IBM Big Data Hub

AUGUST 15, 2022

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. Model risk management.

Data mining 60

Data mining Risk Modeling Risk Management 60

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.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. A DataOps implementation project consists of three steps.

Testing 152

Testing Metadata Dashboards Statistics 152

CIO Business Intelligence

AUGUST 9, 2022

Data engineers and data scientists often work closely together but serve very different functions. Data engineers and data scientists often work closely together but serve very different functions. Data engineers are responsible for developing, testing, and maintaining data pipelines and data architectures.

Analytics 119

Analytics Data Science Statistics Unstructured Data 119

Octopai

APRIL 19, 2021

Her Twitter page is filled with interesting articles, webinars, reports, and current news surrounding data management. She tweets and retweets about topics such as data governance, data strategy, and data architecture. TDAN stands for The Data Administration Newsletter. TDWI – David Loshin. Dataconomy.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

Peter James Thomas

AUGUST 18, 2018

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63