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.

Data Quality 130

Data Quality Testing Measurement Data Integration 130

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.

Data Integration 102

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.

Data Quality 112

Data Quality Statistics Visualization Metrics 112

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.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

MARCH 19, 2025

On top of that, they are storing data in IT environments that are increasingly complex, including in the cloud and on mainframes, sometimes simultaneously, all while needing to ensure proper security and compliance. How do companies ensure their data landscape is ready for the future? All of this complexity creates a challenge.

Data Integration 52

Data Integration Data Quality Visualization Risk 52

Sisense

AUGUST 28, 2020

We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways data teams are tackling the challenges of this new world to help their companies and their customers thrive. What is data integrity?

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

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.

Data Integration 122

Data Integration Strategy Software Management 122

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

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.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

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

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 300

Testing Machine Learning Consulting Data Science 300

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.

Data Quality 101

Data Quality Metrics Data Lake Sales 101

DataKitchen

JANUARY 25, 2022

When internal resources fall short, companies outsource data engineering and analytics. Large enterprises integrate hundreds or thousands of asynchronous data sources into a web of pipelines that flow into visualizations and purpose-built databases that support self-service analysis. Here is where the loss of control begins.

Consulting 244

Consulting Testing Data Lake Data Quality 244

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. Data teams struggle to find a unified approach that enables effortless discovery, understanding, and assurance of data quality and security across various sources.

Data Analytics 85

Data Analytics Analytics Data Lake Data Quality 85

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.

Data Integration 98

Data Integration Manufacturing Data Quality Data Governance 98

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.

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

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

CIO Business Intelligence

APRIL 29, 2022

This would be straightforward task were it not for the fact that, during the digital-era, there has been an explosion of data – collected and stored everywhere – much of it poorly governed, ill-understood, and irrelevant. Data Centricity. There is evidence to suggest that there is a blind spot when it comes to data in the AI context.

Data Governance 145

Data Governance IT Data Lake Risk 145

Smart Data Collective

JULY 15, 2021

A data catalog serves the same purpose. By using metadata (or short descriptions), data catalogs help companies gather, organize, retrieve, and manage information. You can think of a data catalog as an enhanced Access database or library card catalog system. It helps you locate and discover data that fit your search criteria.

Metadata 133

Metadata IT Data-driven Data Quality 133

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? I hope that you enjoy reading this blog post, but most important, I hope you always remember: “Data are friends, not food.” Data Silos. Good feeling’s gone—AHH!”

Data Quality 56

Data Quality Enterprise Business Intelligence Data Governance 56

CIO Business Intelligence

JANUARY 9, 2024

Our firm’s leaders] wanted to make sure there were guidelines in place to protect the company, its data, and its people.” “The CIO is at the nexus of those conversations,” says Tim Crawford, CIO strategic adviser at Los Angeles-based IT advisory firm AVOA. Which business cases actually need AI?

IT 140

IT Risk Cost-Benefit Machine Learning 140

Alation

AUGUST 9, 2022

Today, data is more valuable to a company than it’s ever been. It can help you make data-driven decisions which can improve business performance, boost revenue, and improve efficiencies. But in the four years since it came into force, have companies reached their full potential for data integrity? What is data integrity?

Data Integration 52

Data Integration Data Quality Measurement Data-driven 52

DataKitchen

SEPTEMBER 21, 2023

This is where the true power of complete data observability comes into play, and it’s time to get acquainted with its two critical parts: ‘Data in Place’ and ‘Data in Use.’ 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

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

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.

Data Quality 136

Data Quality Testing Data Lake Data Integration 136

David Menninger's Analyst Perspectives

OCTOBER 8, 2024

Collibra was founded in 2008 by Chief Executive Officer Felix Van de Maele and Chief Data Citizen Stijn Christiaens. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed internally by workers or externally by partners and customers.

Data Quality 130

Data Quality Data Governance Enterprise Visualization 130

DataKitchen

MAY 10, 2021

Data errors also affect careers. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics. Some of the DataOps best practices and industry discussion around errors have coalesced around the term “data observability.”

Testing 214

Testing Manufacturing Data Quality Statistics 214

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.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

AWS Big Data

JANUARY 15, 2025

Their terminal operations rely heavily on seamless data flows and the management of vast volumes of data. Recently, EUROGATE has developed a digital twin for its container terminal Hamburg (CTH), generating millions of data points every second from Internet of Things (IoT)devices attached to its container handling equipment (CHE).

IoT 100

IoT Machine Learning Metadata Data-driven 100

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.

Testing 130

Testing Statistics Data Quality Data Integration 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

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.

Data Quality 52

Data Quality Strategy ROI Testing 52

CIO Business Intelligence

NOVEMBER 13, 2024

Now, picture doing that with a mountain of data. Infused with the magic of artificial intelligence (AI), DataLark revolutionizes data migration, making it faster, more efficient, and surprisingly painless. It involves shifting massive amounts of data from outdated legacy systems to a sleek, modern ERP platform.

Cost-Benefit 64

Cost-Benefit Data-driven Data Integration Data Quality 64

Andrew White

JUNE 11, 2019

Salesforce closes acquisition of Mulesoft – May 2018 (business app vendor acquires data integration). But the recent spate of acquisitions just prove the point: the work of data and analytics governance remains an after-thought, even for these large vendors (just as it does for many of their prospects).

IT 82

IT Data Quality Data Integration Business Objectives 82

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

Data Quality 52

Data Quality Metadata Reporting Metrics 52

O'Reilly on Data

DECEMBER 19, 2018

are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage. GitHub is an excellent tool for managing code, but we need to think about [code+data].

Machine Learning 279

Machine Learning Software Metadata Testing 279

DataKitchen

AUGUST 16, 2024

As the pioneer in the DataOps category, we are proud to have laid the groundwork for what has become an essential approach to managing data operations in today’s fast-paced business environment. At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data.

Marketing 124

Marketing Data Quality Testing Metadata 124