CIO Business Intelligence

NOVEMBER 25, 2024

Organizations must prioritize strong data foundations to ensure that their AI systems are producing trustworthy, actionable insights. In Session 2 of our Analytics AI-ssentials webinar series , Zeba Hasan, Customer Engineer at Google Cloud, shared valuable insights on why data quality is key to unlocking the full potential of AI.

Data Quality

TDAN

NOVEMBER 20, 2024

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Key Examples of Data Quality Failures — […]

Data Quality

DataFloq

JULY 8, 2025

As organizations race to adopt generative AI tools-from AI writing assistants to autonomous coding platforms-one often-overlooked variable makes the difference between game-changing innovation and disastrous missteps: data quality. While often viewed as a backend or IT concern, data quality is now a strategic priority.

Data Quality

DataKitchen

FEBRUARY 20, 2025

Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. You don’t have to imagine — start using it today: [link] Introducing Data Quality Scoring in Open Source DataOps Data Quality TestGen 3.0! DataOps just got more intelligent.

Data Quality

DataKitchen

JULY 17, 2025

You’ll see how a structured approach to data test coverage can catch issues before stakeholders do. The post Webinar: Test Coverage: The Software Development Idea That Supercharges Data Quality & Data Engineering first appeared on DataKitchen.

Data Quality

DataKitchen

JUNE 18, 2025

In this exciting webinar , Christopher Bergh discussed various types of data quality dashboards, emphasizing that effective dashboards make data health visible and drive targeted improvements by relying on concrete, actionable tests. He stressed the importance of measuring quality to demonstrate value and extend influence.

Data Quality

AWS Big Data

JULY 28, 2025

High-quality data is essential for building trust in analytics, enhancing the performance of machine learning (ML) models, and supporting strategic business initiatives. By using AWS Glue Data Quality , you can measure and monitor the quality of your data. Under Data Catalog , choose Databases.

Data Quality

DataFloq

JULY 20, 2025

As AI has gained prominence, all the data quality issues we’ve faced historically are still relevant. However, there are additional complexities faced when dealing with the nontraditional data that AI often makes use of. When using AI models with this type of data, quality is as important as ever.

Data Quality

DataKitchen

JULY 22, 2025

Tested – Automated Tests Everywhere, Hope is Not a Strategy We treat tests as first-class citizens in our data architecture, not afterthoughts or nice-to-haves. Every pipeline includes comprehensive data quality tests covering schema validation, null checks, and range verification. You need to have tests, lots of them.

Data Architecture

AWS Big Data

DECEMBER 17, 2024

Prevent the inclusion of invalid values in categorical data and process data without any data loss. Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times.

Data Quality

CIO Business Intelligence

DECEMBER 12, 2024

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. Data quality is a problem that is going to limit the usefulness of AI technologies for the foreseeable future, Brown adds.

IT

DataKitchen

DECEMBER 9, 2024

data engineers delivered over 100 lines of code and 1.5 data quality tests every day to support a cast of analysts and customers. The team used DataKitchen’s DataOps Automation Software, which provided one place to collaborate and orchestrate source code, data quality, and deliver features into production.

Data Quality

DataKitchen

APRIL 24, 2025

White Paper: A New, More Effective Approach To Data Quality Assessments Data quality leaders must rethink their role. They are neither compliance officers nor gatekeepers of platonic data ideals. In this new approach, the data quality assessment becomes a tool of persuasion and influence.

Data Quality

BI-Survey

JULY 10, 2025

At their presentation at this year’s DATA festival in Munich, the team of the Global Legal Entity Identifier Foundation (GLEIF) turned the conversation around. GLEIF takes pride in the quality of this data, even though it’s openly available. Setting the Scene: Who Is GLEIF and What Is a LEI?

Data Quality

DataKitchen

JULY 8, 2025

The Hidden Crisis in Data Usability … and How DataOps Data Quality TestGen Can Help Fix It In many data organizations, there’s a silent crisis: data usability is broken. Whether you work in data quality or engineering, you’ve probably said one of these things: “That’s the way the data came to us.”

Scorecard

CIO Business Intelligence

NOVEMBER 27, 2024

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Enterprise

DataKitchen

JULY 31, 2025

These aren’t just any data points—they are the backbone of your operations, the foundation of your decision-making, and often the difference between business success and failure. Identifying CDEs is a vital step in data governance because it changes how organizations handle data quality.

Data Governance

DataKitchen

MAY 2, 2025

Is Your Team in Denial of Data Quality? Here’s How to Tell In many organizations, data quality problems fester in the shadowsignored, rationalized, or swept aside with confident-sounding statements that mask a deeper dysfunction. That’s not data quality; that’s data folklore.

Data Quality

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.

Data Quality

CIO Business Intelligence

APRIL 8, 2025

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

Data Quality

CIO Business Intelligence

DECEMBER 11, 2024

If youre not keeping up the fundamentals of data and data management, your ability to adopt AIat whatever stage you are at in your AI journeywill be impacted, Kulkarni points out. Without it, businesses risk perpetuating the very inefficiencies they aim to eliminate, adds Kulkarni.

Risk

Data Virtualization

JUNE 12, 2025

Reading Time: 5 minutes There is a lot of talk about data quality, but I often have the impression that this is done almost to exorcise it, to recognize its importance without delving into its complexities, or its real meaning. But this can result.

Data Quality

Jen Stirrup

JULY 12, 2025

According to the MIT Technology Review's 2024 Data Integration Survey, organizations with highly fragmented data environments spend up to 67% of their data scientists' time on data collection and preparation rather than on developing and refining AI models. million annually.

Enterprise

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.

Data Analytics

KDnuggets

JUNE 24, 2025

Instead of writing the same cleaning code repeatedly, a well-designed pipeline saves time and ensures consistency across your data science projects. In this article, well build a reusable data cleaning and validation pipeline that handles common data quality issues while providing detailed feedback about what was fixed.

Machine Learning

DataKitchen

MARCH 20, 2025

We’ve identified two distinct types of data teams: process-centric and data-centric. Understanding this framework offers valuable insights into team efficiency, operational excellence, and data quality. Process-centric data teams focus their energies predominantly on orchestrating and automating workflows.

Data Quality

CIO Business Intelligence

JULY 8, 2025

To address this gap and ensure the data supply chain receives enough top-level attention, CIOs have hired or partnered with chief data officers, entrusting them to address the data debt , automate data pipelines , and transform to a proactive data governance model focusing on health metrics, data quality , and data model interoperability. [

Risk

DataKitchen

JULY 8, 2025

The Dual Challenge of Production and Development Testing Test coverage in data and analytics operates across two distinct but interconnected dimensions: production testing and development testing. Production test coverage ensures that data quality remains high and error rates remain low throughout the value pipeline during live operations.

Testing

Anmut

JANUARY 30, 2025

They made us realise that building systems, processes and procedures to ensure quality is built in at the outset is far more cost effective than correcting mistakes once made. How about data quality? Redman and David Sammon, propose an interesting (and simple) exercise to measure data quality.

Data Quality

CIO Business Intelligence

NOVEMBER 4, 2024

Data security, data quality, and data governance still raise warning bells Data security remains a top concern. Respondents rank data security as the top concern for AI workloads, followed closely by data quality. AI applications rely heavily on secure data, models, and infrastructure.

Marketing