DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The communication between business units and data professionals is usually incomplete and inconsistent. Introduction to Data Mesh. Source: Thoughtworks.

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

Cloudera

JUNE 29, 2023

To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.

Data Architecture 91

Data Architecture Risk Management Risk Management 91

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 111

Data Quality Data Governance Data Lake Testing 111

AWS Big Data

SEPTEMBER 11, 2024

This complex process involves suppliers, logistics, quality control, and delivery. This post describes how HPE Aruba automated their Supply Chain management pipeline, and re-architected and deployed their data solution by adopting a modern data architecture on AWS.

Data Architecture 110

Data Architecture Optimization Data Warehouse Metadata 110

Cloudera

AUGUST 30, 2022

Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Data quality issues deter trust and hinder accurate analytics. Modern data architectures. Deploying modern data architectures. Forrester ).

Data Architecture 84

Data Architecture Data Lake Data Warehouse Metadata 84

CIO Business Intelligence

OCTOBER 30, 2024

The path to achieving AI at scale is paved with myriad challenges: data quality and availability, deployment, and integration with existing systems among them. Another challenge here stems from the existing architecture within these organizations.

Enterprise 139

Enterprise Data Architecture Unstructured Data Insurance 139

Jim Harris

JANUARY 1, 2016

1 — Investigate Data quality is not exactly a riddle wrapped in a mystery inside an enigma. However, understanding your data is essential to using it effectively and improving its quality. In order for you to make sense of those data elements, you require business context.

Data Quality 61

Data Quality Data Governance Cost-Benefit ROI 61

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 Data quality is essentially the measure of data integrity.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

Smart Data Collective

FEBRUARY 23, 2021

To help you identify and resolve these mistakes, we’ve put together this guide on the various big data mistakes that marketers tend to make. Big Data Mistakes You Must Avoid. Here are some common big data mistakes you must avoid to ensure that your campaigns aren’t affected. Ignoring Data Quality.

Big Data 122

Big Data Visualization Data Quality Data-driven 122

TDAN

DECEMBER 4, 2024

We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]

Unstructured Data 52

Unstructured Data Data Quality Data Architecture Management 52

Data Virtualization

FEBRUARY 28, 2020

Too often the design of new data architectures is based on old principles: they are still very data-store-centric. They consist of many physical data stores in which data is stored repeatedly and redundantly. Over time, new types of data stores,

Data Architecture 52

Data Architecture Digital Transformation Data Lake Data Quality 52

AWS Big Data

DECEMBER 4, 2024

With this launch, you can query data regardless of where it is stored with support for a wide range of use cases, including analytics, ad-hoc querying, data science, machine learning, and generative AI. We’ve simplified data architectures, saving you time and costs on unnecessary data movement, data duplication, and custom solutions.

Data Analytics 101

Data Analytics Analytics Data Lake Data Quality 101

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. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Cloudera

MAY 14, 2024

More than that, though, harnessing the potential of these technologies requires quality data—without it, the output from an AI implementation can end up inefficient or wholly inaccurate. Meaningful results, and a scalable, flexible data architecture demand a ‘true’ hybrid cloud approach to data management.

Data Architecture 104

Data Architecture Data Governance Unstructured Data Structured Data 104

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.

Data Governance 110

Data Governance Management Metadata Risk Management 110

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 Data Quality 119

erwin

OCTOBER 24, 2019

The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires. Organizations then can take a data-driven approach to business transformation , speed to insights, and risk management.

Metadata 104

Metadata Management Data-driven Data Architecture 104

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. A DataOps implementation project consists of three steps.

Testing 162

Testing Metadata Dashboards Statistics 162

TDAN

JUNE 18, 2019

The first step to fixing any problem is to understand that problem—this is a significant point of failure when it comes to data. Most organizations agree that they have data issues, categorized as data quality. However, this definition is […].

Data Quality 90

Data Quality Data Architecture IT Data Strategy 90

TDAN

APRIL 16, 2019

The phrase “data architecture” often has different connotations across an organization depending on where their job role is. For instance, most of my earlier career roles were within IT, though throughout the last decade or so, has been primarily working with business line staff.

Data Architecture 40

Data Architecture IT Data Quality Data Governance 40

CIO Business Intelligence

NOVEMBER 29, 2022

A sea of complexity For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall data architecture introduces more complexity.

Data Architecture 111

Data Architecture Data Integration IoT Strategy 111

IBM Big Data Hub

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

Strategy 85

Strategy Data Architecture Cost-Benefit Reporting 85

erwin

DECEMBER 6, 2019

Enterprise Data Management Methodology : DG is foundational to enterprise data management. metadata management, enterprise data architecture, data quality management), DG will be a struggle. Without the other essential components (e.g.,

Data Governance 145

Data Governance Metadata Enterprise Data-driven 145

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI).

Data Warehouse 113

Data Warehouse KPI Optimization Cost-Benefit 113

CIO Business Intelligence

JUNE 9, 2023

A few years ago, Gartner found that “organizations estimate the average cost of poor data quality at $12.8 million per year.’” Beyond lost revenue, data quality issues can also result in wasted resources and a damaged reputation. Learn more about data architectures in my article here.

Strategy 116

Strategy Management Key Performance Indicator ROI 116

Cloudera

SEPTEMBER 15, 2022

The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.

Data Lake 88

Data Lake Data Architecture Data-driven Data Warehouse 88

CIO Business Intelligence

JANUARY 30, 2024

Birgit Fridrich, who joined Allianz as sustainability manager responsible for ESG reporting in late 2022, spends many hours validating data in the company’s Microsoft Sustainability Manager tool. Data quality is key, but if we’re doing it manually there’s the potential for mistakes.

Reporting 115

Reporting Data Quality Strategy Data-driven 115

CIO Business Intelligence

JULY 28, 2023

Modern data platforms can stop enterprises from drowning in a sea of data by integrating AI and ML to enable more efficient, accessible data. It also helps to overcome the challenges of shadow data, which enterprise security policies do not recognize or cover.

Risk 98

Risk Management Data mining Cost-Benefit 98

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Cloudera

DECEMBER 1, 2022

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. RI is a global leader in the design and deployment of large-scale, production-level modern data platforms for the world’s largest enterprises.

Metrics 90

Metrics Data Quality Data Lake Statistics 90

CIO Business Intelligence

MAY 20, 2022

Modernizing a utility’s data architecture. These capabilities allow us to reduce business risk as we move off of our monolithic, on-premise environments and provide cloud resiliency and scale,” the CIO says, noting National Grid also has a major data center consolidation under way as it moves more data to the cloud.

IT 105

IT Internet of Things Digital Transformation Data Architecture 105

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

CIO Business Intelligence

MAY 15, 2024

The ability to leverage data to understand and plan for those behaviors is extremely important. How did you improve the organization’s data literacy? Once we set up a data architecture that provides data liquidity, where data can go everywhere, we had to teach people how to use it.

Cost-Benefit 98

Cost-Benefit Data Governance Data Architecture Reporting 98

erwin

DECEMBER 12, 2019

As data continues to proliferate, so does the need for data and analytics initiatives to make sense of it all. Quicker Project Delivery: Accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc., by up to 70 percent.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

AWS Big Data

NOVEMBER 9, 2023

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. Data ingestion, whether real time or batch, forms the basis of any effective data analysis, enabling organizations to gather information from diverse sources and use it for insightful decision-making.

Data Warehouse 126

Data Warehouse Testing Data Quality Reporting 126

CIO Business Intelligence

JUNE 23, 2023

There are also no-code data engineering and AI/ML platforms so regular business users, as well as data engineers, scientists and DevOps staff, can rapidly develop, deploy, and derive business value. Of course, no set of imperatives for a data strategy would be complete without the need to consider people, process, and technology.

Data Strategy 116

Data Strategy Strategy Visualization Consulting 116