Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Plus, AI can also help find key insights encoded in data.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

IBM Big Data Hub

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture. Don’t try to do everything at once!

Data Governance 76

Data Governance Business Objectives Strategy Data Quality 76

DataKitchen

JANUARY 23, 2023

What is data lineage? Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. What about DataOps Observability? How does it compare?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

JANUARY 17, 2023

Chances are, you’ve heard of the term “modern data stack” before. In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? It is known to have benefits in handling data due to its robustness, speed, and scalability.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Jet Global

MAY 1, 2023

By leveraging data analysis to solve high-value business problems, they will become more efficient. This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68