting 317

CIO Business Intelligence

JANUARY 1, 2025

Referring to the latest figures from the National Institute of Statistics, Abril highlights thatin the last five years, technological investment within the sector has grown more than 40%.

Strategy 115

Strategy Digital Transformation Optimization Technology 115

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

datapine

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?

IT 317

IT Statistics KPI Data-driven 317

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

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

CIO Business Intelligence

DECEMBER 11, 2024

To succeed in todays landscape, every company small, mid-sized or large must embrace a data-centric mindset. This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. Implementing ML capabilities can help find the right thresholds.

Management 111

Management Data Governance Data Science Reporting 111

Domino Data Lab

MAY 31, 2022

What is Data Governance? Data governance refers to the process of managing enterprise data with the aim of making data more accessible, reliable, usable, secure, and compliant across an organization.

Data Governance 98

Data Governance Data Quality Enterprise Management 98

erwin

MARCH 30, 2020

As a result, the data may be compromised, rendering faulty analyses and insights. To marry the epidemiological data to the population data it will require a tremendous amount of data intelligence about the: Source of the data; Currency of the data; Quality of the data; and.

Metadata 122

Metadata IT Data Governance Data Quality 122

AWS Big Data

FEBRUARY 13, 2025

This plane drives users to engage in data-driven conversations with knowledge and insights shared across the organization. Through the product experience plane, data product owners can use automated workflows to capture data lineage and data quality metrics and oversee access controls.

Data Analytics 72

Data Analytics Analytics Modeling Data-driven 72

AWS Big Data

APRIL 8, 2025

Concurrent UPDATE/DELETE on overlapping partitions When multiple processes attempt to modify the same partition simultaneously, data conflicts can arise. For example, imagine a data quality process updating customer records with corrected addresses while another process is deleting outdated customer records.

Snapshot 117

Snapshot Management Metadata Big Data 117

CIO Business Intelligence

SEPTEMBER 12, 2024

Assess and address data quality Once your data is centralized and cataloged, assessing and addressing data quality standards is crucial. That’s because AI model output is only as accurate as the data inputs. Any discrepancies or errors are flagged for manual review and resolution.

Unstructured Data 119

Unstructured Data Data Quality Structured Data Machine Learning 119