AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. FMs are multimodal; they work with different data types such as text, video, audio, and images.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Ontotext

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

CIO Business Intelligence

NOVEMBER 1, 2023

SAP doesn’t want to build those tools from scratch itself: “We definitely want to leverage what’s already out there,” Sun said, noting there are already many large language models (LLMs) it can build on, adding its own prompting, fine tuning, and data embedding to get those models to business customers quickly.

Enterprise 129

Enterprise Cost-Benefit Unstructured Data Software 129

Cloudera

JANUARY 30, 2023

Have you ever considered how much data a single person generates in a day? Every web document, scanned document, email, social media post, and media download? One estimate states that “ on average, people will produce 463 exabytes of data per day by 2025.” Now consider that the federal government has approximately 2.8

IoT 82

IoT Data Architecture Data Lake Machine Learning 82

IBM Big Data Hub

DECEMBER 16, 2022

Building the foundation: data architecture. Collecting, organizing, managing, and storing data is a complex challenge. A fit-for-purpose data architecture underpins effective data-driven organizations. Read more: Why IBM recommends a data fabric architecture as a solution.

Enterprise 52

Enterprise Data Architecture Data-driven ROI 52

CIO Business Intelligence

MARCH 24, 2023

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

Alation

MAY 31, 2023

This means that specialized roles such as data architects, which focus on modernizing data architecture to help meet business goals, are increasingly important to support data governance. What is a data architect? Their broad range of responsibilities include: Design and implement data architecture.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

IBM Big Data Hub

APRIL 11, 2023

The evaluation team should assess and document each system, decision point, and vendor by the population they serve, such as hourly workers, salaried employees, different pay groups, and countries. Policy orchestration within a data fabric architecture is an excellent tool that can simplify the complex AI audit processes.

Strategy 90

Strategy Data Architecture Data-driven Testing 90

CIO Business Intelligence

SEPTEMBER 21, 2023

AI is now a board-level priority Last year, AI consisted of point solutions and niche applications that used ML to predict behaviors, find patterns, and spot anomalies in carefully curated data sets. Today’s foundational models are jacks-of-all-trades. All of PwC’s clients are having this discussion, he says. Then gen AI came out.

Enterprise 127

Enterprise Consulting Modeling Cost-Benefit 127

CIO Business Intelligence

NOVEMBER 7, 2023

Opting for a centralized data and reporting model rather than training and embedding analysts in individual departments has allowed us to stay nimble and responsive to meet urgent needs, and prevented us from spending valuable resources on low-value data projects which often had little organizational impact,” Higginson says.

Data Warehouse 139

Data Warehouse Digital Transformation Data-driven Reporting 139

AWS Big Data

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. Amazon Textract – You can use this ML service to extract metadata from scanned documents and images.

Unstructured Data 121

Unstructured Data Metadata Management Analytics 121

IBM Big Data Hub

AUGUST 15, 2023

To overcome these challenges will require a shift in many of the processes and models that businesses use today: changes in IT architecture, data management and culture. A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it.

IT 76

IT Data Governance Modeling Cost-Benefit 76

erwin

JULY 12, 2019

Modern, strategic data governance , which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

erwin

JUNE 25, 2020

The Difference Between Technical Architecture and Enterprise Architecture. We previously have discussed the difference between data architecture and EA plus the difference between solutions architecture and EA. erwin Evolve is one such tool that also supports business process modeling use cases.

Enterprise 144

Enterprise Data Architecture Data-driven Cost-Benefit 144

DataKitchen

AUGUST 8, 2023

Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis. This is especially true regarding our one-to-many, producer-to-consumer relationships on our data architecture. Monitoring and testing the data to ensure its reliability continually is crucial.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

erwin

OCTOBER 24, 2019

Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

Metadata 104

Metadata Management Data-driven Data Architecture 104

CIO Business Intelligence

FEBRUARY 21, 2024

At the heart of the issue is retrieval-augmented generation (RAG), a natural language processing technique combining data retrieval with a GenAI model. But the critical step of data preparation can’t be overlooked — and today, it uses decades-old technologies.

Insurance 62

Insurance Unstructured Data Data Architecture Technology 62

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 84

Metadata Data Governance Digital Transformation Data Quality 84

Alation

APRIL 14, 2022

Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Problems arise when data sources are semantically incompatible.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

SEPTEMBER 28, 2020

The customer had shorter upgrade windows, which dictated the need to do an in-place upgrade by installing CDP on their existing environment, instead of a longer migration path that required data migrations, operation overhead to setup news clusters and time needed to procure and setup new hardware. using the steps documented here.

Testing 131

Testing Metadata Risk Data Science 131

Cloudera

AUGUST 9, 2022

Today we announce the official release of the CDP Disaster Recovery Reference Architecture (DRRA). The DRRA focuses on describing how to think about reliability, resiliency, and recovery for the Cloudera Data Platform, and is a living document describing our collected learning across the platform and across customers. .

Data Lake 77

Data Lake Data Warehouse Data-driven IoT 77

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

IBM Big Data Hub

JULY 7, 2023

When knowledge workers leave the company, businesses face another challenge—finding a way to document, share and retain their knowledge to extend its benefits throughout the company. While Big Data and artificial intelligence (AI) provide the numbers, knowledge workers are key to understanding them.

Cost-Benefit 66

Cost-Benefit Consulting Management Data-driven 66

AWS Big Data

NOVEMBER 9, 2023

This popular open-source tool for data warehouse transformations won out over other ETL tools for several reasons. The tool also offered desirable out-of-the-box features like data lineage, documentation, and unit testing. It’s raw, unprocessed data straight from the source.

Data Warehouse 107

Data Warehouse Testing Data Quality Reporting 107

TDAN

AUGUST 3, 2021

Database standards are common practices and procedures that are documented and […]. Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard.

Data Quality 116

Data Quality Data Architecture Modeling Management 116

erwin

SEPTEMBER 19, 2019

Click here to download our latest, best practice guide for Data Modeling for free. Historically, little attention has focused on what can literally make or break any data governance initiative — turning it from a launchpad for competitive advantage to a recipe for disaster. Passing the Data Governance Ball.

Data Governance 108

Data Governance Cost-Benefit Enterprise Modeling 108

BI-Survey

AUGUST 21, 2020

They focus less on data marts, tend to extend the data warehouse with a data lake and utilize analytic databases and real-time processing more frequently. They are opting for cloud data services more frequently. For example, varied data types are handled with new data modeling approaches supported by appropriate tools.

Data Warehouse 98

Data Warehouse Data Lake Data Governance Data Architecture 98

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. The reality of LLMs and other “narrow” AI technologies is that none of them is turn-key.

Insurance 81

Insurance Risk Data-driven Finance 81

Sisense

JULY 6, 2020

Its main purpose is to establish an enterprise data management strategy. That includes the creation of fundamental documents that define policies, procedures, roles, tasks, and responsibilities throughout the organization. These regulations, ultimately, ensure key business values: data consistency, quality, and trustworthiness.

Enterprise 98

Enterprise Management Data Architecture Strategy 98

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

Ontotext

FEBRUARY 26, 2024

However, it still requires more effort than building a single-purpose graph, like a labeled property graph (LPG), where you can whiteboard your graph model in an hour. Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. RDF vs LPG?

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

AWS Big Data

JANUARY 22, 2024

Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling. All other teams can be data producers or data consumers.

Data-driven 87

Data-driven Advertising Metadata Data Architecture 87

TDAN

AUGUST 18, 2020

Consistent business meaning is important because distinctions between business terms are not typically well defined or documented. The business glossary is simple in concept, but it can be a challenge to structure, define and maintain shared business terminology. What are the standards for writing […].

Management 96

Management Metadata Data Architecture IT 96

AWS Big Data

APRIL 19, 2023

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

Technology 85

Technology Data-driven Machine Learning Sales 85

CIO Business Intelligence

MARCH 28, 2022

Associate Principal Analyst Nolan Hart calls the proper EA scope “the least number of deliverables, such as viewpoints, reference models and design patterns, that help ensure timely, compliant delivery of products and solutions.”

Enterprise 102

Enterprise Measurement Reporting Cost-Benefit 102

IBM Big Data Hub

FEBRUARY 9, 2023

Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes. Start by identifying business objectives, desired outcomes, key stakeholders, and the data needed to deliver these objectives. Don’t try to do everything at once!

Data Governance 77

Data Governance Business Objectives Strategy Data Quality 77

Sisense

AUGUST 28, 2019

As markets consolidate and acquisitions are made, incorporating multiple data architectures shouldn’t necessitate the consolidation of new data sources and data models with a single cloud vendor. Conclusion.

Analytics 73

Analytics Data Strategy Strategy Data Architecture 73