21 June 2023 —
14:50 to
15:20
The
significance of data in modern work environments cannot be overstated.
From the moment a car engine starts, a payment to a merchant is initiated, something is searched online or a customer applies for a loan, data is generated and must be effectively managed. From the beginnings of data awareness until nowadays, technology evolved and together with it, enterprise architectures increased in complexity. As a result, the manipulation of increasingly large volumes of diverse data became mandatory. Data grew into one of the most important assets of a company and the need of a Data Warehouse emerged regardless the industry the company operates in. However, the evolution of technology has led to the emergence of new platforms and architectures in the data management field. These include Cloud-based solutions, real-time data processing, self-service data analytics, data lakes, data lakehouses, data mesh and other cutting-edge technologies. Despite these trends, there are timeless DOs and DON'Ts that we have learned from our experience in implementing Data Warehouses over the past decade. These lessons will continue to apply for years to come. In this session, we will explore these key lessons learned on different dimensions such as: requirements collection, implementation methodology (phased vs big bang), data model, data integration in a source agnostic layer, golden data sources, data quality and so on.
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