Octopai

APRIL 19, 2021

BI teams will have a better handle on their data’s history, its current status, and any changes it may have undergone. Without organized metadata management, the validity of a company’s data is compromised and they won’t achieve adequate compliance, data governance, or generate correct insights. TDWI – David Loshin.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

BizAcuity

APRIL 1, 2023

Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture. Data governance and security measures are critical components of data strategy.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

BizAcuity

JANUARY 31, 2023

Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture. Data governance and security measures are critical components of data strategy.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

CIO Business Intelligence

NOVEMBER 28, 2023

Making the most of enterprise data is a top concern for IT leaders today. With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides. Quality is job one.

Unstructured Data 127

Unstructured Data Data-driven Visualization Data Quality 127

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

Grooper

AUGUST 19, 2019

Master data management. Data governance. Structured, semi-structured, and unstructured data. Data pipelines. Data science skills. Technology – i.e. data mining, predictive analytics, and statistics. Best practices for exploring collected data. Data science approaches. Simulations.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Metadata 64

Metadata Data Governance Dashboards Software 64