Dataconomy

DECEMBER 17, 2024

Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictive analytics in the first phase. What are the data quality expectations? Tools to use: Data dictionaries : Document metadata about datasets.

Data Quality 91

Data Quality Power BI Data Engineering Data Engineer 91

Smart Data Collective

OCTOBER 1, 2019

Other uses extend to student support, which for example, makes recommendations on courses and career paths based on how students with similar data profiles performed in the past. AI systems allow for the analysis of more granular patterns of the student’s data profile. Perils of Depending on AI in Higher Education.

Artificial Intelligence 109

Artificial Intelligence Artificial Intelligence Big Data Big Data 109

Precisely

FEBRUARY 20, 2025

If you cant use predictive analytics and make quick, confident data-driven decisions, you risk falling behind to your competitors that can. Solution: Ensure real-time insights and predictive analytics are both accurate and actionable with data integration.

Data Silos 69

Data Silos AI AI Data Quality 69

Pickl AI

OCTOBER 18, 2023

– Predictive analytics to assess data quality issues before they become critical. Data Cleansing and Standardization – Automated data cleansing using AI algorithms to correct errors, remove duplicates, and standardize formats. – Natural Language Processing (NLP) for text data standardization. .

Data Quality 52

Data Quality ML ML Machine Learning 52