Towards AI

OCTOBER 31, 2024

Last Updated on October 31, 2024 by Editorial Team Author(s): Jonas Dieckmann Originally published on Towards AI. Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities.

Data Quality 122

Data Quality Analytics Analytics Clean Data 122

Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.

Analytics 72

Analytics Analytics AI AI 72

Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.

Analytics 59

Analytics Analytics AI AI 59

Pickl AI

FEBRUARY 6, 2025

Overcoming challenges like data quality and bias improves accuracy, helping businesses and researchers make data-driven choices with confidence. Introduction Data Analysis and interpretation are key steps in understanding and making sense of data. Challenges like poor data quality and bias can impact accuracy.

Data Analysis 52

Data Analysis Data Analysis Data Quality Power BI 52

Pickl AI

DECEMBER 16, 2024

This capability is essential for businesses aiming to make informed decisions in an increasingly data-driven world. In 2024, the global Time Series Forecasting market was valued at approximately USD 214.6 billion in 2024 and is projected to reach a mark of USD 1339.1 billion by 2030. databases, APIs, CSV files).

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

DECEMBER 3, 2024

It is projected to grow at a CAGR of 34.20% in the forecast period (2024-2031). Pandas are widely use for handling missing data and cleaning data frames, while Scikit-learn provides tools for normalisation and encoding. NumPy and SciPy can also help apply statistical methods for data imputation and feature transformation.

Machine Learning 52

Machine Learning Machine Learning Clustering Supervised Learning 52

phData

NOVEMBER 13, 2023

Read more about the dbt Explorer: Explore your dbt projects dbt Semantic Layer: Relaunch The dbt Semantic Layer is an innovative approach to solving the common data consistency and trust challenges.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52