IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. After all, Alex may not be aware of all the data available to her.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Andrew White

DECEMBER 4, 2019

I argued that one vendors’ book on data quality was really about data governance; I argued that another vendors’ marketing message was totally upside down; and I argued that some approaches to achieving single source of truth were different from traditional approaches. See Salesforce acquisition of Tableau – What does it mean?

Digital Transformation 64

Digital Transformation Data Integration Data Quality Data Warehouse 64

Jet Global

MAY 1, 2020

We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. The mechanical solution is to build a data warehouse. Data quality issues. Here’s the ugly truth: Everybody has a data quality problem. Do they use data from all of your systems?

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Data Virtualization

MARCH 7, 2024

One thing is clear; if data-centric organizations want to succeed in. The post Data Management Predictions for 2024: Five Trends appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 59

Management Data Integration Strategy Data Lake 59

Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

FineReport

MAY 31, 2023

Dupont Analysis Dashboard FineReport simplifies the implementation of the Dupont analysis model, enabling an in-depth examination of an enterprise’s financial condition through Return on Investment (ROI). The Dupont analysis model comprises three key components: profit margin, asset turnover, and equity multiplier.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Data Virtualization

JANUARY 25, 2024

One thing is clear; if data-centric organizations want to succeed in 2024, The post Data Management Predictions for 2024: Five Trends appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 59

Management Data Integration Strategy Data Lake 59

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 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. As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

Data Quality 85

Data Quality Data Warehouse Data-driven Reporting 85

AWS Big Data

JULY 12, 2023

With a wide array of data sources, including transactional databases, log files, and event streams, you need a simple-to-use solution capable of efficiently ingesting and transforming large volumes of data in real time, ensuring data cleanliness, structural integrity, and data team collaboration.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

BI-Survey

APRIL 27, 2023

While most continue to struggle with data quality issues and cumbersome manual processes, best-in-class companies are making improvements with commercial automation tools. The data vault has strong adherents among best-in-class companies, even though its usage lags the alternative approaches of third-normal-form and star schema.

Modeling 52

Modeling Data Warehouse Data Quality Business Driver 52

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

CIO Business Intelligence

NOVEMBER 29, 2022

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. Layering technology on the overall data architecture introduces more complexity. For data warehouses, it can be a wide column analytical table.

Data Architecture 111

Data Architecture Data Integration IoT Strategy 111

AWS Big Data

JULY 13, 2023

To reflect the needs of their customers spread across different geographies and industries, Altron has organized their operating model across individual Operating Companies (OpCos). Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

Optimization 91

Optimization B2B Data Quality Sales 91

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. Connect physical metadata to specific data models, business terms, definitions and reusable design standards. Map data flows. Govern data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

IBM Big Data Hub

APRIL 27, 2022

The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? 11 May 2021. .

Management 80

Management Metadata Data Architecture Data Lake 80

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

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. We call this the “bad data tax.”

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

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 77

Cost-Benefit Metadata Data Governance Modeling 77

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

CIO Business Intelligence

MARCH 22, 2022

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

JANUARY 13, 2022

Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. Let’s turn our attention now to data mesh.

Data Lake 59

Data Lake Metadata Data-driven Data Governance 59

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations. Simply put?

Metadata 52

Metadata IT Metrics Data-driven 52

CIO Business Intelligence

NOVEMBER 7, 2023

Centralized reporting boosts data value For more than a decade, pediatric health system Phoenix Children’s has operated a data warehouse containing more than 120 separate data systems, providing the ability to connect data from disparate systems.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

Ontotext

AUGUST 4, 2023

Thanks to the metadata that the data fabric relies on, companies can also recognize different types of data, what is relevant, and what needs privacy controls; thereby, improving the intelligence of the whole information ecosystem. Data fabric does not replace data warehouses, data lakes, or data lakehouses.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

AWS Big Data

APRIL 25, 2023

The following factors guided our decision: Tool close to data – It was important to have the data visualization tool as close to the data as possible. At Dafiti, the entire infrastructure is on AWS, and we use Amazon Redshift as our Data Warehouse. Therefore, the reports are always shared only through the folders.

Visualization 52

Visualization IT Data-driven Reporting 52

IBM Big Data Hub

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. By recognizing data as a product, it creates greater incentive to properly manage data.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98