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Designing, operating and managing a Multi-purpose Data Lake


Jun 04 - Jun 05, 2020

By: Mike Ferguson

Data Visualisation


Jun 11 - Jun 12, 2020

By: Andy Kirk

Practical steps for developing Data Strategy and Governance


Jun 15 - Jun 16, 2020

By: Christopher Bradley

How to Revamp your Data Warehouse and Lake for Digital Business


Jun 24 - Jun 25, 2020

By: Barry Devlin

How to Revamp your BI and Analytics for AI-based Digital Business


Jun 26, 2020

By: Barry Devlin

Enterprise Data Governance & Master Data Management

Oct 08 - Oct 09, 2020

By: Mike Ferguson

Mastering the Requirements Process

Oct 12 - Oct 14, 2020

By: Suzanne Robertson

Business Analysis Agility

Oct 15 - Oct 16, 2020

By: James Robertson

Agile Data Science 2.0

Oct 19 - Oct 21, 2020

By: Russell Jurney

Software 2.0

Oct 22 - Oct 23, 2020

By: Russell Jurney

Big Data for Managers

Oct 26, 2020

By: Jesse Anderson

Real-Time Big Data Systems with Spark Streaming and Kafka

Oct 27 - Oct 28, 2020

By: Jesse Anderson

Practical Guidelines for Designing Modern Data Architectures

Nov 02 - Nov 03, 2020

By: Rick van der Lans

Free article of the month

May 2020

AI Requires Data Perfection

AI requires “perfect” data to lessen the chances of faulty AI outcomes. Perfect data is, of course, impossible. But improved data quality via good data management is achievable.

Over the past few years, artificial intelligence (AI)—also known as machine learning (ML), cognitive computing, etc.—has become the Holy Grail of almost every business. According to the hype, it will revolutionise sales with advanced segmentation for customer acquisition, retention, and up-selling. It will optimise production systems and supply chains by anticipating problems and opportunities. It will drive trucks and fly planes. It will converse meaningfully with people, replacing many support staff. It will diagnose disease and provide personalised treatments and drugs.

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