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. SageMaker simplifies the discovery, governance, and collaboration for data and AI across your lakehouse, AI models, and applications.

Data Analytics 85

Data Analytics Analytics Data Lake Data Quality 85

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

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 281

Machine Learning Modeling Testing Risk Management 281

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. This can cause anything from day-to-day issues to significant business problems and risks. But firstly, we need to look at how we define data integrity.

Data Integration 52

Data Integration Data Quality Measurement Strategy 52

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 169

Testing Data Quality Predictive Modeling Metrics 169

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

By automating data profiling and validation, it minimizes errors and maintains data integrity throughout the migration. Advanced algorithms and generative AI systematically check data for accuracy and completeness, catching inconsistencies that might otherwise slip through the cracks.

Cost-Benefit 64

Cost-Benefit Data-driven Data Integration Data Quality 64

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.

Data Governance 142

Data Governance Management Metadata Data Quality 142

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 169

Insurance Metadata Data-driven Data Quality 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

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

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. Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

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 Data Lake Risk 145

DataKitchen

FEBRUARY 27, 2024

However, the foundation of their success rests not just on sophisticated algorithms or computational power but on the quality and integrity of the data they are trained on and interact with. The Imperative of Data Quality Validation Testing Data quality validation testing is not just a best practice; it’s imperative.

Data Quality 117

Data Quality Unstructured Data Testing Data-driven 117

erwin

FEBRUARY 19, 2021

It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization. Is it sensitive or are there any risks associated with it? Metadata also helps your organization to: Discover data. Harvest data. Better data quality.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

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. Most have only data governance operations.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

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.” To get past those points, Colisto worked to educate leaders about the capabilities and risks of AI, seeking to move the company from “no to know.” What is our appetite for risk and how do we address it?

IT 140

IT Risk Cost-Benefit Machine Learning 140

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

Smart Data Collective

JULY 15, 2021

It helps you locate and discover data that fit your search criteria. With data catalogs, you won’t have to waste time looking for information you think you have. What Does a Data Catalog Do? This means that they can be ideal for data cleansing and maintenance. What Does a Data Catalog Consist Of?

Metadata 133

Metadata IT Data-driven Data Quality 133

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

CIO Business Intelligence

JANUARY 24, 2024

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

BizAcuity

NOVEMBER 13, 2024

The Significance of Data-Driven Decision-Making In sectors ranging from healthcare to finance, data-driven decision-making has become a strategic asset. Making decisions based on data, rather than intuition alone, brings benefits such as increased accuracy, reduced risks, and deeper customer insights.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

erwin

JULY 17, 2019

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Clean data in, clean analytics out. Indeed, every year low-quality data is estimated to cost over $9.7

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

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. Overcoming Data Governance Bottlenecks. Put data quality first : Users must have confidence in the data they use for analytics.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

CIO Business Intelligence

MAY 8, 2024

Then we have to make sense of the data, massage it and import it in our system. The first is to reconcile the data. Our system has a mandatory data integrity check, so if you try to import the data that doesn’t reconcile, our system isn’t going to let you, so we don’t allow any shortcuts.

Risk 111

Risk Testing Strategy Technology 111

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

MAY 17, 2021

However, if we’ve learned anything, isn’t it that data governance is an ever-evolving, ever-changing tenet of modern business? We explored the bottlenecks and issues causing delays across the entire data value chain. Data governance provides visibility, automation, governance and collaboration for data democratization.

Data Governance 99

Data Governance Data Quality Snapshot Reporting 99

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. 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 Governance 109

Data Governance Unstructured Data Metadata Data Lake 109

CIO Business Intelligence

MAY 30, 2024

Talk to us about how leaders should be thinking about the role of data quality in terms of their AI deployments. Data quality is the cornerstone of effective AI deployment. Leaders must prioritize investments in data quality and governance. Leaders should view data quality as a strategic asset.

Data Quality 104

Data Quality Key Performance Indicator Digital Transformation Management 104

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

Analytics 98

Analytics Insurance Big Data Uncertainty 98

BI-Survey

FEBRUARY 17, 2020

Best-in-class companies have realized that it is important to cover all data environments and establish a feedback loop from data usage in BI and analytics to drive data improvements. While compliance is the major driver for data governance, it bears the risk of reducing it to a very restrictive procedure.

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

DataKitchen

OCTOBER 2, 2023

One of the most pressing issues is the ownership of databases by multiple data teams, each with its governance protocols, leading to a volatile data environment rife with inconsistencies and errors. This lack of control is exacerbated by many people and/or automated data ingestion processes introducing changes to the data.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

AWS Big Data

JULY 29, 2024

This ensures that each change is tracked and reversible, enhancing data governance and auditability. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks. This helps reduce the risk of false alerts.

Metadata 118

Metadata Snapshot Data Lake Metrics 118

Octopai

MAY 15, 2023

By analyzing this information, organizations can optimize their infrastructure and storage strategies, avoiding unnecessary storage costs and efficiently allocating resources based on data usage patterns. Data integration and ETL costs: Large organizations often deal with complex data integration and Extract, Transform, Load (ETL) processes.

Data Quality 52

Data Quality Data Governance Data Integration Risk 52

IBM Big Data Hub

MAY 15, 2024

Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges.

Cost-Benefit 67

Cost-Benefit Data Integration Data Architecture Manufacturing 67

Octopai

JUNE 26, 2020

Data governance tools are available to help ensure availability, usability, consistency, data integrity and data security. This helps establish clear processes for effective data management throughout the enterprise. Without metadata, the organization is at risk of making decisions based on the wrong data.”.

Metadata 98

Metadata Data Governance Enterprise Reporting 98