Remove Data Architecture Remove Data Integration Remove Data Quality Remove Optimization
article thumbnail

Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

article thumbnail

What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

So, KGF 2023 proved to be a breath of fresh air for anyone interested in topics like data mesh and data fabric , knowledge graphs, text analysis , large language model (LLM) integrations, retrieval augmented generation (RAG), chatbots, semantic data integration , and ontology building.

article thumbnail

Salesforce certification guide: Roles, paths, exams, cost, training, requirements

CIO Business Intelligence

They handle complex tasks such as customizing the platform, configuring advanced security features, and optimizing performance while ensuring the platform aligns with company requirements and goals. The certification emphasizes testing, governance, and integration with external systems within an organization’s infrastructure.

article thumbnail

The power of remote engine execution for ETL/ELT data pipelines

IBM Big Data Hub

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. Unified, governed data can also be put to use for various analytical, operational and decision-making purposes. There are several styles of data integration.

article thumbnail

How to Pinpoint Where Your Organization Wins (and Loses) with Data

CIO Business Intelligence

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. Data and cloud strategy must align.

article thumbnail

Choosing A Graph Data Model to Best Serve Your Use Case

Ontotext

Poor data modeling capabilities of LPGs with vendor specific constructs to express semantic constraints hinders portability, expressibility, and semantic data integration. It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier.