![]() ![]() ![]() Analytics is an important catalyst for change, innovation, and transformation. Modeling an organization’s strategic vision for the future requires changing the organization in incremental, evolutionary, and revolutionary ways. I am going to consider analytical system architecture from a strategic, organizational, architectural, and technical point of view. It’s now just with more diverse personnel using more affordable, intuitive, and scalable tools than in past decades. We are still getting, preparing, modeling, analyzing, and visualizing data regardless of role (Data Analyst, Data Scientist, Data Engineer, Data Consumer) and tools (BI, AI/ML, Pipelines/Notebooks, Report/Dashboards) used. So much has changed in the last 29 years, but so much has also stayed the same. ![]() I have experienced many of these firsthand as an implementer and user since I wrote my first SQL statement back in 1993. There’s always been organizational, architectural, and technical blockers to successfully building, deploying, maintaining, and using analytical systems. Whether you have been in the arena, are new to the space, or can’t wait to get on the field, I hope this will inspire thought, discussion, debate, and action. You have personified Teddy Roosevelt’s "the person in the arena" as opposed to sitting on the sidelines and watching. Many of you may have been focused on analytics for years and have shown audacity, acumen, and determination in improving your decision-making and the decisions of those around you. ![]()
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