Dataconomy

APRIL 16, 2025

Data visualization and reporting: Tools create dashboards and visual representations that help users gain insights quickly. Analytics engines: Systems that process data and execute complex analyses, from basic queries to advanced algorithms.

Analytics 91

Analytics Analytics Data Analysis Data Analysis 91

Ocean Protocol

MARCH 8, 2023

Launched on February 1st 2023, the contestants of our Air Quality challenge were asked to use Ocean.py’s open-source tool, Compute-to-Data, to publish predictions of air pollutant concentrations in a fully decentralized manner. Contestants also submitted a report about their algorithmic approach to predictions.

Data Silos 64

Data Silos Data Scientist Algorithm Predictive Analytics 64

IBM Journey to AI blog

OCTOBER 30, 2023

Real-time data analytics helps in quick decision-making, while advanced forecasting algorithms predict product demand across diverse locations. AWS’s scalable infrastructure allows for rapid, large-scale implementation, ensuring agility and data security.

AWS 95

AWS Data Warehouse AI AI 95

Pickl AI

DECEMBER 3, 2024

Current Challenges in Data Analytics Despite the advancements in Data Analytics technologies, organisations face several challenges: Data Quality: Inconsistent or incomplete data can lead to inaccurate insights. Poor-quality data hampers decision-making and can result in significant financial losses.

Analytics 52

Analytics Analytics Data Quality AI 52

Dataconomy

JULY 19, 2023

Assess the uniqueness and viability of the AI algorithms being used, as well as their potential applications in real-world scenarios. Its privacy-preserving features make it ideal for applications that require sensitive data, such as healthcare and financial services. Data security : Data can be vulnerable to security risks.

AI 172

AI AI Artificial Intelligence Artificial Intelligence 172

phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

Data Governance 52

Data Governance Data Silos Predictive Analytics Data Wrangling 52

Precisely

FEBRUARY 12, 2024

Here are some of the key trends and challenges facing telecommunications companies today: The growth of AI and machine learning: Telecom companies use artificial intelligence and machine learning (AI/ML) for predictive analytics and network troubleshooting.

Data Governance 64

Data Governance Analytics Analytics Machine Learning 64