Data Pipelines for Engineered Decision Intelligence Posted on : May 23 - 2022

Set up the right data pipeline for a continuous flow of new data for all your data science, artificial intelligence, ML, and decision intelligence projects.

Data science has reached its peak through automation. All the phases of a data science project — like data cleaning, model development, model comparison, model validation, and deployment — are fully automated and can be executed in minutes, which earlier would have taken months. Machine learning (ML) continuously works to tweak the model to improve predictions. It's extremely critical to set up the right data pipeline to have a continuous flow of new data for all your data science, artificial intelligence (AI), ML, and decision intelligence projects. Decision intelligence (DI) is the next major data-driven decision-making technique for disruptive innovation after data science. It is:

Futuristic – Models ML outcomes to predict social, environmental, and business impact. 

Holistic – Meaningfully integrates both managerial and behavioral perspectives.

Realistic – Models all contextual variables and real-life constraints. 

So it's more important for DI projects to have a robust data pipeline. They need a continuous inflow of the right data with the right velocity to get stored in the right container and subsequently processed correctly for model development to generate actionable insights. View More