AWS Big Data

JUNE 6, 2023

You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 72

Data Quality Data Lake Data-driven Metrics 72

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 96

Data Lake Data Warehouse Management Risk 96

datapine

APRIL 15, 2020

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. But before production, you need to develop documentation, test driven design (TDD), and implement these important steps: Actively involve key stakeholders once again.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

CIO Business Intelligence

JANUARY 30, 2024

“Always the gatekeepers of much of the data necessary for ESG reporting, CIOs are finding that companies are even more dependent on them,” says Nancy Mentesana, ESG executive director at Labrador US, a global communications firm focused on corporate disclosure documents.

Reporting 106

Reporting Data Quality Strategy Data-driven 106

IBM Big Data Hub

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

Cost-Benefit 81

Cost-Benefit Metadata Data Governance Modeling 81

Cloudera

AUGUST 4, 2021

Factors such as siloed platforms and the absence of centralized data stewardship all regularly contribute to a lack of data visibility. . Organizations are facing a data tsunami with more data being generated than ever, making it even more difficult to discover, catalog, and keep track of all this information.

Data Governance 82

Data Governance Metadata Data-driven Enterprise 82

Alation

MARCH 21, 2023

Data security The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. Otherwise, they risk a data privacy violation.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

datapine

AUGUST 29, 2022

The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz. 8) Data Smart: Using Data Science to Transform Information into Insight, by John W.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

erwin

DECEMBER 13, 2018

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices.

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

IBM Big Data Hub

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

Management 57

Management Enterprise Modeling Data Quality 57

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. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs. With automation, data quality is systemically assured.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Andrew White

MARCH 22, 2019

This ties into the failure of data governance and MDM (see first item in this list). A data hub strategy should be economical, not perfected; and a data hub does not collect data like a data warehouses or data lake does – they are very different things. Age maybe against us.

Analytics 68

Analytics Metadata Data Governance Management 68

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52