erwin

JANUARY 9, 2020

For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Today’s data modeling is not your father’s data modeling software. So here’s why data modeling is so critical to data governance.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

erwin

OCTOBER 8, 2020

No less daunting, your next step is to re-point or even re-platform your data movement processes. And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. The metadata-driven suite automatically finds, models, ingests, catalogs and governs cloud data assets.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

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.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

CIO Business Intelligence

SEPTEMBER 23, 2024

For sectors such as industrial manufacturing and energy distribution, metering, and storage, embracing artificial intelligence (AI) and generative AI (GenAI) along with real-time data analytics, instrumentation, automation, and other advanced technologies is the key to meeting the demands of an evolving marketplace, but it’s not without risks.

Manufacturing 122

Manufacturing Internet of Things Metadata Risk 122

erwin

JANUARY 30, 2020

The results of our new research show that organizations are still trying to master data governance, including adjusting their strategies to address changing priorities and overcoming challenges related to data discovery, preparation, quality and traceability. And close to 50 percent have deployed data catalogs and business glossaries.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

erwin

DECEMBER 9, 2024

And do you have the transparency and data observability built into your data strategy to adequately support the AI teams building them? Will the new creative, diverse and scalable data pipelines you are building also incorporate the AI governance guardrails needed to manage and limit your organizational risk?

Metadata 52

Metadata Data Quality Sales Modeling 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

O'Reilly on Data

MARCH 20, 2019

Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Disparate impact analysis: see section 1.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

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. What Does a Data Catalog Consist Of?

Metadata 133

Metadata IT Data-driven Data Quality 133

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

DataKitchen

OCTOBER 20, 2023

Example 2: The Data Engineering Team Has Many Small, Valuable Files Where They Need Individual Source File Tracking In a typical data processing workflow, tracking individual files as they progress through various stages—from file delivery to data ingestion—is crucial.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

erwin

JANUARY 17, 2020

Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise. SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

AWS Big Data

DECEMBER 4, 2024

Our customers tell us that the fragmented nature of permissions and access controls, managed separately within individual data sources and tools, leads to inconsistent implementation and potential security risks. Having confidence in your data is key.

Data Analytics 93

Data Analytics Analytics Data Lake Data Quality 93

CIO Business Intelligence

APRIL 29, 2022

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

Data Governance 145

Data Governance IT Risk Data Lake 145

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. Who are the data owners? Data lineage offers proof that the data provided is reflected accurately.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

erwin

JANUARY 24, 2019

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape. With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way. Governing metadata.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

CIO Business Intelligence

OCTOBER 22, 2024

The hybrid cloud factor A modicum of interoperability between public clouds may be achieved through network interconnects, APIs, or data integration between them, but “you probably won’t find too much of that unless it’s the identical application running in both clouds,” IDC’s Tiffany says.

Strategy 128

Strategy Cost-Benefit Risk Enterprise 128

AWS Big Data

APRIL 29, 2024

This system simplifies managing user access, saves time for data security administrators, and minimizes the risk of configuration errors. Addressing big data challenges – Big data comes with unique challenges, like managing large volumes of rapidly evolving data across multiple platforms.

Management 114

Management Big Data Data Warehouse Metadata 114

CIO Business Intelligence

JULY 10, 2024

With data privacy and security becoming an increased concern, Sovereign cloud is turning from an optional, like-to-have, to an essential requirement, especially for highly protected markets like Government, Healthcare, Financial Services, Legal, etc. This local presence is crucial for maintaining data integrity and security.

Metadata 98

Metadata Data-driven Marketing Measurement 98

DataKitchen

SEPTEMBER 21, 2023

Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? ” For example, these tools may offer metadata-based notifications.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Grooper

MARCH 5, 2020

However, if you haven’t explicitly defined what information stewardship is, or there is some confusion regarding roles and responsibilities for your precious data – your data-related projects are at a high risk for failure. Lower cost data processes. More effective business process execution.

Data Lake 98

Data Lake Metadata Data Quality Software 98

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Automate code generation : Alleviate the need for developers to hand code connections from data sources to target schema.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. Data discoverability Unlike structured data, which is managed in well-defined rows and columns, unstructured data is stored as objects.

Data Governance 117

Data Governance Unstructured Data Metadata Data Lake 117

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. This may also entail working with new data through methods like web scraping or uploading.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

AWS Big Data

JUNE 25, 2024

It provides secure, real-time access to Redshift data without copying, keeping enterprise data in place. This eliminates replication overhead and ensures access to current information, enhancing data integration while maintaining data integrity and efficiency.

Data Lake 118

Data Lake Cost-Benefit Data-driven Data Warehouse 118

AWS Big Data

NOVEMBER 29, 2023

With Redshift, we are able to view risk counterparts and data in near real time— instead of on an hourly basis. Amazon Redshift ML large language model (LLM) integration Amazon Redshift ML enables customers to create, train, and deploy machine learning models using familiar SQL commands.

Data Warehouse 121

Data Warehouse Analytics Data Lake Machine Learning 121

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 86

Metadata Data Lake Optimization Strategy 86

CIO Business Intelligence

APRIL 27, 2022

All are ideally qualified to help their customers achieve and maintain the highest standards for data integrity, including absolute control over data access, transparency and visibility into the provider’s operation, the knowledge that their information is managed appropriately, and access to VMware’s growing ecosystem of sovereign cloud solutions.

Digital Transformation 111

Digital Transformation Metadata Risk Enterprise 111

AWS Big Data

AUGUST 15, 2024

With Amazon DataZone, individual business units can discover and directly consume these new data assets, gaining insights to a holistic view of the data (360-degree insights) across the organization. The Central IT team manages a unified Redshift data warehouse, handling all data integration, processing, and maintenance.

Data Lake 116

Data Lake Data Warehouse Data Governance Publishing 116

Octopai

JUNE 1, 2020

Data mapping is the cornerstone of many important business intelligence processes: Data Integration – Even if systems are compatible, combining two disparate data repositories still requires meticulous data mapping. Manual Metadata Management Hinders Compliance.

Metadata 52

Metadata Data Warehouse Data Integration Finance 52

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. And for data models that can be directly reported, a dimensional model can be developed.

Data Warehouse 78

Data Warehouse Data Lake Cost-Benefit Metadata 78

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

Cost-Benefit 76

Cost-Benefit Metadata Data Governance Modeling 76

Wayne Yaddow

MARCH 5, 2025

The goal is to examine five major methods of verifying and validating data transformations in data pipelines with an eye toward high-quality data deployment. First, we look at how unit and integration tests uncover transformation errors at an early stage. Test harnesses that mimic pipeline steps (e.g.,

Testing 52

Testing Data Transformation Statistics Metadata 52

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

AWS Big Data

OCTOBER 11, 2023

This is due to the complexity of the JSON structure, contracts, and the risk evaluation process on the payor side. Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics. Then you can use Amazon Athena V3 to query the tables in the Data Catalog.

Visualization 116

Visualization Dashboards Data-driven Gap analysis 116

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.

Data Strategy 84

Data Strategy Strategy Data Warehouse Prescriptive Analytics 84