Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Cloudera

JANUARY 21, 2021

And you also already know siloed data is costly, as that means it will be much tougher to derive novel insights from all of your data by joining data sets. Of course you don’t want to re-create the risks and costs of data silos your organization has spent the last decade trying to eliminate. Must you be: .

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

CIO Business Intelligence

JANUARY 30, 2024

Even for more straightforward ESG information, such as kilowatt-hours of energy consumed, ESG reporting requirements call for not just the data, but the metadata, including “the dates over which the data was collected and the data quality,” says Fridrich. “The complexity is at a much higher level.”

Reporting 100

Reporting Data Quality Strategy Data-driven 100

Cloudera

DECEMBER 16, 2022

There are six key capabilities that cement our leadership in enterprise data platforms: 1-Hybrid and multi-cloud portability and scale to support the most demanding workloads. Anything else requires integration, sometimes between multiple vendors, which means complexity and risk. 4-Ready for modern data fabric architectures.

Management 88

Management Metadata Machine Learning Data Lake 88

Cloudera

AUGUST 4, 2021

Data governance used to be considered a “nice to have” function within an enterprise, but it didn’t receive serious attention until the sheer volume of business and personal data started taking off with the introduction of smartphones in the mid-2000s.

Data Governance 83

Data Governance Metadata Data-driven Enterprise 83

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. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues.

Data Warehouse 63

Data Warehouse Data Lake Cost-Benefit Modeling 63

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 85

Metadata Data Lake Optimization Strategy 85

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 64

Data Architecture Data Lake Machine Learning Data Governance 64

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. Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98