A journey through time and the introduction to data mesh. Becoming a data-driven organization remains one of the top strategic goals of many companies. Data-driven means placing data at the center of all the decisions and processes that are made in the organization. Leaders understand that becoming data-driven is the only way to improve customers’ experience, through hyper-personalization and customer journey re-design, to reduce operational costs through automation and machine learning, and to understand business trends which are important for high-level strategy and market positioning. A data platform creates a prosperous environment for data to thrive. Analytical data has gone through evolutionary changes, driven by new consumption models, ranging from traditional analytics in support of business decisions to intelligent products augmented with ML. The data warehouse architecture is defined by the data movement from operational systems (SAP, Salesforce) and 1st party databases (MySQL, SQL Server) to business intelligence systems. The data warehouse is the central point in which a schema is defined (snowflake schema, star schema) and where data will be stored in dimensions and fact tables, allowing businesses to trace and follow changes in their operations and customer interactions. Data is extracted from many operational databases and sources, transformed into a universal schema represented in a multidimensional and time-variant tabular format, and loaded into the warehouse tables through a CDC (change data capture) process. It is mainly serving data analysts for reporting and analytical visualization.
