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 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 93

Data Governance Unstructured Data Metadata Data Lake 93

Jen Stirrup

MARCH 29, 2023

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface.

Metadata 120

Metadata Data Warehouse Data Integration Business Intelligence 120

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. How Data Lineage Is a Use Case in ML. from cognilytica.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 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

erwin

OCTOBER 8, 2020

But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy. With all these diverse metadata sources, it is difficult to understand the complicated web they form much less get a simple visual flow of data lineage and impact analysis.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

Ontotext

NOVEMBER 4, 2021

Given all the advantages of the RDF model regarding enterprise data management, one no longer has good arguments why to bother with LPG at all. These models originate from different use cases: distributed knowledge representation and open data publishing on the web vs graph analytics designed to be as easy to start with as possible.

Metadata 98

Metadata Visualization Modeling Enterprise 98

AWS Big Data

NOVEMBER 29, 2023

This includes tools to help you customize your foundation models, and new services and features to build a strong data foundation to fuel your generative AI applications. Customizing foundation models The need for data is quite obvious if you are building your own foundation models (FMs).

Data Warehouse 109

Data Warehouse Data Lake Data Integration Dashboards 109

AWS Big Data

OCTOBER 11, 2023

The availability of machine-readable files opens up new possibilities for data analytics, allowing organizations to analyze large amounts of pricing data. Using machine learning (ML) and data visualization tools, these datasets can be transformed into actionable insights that can inform decision-making.

Visualization 94

Visualization Dashboards Data-driven Gap analysis 94

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Here’s how our tool makes it work.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

AWS Big Data

SEPTEMBER 29, 2023

Analyzing XML files can help organizations gain insights into their data, allowing them to make better decisions and improve their operations. Analyzing XML files can also help in data integration, because many applications and systems use XML as a standard data format. This approach optimizes the use of your XML files.

Metadata 65

Metadata Visualization Data-driven Optimization 65

IBM Big Data Hub

MAY 9, 2022

Others have come into sharper focus relatively recently: a global effort to create new data privacy laws, a post-pandemic expectation by customers to know them individually across all touchpoints, and increased attention on any racial, gender-based, or socioeconomic bias in AI models. Multicloud data integration.

Management 57

Management Data Quality Metadata Data Integration 57

Ontotext

SEPTEMBER 15, 2020

Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. Knowledge Graphs are the Warp and Weft of a Data Fabric.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

AWS Big Data

NOVEMBER 29, 2023

Maximize value with comprehensive analytics and ML capabilities “Amazon Redshift is one of the most important tools we had in growing Jobcase as a company.” – Ajay Joshi, Distinguished Engineer, Jobcase With all your data integrated and available, you can easily build and run near real-time analytics to AI/ML/Generative AI applications.

Data Warehouse 99

Data Warehouse Data Lake Analytics Machine Learning 99

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

FEBRUARY 1, 2023

The response times for these data sources are critical to our key stakeholders. Therefore, we must take a data-driven approach to select a high-performance architecture. Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

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 66

Data Quality Data Integration Metadata Cost-Benefit 66

Andrew White

OCTOBER 22, 2021

Monetization/Link data to outcome (value pyramid) business value of data/business impact 20. Business Information Model/Arch compared to classic enterprise data model and how to relate it to catalogs and marketplaces and enterprise data models 13. Data Management Infrastructure/Data Fabric 5.

IT 52

IT Data Lake Strategy Data Science 52

Ontotext

MARCH 8, 2023

With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models. Graph-based solutions further leverage the relationships among the entities involved to create a semantically enhanced machine learning model. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

erwin

MAY 27, 2020

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.

Modeling 98

Modeling Metadata Data Governance Visualization 98

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. There are models everywhere.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

DataKitchen

SEPTEMBER 21, 2023

Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes. These applications are where the rubber meets the road and often where customers first encounter data quality issues.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. 11 May 2021. . 3 March 2022.

Management 85

Management Metadata Data Architecture Data Lake 85

IBM Big Data Hub

OCTOBER 12, 2023

Connecting AI models to a myriad of data sources across cloud and on-premises environments AI models rely on vast amounts of data for training. Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc.

Cost-Benefit 81

Cost-Benefit Metadata Data Governance Modeling 81

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

Metadata 89

Metadata Data Architecture Machine Learning Cost-Benefit 89

Alation

JANUARY 17, 2023

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

Cloudera

SEPTEMBER 6, 2018

Provide audit and data lineage information to facilitate regulatory reviews. Adapt to changes in the business and regulatory requirements by facilitating the provisioning of new data and modeling of new scenarios. SDX helps to facilitate governance over enterprise data in order to satisfy regulatory inquiries.

Machine Learning 40

Machine Learning Risk Big Data Unstructured Data 40

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. It can be used for something as visual as reducing traffic jams, to personalizing products and services, to improving the experience in multiplayer video games. We would like to talk about data visualization and its role in the big data movement.

Visualization 133

Visualization Cost-Benefit Big Data Prescriptive Analytics 133

erwin

AUGUST 1, 2023

Intro erwin ® Data Modeler 12.5 is now available and provides new collaboration capabilities, integration with the Databricks Unity Catalog and more! erwin Data Modeler 12.5 erwin Data Modeler 12.5 What can you do with erwin Data Modeler 12.5? What value does erwin Data Modeler 12.5

Modeling 52

Modeling Metadata Data Quality Data Governance 52

erwin

NOVEMBER 6, 2020

To better explain our vision for automating data governance, let’s look at some of the different aspects of how the erwin Data Intelligence Suite (erwin DI) incorporates automation. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. As a result, alternative data integration technologies (e.g.,

Data Processing 89

Data Processing Data Warehouse Enterprise Visualization 89

DataKitchen

JANUARY 23, 2023

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MARCH 14, 2024

Mastering Data: Effectively Manage Your Data Download Now How Jet Analytics Enhances Microsoft Fabric Jet Analytics from insightsoftware is a complete data preparation, automation and modeling solution that enables Microsoft Dynamics customers to accelerate Dynamics ERP-ready BI projects without requiring specialist skills.

Analytics 52

Analytics Management Reporting Enterprise 52