Data Collection Data Integration Data Quality Modeling 
O'Reilly on Data
JUNE 18, 2019
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. The problem is even more magnified in the case of structured enterprise data.
AWS Big Data
MARCH 26, 2024
A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. This is aligned to the five pillars we discuss in this post.
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CIO Business Intelligence
MARCH 24, 2023
It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”
CIO Business Intelligence
JANUARY 24, 2024
“Establishing data governance rules helps organizations comply with these regulations, reducing the risk of legal and financial penalties. Clear governance rules can also help ensure data quality by defining standards for data collection, storage, and formatting, which can improve the accuracy and reliability of your analysis.”
CIO Business Intelligence
APRIL 29, 2022
In this new era the role of humans in the development process also changes as they morph from being software programmers to becoming ‘data producers’ and ‘data curators’ – tasked with ensuring the quality of the input. Many organisations focus too heavily on fine tuning their computational models in their pursuit of ‘quick-wins.’
O'Reilly on Data
DECEMBER 19, 2018
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.
Jet Global
MAY 9, 2024
Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.
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