Data Governance and Architecture – Making the connections

Data Governance is one of the hottest topics in data management, focusing both on how Governance driven change can enable companies to gain better leverage from their data and also to help them design and enforce the controls needed to ensure they remain compliant with regulations. Despite this rapidly growing focus, many Data Governance initiatives fail to meet their goals. This session will outline why Data Governance and Architecture should be connected, how to make it happen, and what part Business Intelligence and Data Warehousing will play in defining a robust and sustainable Governance programme.

Data Preparation for Machine Learning: Why Feature Engineering Remains a Human-Driven Activity

In Data preparation it is important how the human modeler creates a dataset that is uniquely suited to the business problem. In this session Keith McCormick will expose analytic practitioners, data scientists, and those looking to get started in predictive analytics to the critical importance of properly preparing data in advance of model building. He will present the critical role of feature engineering and explaining how to do it effectively.

Managing and exploring data using a data lake and an analytics lab (Dutch spoken)

ASML, manufacturer of machines for the production of semiconductors, is implementing a central data lake to capture this data and make it accessible for reporting and analytics in a central environment. The data lake environment also includes an analytics lab for detailed exploration of data. In this session Jeroen Vermunt presents real-life examples of how ASML approaches the challenges of managing rapidly changing data.

Modern Data Management & Data Integration (Dutch spoken)

The digital future: think big, think highly distributed data, think in ecosystems.
What Integration Architecture is needed to play an important role in the digital world with eco-system with FinTech company’s and other banks? Do Enterprise Data Warehouses still have a role in this landscape?

Cloud Data Warehousing: Planning for Data Warehouse Migration

Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema or rebuilding of data pipelines is needed. In this session Dave Wells provides an overview of the benefits, techniques, and challenges when migrating an existing data warehouse to the cloud.

Cloud Data Warehousing

What are the benefits, techniques, and challenges of migrating an existing data warehouse to the cloud? Highly participative workshop by Dave Wells.

Putting Machine Learning to Work

Hands-on workshop with Keith McCormick on applying supervised and unsupervised learning. How do you convert business challenges into effective machine learning models? Which techniques do you need to apply in which situation? This workshop is highly participative and contains real world examples.

Taking data management automation to the next level

Following up on its successful predecessor we are happy to announce the release of Quipu 4.0. We’re taking things a step further by introducing the next level in data management automation using patterns as guiding principle.