David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

To improve data reliability, enterprises were largely dependent on data-quality tools that required manual effort by data engineers, data architects, data scientists and data analysts.  With the aim of rectifying that situation, Bigeye’s founders set out to build a business around data observability.

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David Menninger's Analyst Perspectives

JUNE 12, 2019

Under that focus, Informatica's conference emphasized capabilities across six areas (all strong areas for Informatica): data integration, data management, data quality & governance, Master Data Management (MDM), data cataloging, and data security.

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DataKitchen

NOVEMBER 18, 2022

Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement.

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CIO Business Intelligence

MARCH 19, 2025

Dependency mapping can uncover where companies are generating incorrect, incomplete, or unnecessary data that only detract from sound decision-making. It can also be helpful to conduct a root cause analysis to identify why data quality may be slipping in certain areas.

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Data Integration Data Quality Visualization Risk 52

CIO Business Intelligence

MAY 8, 2024

In the age of big data, where information is generated at an unprecedented rate, the ability to integrate and manage diverse data sources has become a critical business imperative. Traditional data integration methods are often cumbersome, time-consuming, and unable to keep up with the rapidly evolving data landscape.

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Data Integration IoT Cost-Benefit Machine Learning 102

AWS Big Data

AUGUST 8, 2024

Thousands of organizations build data integration pipelines to extract and transform data. They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. After a few months, daily sales surpassed 2 million dollars, rendering the threshold obsolete.

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Data Quality Statistics Visualization Metrics 116

AWS Big Data

MAY 23, 2024

Hundreds of thousands of organizations build data integration pipelines to extract and transform data. They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. We also show how to take action based on the data quality results.

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Data Quality Metrics Data Lake Sales 117

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.

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Data Quality Data Integration Metadata Cost-Benefit 63

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning. Data unification and integration.

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Machine Learning Data Quality Statistics Modeling 279

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. What is data integrity?

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Data Integration Testing Data Quality Data-driven 101

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.

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Data Quality Statistics Data Lake Visualization 98

Smart Data Collective

APRIL 30, 2021

However, your data integrity practices are just as vital. But what exactly is data integrity? How can data integrity be damaged? And why does data integrity matter? What is data integrity? Indeed, without data integrity, decision-making can be as good as guesswork.

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Data Integration Strategy Software Management 122

AWS Big Data

DECEMBER 4, 2024

Data teams struggle to find a unified approach that enables effortless discovery, understanding, and assurance of data quality and security across various sources. Having confidence in your data is key. Automate data profiling and data quality recommendations, monitor data quality rules, and receive alerts.

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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.

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Data Quality Data-driven Data Lake Metrics 96

DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

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Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Data Quality Data Governance Metrics Statistics 94

CIO Business Intelligence

OCTOBER 29, 2024

Good data provenance helps identify the source of potential contamination and understand how data has been modified over time. This is an important element in regulatory compliance and data quality. AI-native solutions have been developed that can track the provenance of data and the identities of those working with it.

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Octopai

AUGUST 3, 2022

It’s also a critical trait for the data assets of your dreams. What is data with integrity? Data integrity is the extent to which you can rely on a given set of data for use in decision-making. Where can data integrity fall short? Too much or too little access to data systems.

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Data Integration Manufacturing Data Quality Data Governance 98

DataKitchen

JANUARY 25, 2022

Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . For example, DataOps can be used to automate data integration.

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CIO Business Intelligence

FEBRUARY 26, 2025

The problem is that, before AI agents can be integrated into a companys infrastructure, that infrastructure must be brought up to modern standards. In addition, because they require access to multiple data sources, there are data integration hurdles and added complexities of ensuring security and compliance.

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IT Enterprise Interactive Data Quality 141

Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

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Jim Harris

DECEMBER 24, 2015

Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you? These discussions are a critical prerequisite for determining data usage, standards, and the business relevant metrics for measuring and improving data quality.

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Data Quality Enterprise Business Intelligence Data Governance 56

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?

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DataKitchen

NOVEMBER 10, 2023

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

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Data Quality Testing Data Lake Data Integration 136

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

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Testing Data Quality Predictive Modeling Metrics 169

Wayne Yaddow

MARCH 3, 2025

When implementing automated validation, AI-driven regression testing, real-time canary pipelines, synthetic data generation, freshness enforcement, KPI tracking, and CI/CD automation, organizations can shift from reactive data observability to proactive data quality assurance.

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Alation

MAY 24, 2022

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

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Data Quality Metadata Reporting Metrics 52

IBM Big Data Hub

JUNE 12, 2023

Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. However, analytics are only as good as the quality of the data, which must be error-free, trustworthy and transparent. What is data quality? million each year.

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Data Quality Data Governance People Analytics Data-driven 63

DataKitchen

DECEMBER 29, 2022

Data contracts are a new idea for data and analytic team development to ensure that data is transmitted accurately and consistently between different systems or teams. One of the primary benefits of using data contracts is that they help to ensure data integrity and compatibility.

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Testing Statistics Data Quality Data Integration 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.

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Data Quality IT Data Governance Sales 52

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.

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IBM Big Data Hub

NOVEMBER 4, 2022

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. and its leading data observability offerings.

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Data Quality Metadata Data Governance Data-driven 56

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.

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Testing Manufacturing Data Quality Statistics 214

DataKitchen

AUGUST 16, 2024

At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data. Data Pipeline Observability: Optimizes pipelines by monitoring data quality, detecting issues, tracing data lineage, and identifying anomalies using live and historical metadata.

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CIO Business Intelligence

NOVEMBER 13, 2024

By automating data profiling and validation, it minimizes errors and maintains data integrity throughout the migration. Advanced algorithms and generative AI systematically check data for accuracy and completeness, catching inconsistencies that might otherwise slip through the cracks.

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Ontotext

NOVEMBER 23, 2023

The next step is to link the data graph to the shapes graph: ex:TolkienDragonShape sh:shapesGraph ex:TolkienShapesGraph. This technique can be especially useful in data integration projects where you are combining related, potentially overlapping data from multiple sources. Ontotext’s GraphDB Give it a try today!

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