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

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 Internet of Things 102

CIO Business Intelligence

MARCH 19, 2025

Discovering data across a hybrid infrastructure Harnessing the full potential of data in a hybrid environment starts with a thorough discovery process. Teams must first identify the information thats crucial to the business and any associated regulatory requirements.

Data Integration 52

Data Integration Data Quality Visualization Optimization 52

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

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 Sales Data Lake 120

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

Sisense

AUGUST 28, 2020

However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. In this article, we’ll dig into the core aspects of data integrity, what processes ensure it, and how to deal with data that doesn’t meet your standards.

Data Integration 101

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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

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.

IT 141

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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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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 98

Data Quality Data-driven Data Lake Metrics 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.

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Data Quality Data Governance Metrics Statistics 94

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

Grooper

MARCH 5, 2020

Information stewards are the critical link for organizations committed to innovation and maximizing the effective use of data. Haven’t heard the term “information steward” before? By solidifying your understanding of information stewardship, you ensure: Better use of internal resources. Lower cost data processes.

Data Lake 98

Data Lake Metadata Data Quality Software 98

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications.

IoT 110

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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? I hope that you enjoy reading this blog post, but most important, I hope you always remember: “Data are friends, not food.” Data Silos. You, Data-Dude, takin’ on the defects.

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Data Quality Enterprise Business Intelligence Data Governance 56

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 139

Alation

AUGUST 9, 2022

But almost all industries across the world face the same challenge: they aren’t sure if their data is accurate and consistent, which means it’s not trustworthy. On top of this, we’re living through the age of big data , where more information is being processed and stored by organisations that also have to manage regulations.

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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. Self-service access to data is only truly valuable if users can trust the data they have access to, however. Regards, Matt Aslett

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Data Quality Data Governance Enterprise Visualization 130

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.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

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DataKitchen

MAY 10, 2021

No, it could be the effect of an intentional change upstream, but the test gives the data team a chance to investigate and inform users if a change impacts analytics. Tests and alerts enable proactive communication with users that builds data team credibility. It’s not about data quality . Data Quality.

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Testing Manufacturing Data Quality Statistics 214

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

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, 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. Or did the schema change?

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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.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

DataKitchen

FEBRUARY 27, 2024

This brings us to the crucial concept of a “Data Journey” — a comprehensive framework that ensures data quality from its inception to its final use in LLMs. This approach allows LLMs to pull in relevant data when needed, enriching the model’s responses more accurately and contextually.

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Data Quality Unstructured Data Testing Data-driven 118

Smart Data Collective

JULY 15, 2021

A data catalog serves the same purpose. It organizes the information your company has on hand so you can find it easily. By using metadata (or short descriptions), data catalogs help companies gather, organize, retrieve, and manage information. It helps you locate and discover data that fit your search criteria.

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Metadata IT Data-driven Data Quality 133

Data Virtualization

SEPTEMBER 12, 2024

Reading Time: 2 minutes In today’s data-driven landscape, the integration of raw source data into usable business objects is a pivotal step in ensuring that organizations can make informed decisions and maximize the value of their data assets. To achieve these goals, a well-structured.

Data Integration 52

Data Integration Business Objectives Data-driven Management 52

DataKitchen

MAY 10, 2024

The Third 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. Is My Model Still Accurate?

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Metrics Testing Data Quality Dashboards 126

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 173

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erwin

FEBRUARY 19, 2021

However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. It is metadata that turns information into an asset.”. Quite simply, metadata is data about data. Better data quality.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

IBM Big Data Hub

MARCH 25, 2022

To simplify data access and empower users to leverage trusted information, organizations need a better approach that provides better insights and business outcomes faster, without sacrificing data access controls. Automated data enrichment : To create the knowledge catalog, you need automated data stewardship services.

Metadata 104

Metadata Data Quality Data Governance Data Integration 104

Smart Data Collective

JULY 9, 2023

By understanding the power of ETL, organisations can harness the potential of their data and gain valuable insights that drive informed choices. ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse.

Data Quality 105

Data Quality Data Warehouse Data-driven Reporting 105

erwin

DECEMBER 9, 2024

For instance, Large Language Models (LLMs) are known to ultimately perform better when data is structured. And being that data is fluid and constantly changing, its very easy for bias, bad data and sensitive information to creep into your AI data pipeline. And lets not forget about the controls.

Metadata 52

Metadata Data Quality Sales Modeling 52

CIO Business Intelligence

NOVEMBER 7, 2023

For years, IT and business leaders have been talking about breaking down the data silos that exist within their organizations. Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. There’s also the issue of bias.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Seeing data pipelines and information flows further supports compliance efforts. Data Quality.

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