AWS Big Data

APRIL 3, 2024

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

Data Quality 116

Data Quality Visualization Metadata Metrics 116

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

Metadata 98

Metadata Management Data Quality Data Governance 98

AWS Big Data

DECEMBER 4, 2024

Collaborate and build faster using familiar AWS tools for model development, generative AI, data processing, and SQL analytics with Amazon Q Developer , the most capable generative AI assistant for software development, helping you along the way. Having confidence in your data is key.

Data Analytics 102

Data Analytics Analytics Data Lake Data Quality 102

O'Reilly on Data

DECEMBER 19, 2018

In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0

Machine Learning 279

Machine Learning Software Metadata Testing 279

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 350

Enterprise Deep Learning Data Governance Risk 350

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. These are useful for flexible data lifecycle management.

Snapshot 83

Snapshot Metadata Data Lake Optimization 83

DataKitchen

AUGUST 3, 2021

The data mesh addresses the problems characteristic of large, complex, monolithic data architectures by dividing the system into discrete domains that are managed by smaller, cross-functional teams. The domain includes data, code, workflows, a team, and a technical environment.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

David Menninger's Analyst Perspectives

MAY 8, 2025

DataOps was initially seen as antithetical to traditional data management approaches, which typically included batch-based and manual tools and practices. The term was embraced by emerging software providers as a means of differentiating from established data management providers. This is no longer the case, however.

Data Quality 130

Data Quality Data Governance Data Integration Software 130

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). Why AI software development is different. AI products are automated systems that collect and learn from data to make user-facing decisions. We know what “progress” means.

Management 363

Management Machine Learning Experimentation Metrics 363

O'Reilly on Data

JUNE 19, 2019

Why aren’t traditional software tools sufficient? In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Metadata and artifacts needed for a full audit trail.

Modeling 256

Modeling Machine Learning Testing Metrics 256

David Menninger's Analyst Perspectives

OCTOBER 8, 2024

As I recently noted , the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption.

Data Quality 130

Data Quality Data Governance Enterprise Visualization 130

CIO Business Intelligence

SEPTEMBER 19, 2024

It will do this, it said, with bidirectional integration between its platform and Salesforce’s to seamlessly delivers data governance and end-to-end lineage within Salesforce Data Cloud. Additional to that, we are also allowing the metadata inside of Alation to be read into these agents.”

Data Governance 123

Data Governance Metadata Unstructured Data Structured Data 123

erwin

DECEMBER 9, 2024

If you are not observing and reacting to the data, the model will accept every variant and it may end up one of the more than 50% of models, according to Gartner , that never make it to production because there are no clear insights and the results have nothing to do with the original intent of the model.

Metadata 52

Metadata Data Quality Sales Modeling 52

CIO Business Intelligence

MAY 24, 2022

In order to help maintain data privacy while validating and standardizing data for use, the IDMC platform offers a Data Quality Accelerator for Crisis Response. Cloud Computing, Data Management, Financial Services Industry, Healthcare Industry

Finance 140

Finance Management Metadata Machine Learning 140

erwin

JANUARY 23, 2025

Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives. With a variety of providers and offerings addressing data intelligence and governance needs, it can be easy to feel overwhelmed in selecting the right solution for your enterprise.

Metadata 52

Metadata Data Quality Data Governance Software 52

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

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

DataKitchen

AUGUST 16, 2024

2024 Gartner Market Guide To DataOps We at DataKitchen are thrilled to see the publication of the Gartner Market Guide to DataOps, a milestone in the evolution of this critical software category. At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data.

Marketing 126

Marketing Data Quality Testing Metadata 126

Ontotext

MAY 26, 2021

This happens through the process of semantic annotation , where documents are tagged with relevant concepts and enriched with metadata , i.e., references that link the content to concepts, described in a knowledge graph. Evaluation is for AI systems what quality assurance (QA) is for software systems.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

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

Insurance Metadata Data-driven Data Quality 173

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly.

IT 135

IT Metadata Data-driven Data Governance 135

AWS Big Data

FEBRUARY 29, 2024

Implement data privacy policies. Implement data quality by data type and source. Let’s look at some of the key changes in the data pipelines namely, data cataloging, data quality, and vector embedding security in more detail. Link structured and unstructured datasets.

Data Governance 122

Data Governance Unstructured Data Metadata Data Lake 122

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Smart Data Collective

SEPTEMBER 26, 2021

The workflow is basically a sequence of tasks that processes a set of data. These days, you have software to help you handle the process. The best part about data workflow management is that you can take a task and develop a custom solution to bring clarity to the entire team on what needs to be done and, most importantly, how.

Metadata 130

Metadata Visualization Unstructured Data Data mining 130

erwin

OCTOBER 29, 2020

We also looked at data preparation, governance and intelligence to see where organizations might be getting stuck and spending lots of time. Data quality and accuracy are recurring themes as well. And you can schedule metadata scans to ensure it’s always refreshed and up to date.

Data Governance 105

Data Governance Metadata Digital Transformation ROI 105

erwin

SEPTEMBER 2, 2024

Added data quality capability ready for an AI era Data quality has never been more important than as we head into this next AI-focused era. erwin Data Quality is the data quality heart of erwin Data Intelligence. erwin Data Quality is the data quality heart of erwin Data Intelligence.

Data Quality 52

Data Quality Data Processing Measurement Metadata 52

AWS Big Data

SEPTEMBER 11, 2024

Aruba offers networking hardware like access points, switches, routers, software, security devices, and Internet of Things (IoT) products. Each file arrives as a pair with a tail metadata file in CSV format containing the size and name of the file. To achieve this, Aruba used Amazon S3 Event Notifications.

Data Architecture 109

Data Architecture Optimization Data Warehouse Metadata 109

CIO Business Intelligence

APRIL 29, 2022

This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. The shift away from ‘Software 1.0’ where applications have been based on hard-coded rules has begun and the ‘Software 2.0’ era is upon us. Addressing the Challenge.

Data Governance 145

Data Governance IT Risk Data Lake 145

CIO Business Intelligence

JANUARY 15, 2024

Recognizing that giving scientists and researchers access to its data was fundamental to its purpose, SMD developed its Open Source Science Initiative (OSSI) as a result of that report in an effort to make publicly funded scientific research transparent, inclusive, accessible, and reproducible.

Informatics 131

Informatics Metadata Data mining Digital Transformation 131

erwin

SEPTEMBER 17, 2020

Data automation reduces the loss of time in collecting, processing and storing large chunks of data because it replaces manual processes (and human errors) with intelligent processes, software and artificial intelligence (AI). Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

Smart Data Collective

OCTOBER 13, 2021

Data visualization is a concept that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that may go unnoticed in text-based data can be more easily exposed and recognized with data visualization software.

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

DataKitchen

JUNE 24, 2021

He added, “Most organizations are well-versed in software and application development. The first-class citizen is data and the product that you’re manufacturing is a data solution. Ryan Chapin explained that at GE Aviation the main products such as jet engines generated tons and tons of data.

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

CIO Business Intelligence

DECEMBER 21, 2022

BI software helps companies do just that by shepherding the right data into analytical reports and visualizations so that users can make informed decisions. Stout, for instance, explains how Schellman addresses integrating its customer relationship management (CRM) and financial data. “A

IT 131

IT Business Intelligence Sales Key Performance Indicator 131

AWS Big Data

DECEMBER 9, 2024

As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

Snapshot 62

Snapshot Data Warehouse Data Lake Data Quality 62

Grooper

MARCH 5, 2020

If your organization has any kind of data and analytics initiative, then chances are you have people – maybe even an entire department dedicated to managing and integrating data for (and between) software applications to achieve some sort of business outcome. Is a Power-User or a Data Scientist an Information Steward?

Data Lake 98

Data Lake Metadata Data Quality Software 98