Data Management Summit Madrid

October 20-21, 2021 | Spain

Data Management Summit Madrid, A fundamental summit for CIO, CTO, CDO, system directors, data scientists who implement emerging technologies to solve new technological challenges and align with new business opportunities. The summit will immerse attendees in the latest advances in data and analysis, focusing on emerging technologies that will offer the greatest opportunities for impact within your organization. Data management professionals are involved in a wide range of activities designed to ensure that a company benefits from its investment in its data, including enterprise architecture, data modeling, data definition, data quality, data management, and metadata management.

Spotlight

Providing a full end-to-end solution requires not only evaluating a dozen or so technologies for each tier, but also looking at their many permutations. And at each juncture, we must remember we’re not just building technology for technology’s sake we’re hoping to provide analytics (often in near real time) that drive insight, action, and better decision making. Making decisions based on “data, not opinions” is the end game, and the technologies we choose must always be focused on that. It’s a dizzying task. Only by looking at the past, present, and future of the technologies can we have any hope of providing a realistic view of the challenges and possible solutions. This article is meant to be both an exploration of how the analytics ecosystem has evolved into what it is, as well as a glimpse into the future of both analytics and the systems behind it. By starting from both ends, we hope to arrive in the middle the present with a pragmatic list of requirements for an analytics stack that will deal with anything you can throw at it today and tomorrow. Before we can look at the requirements for a frontend analytics tool, however, it’s paramount that we look at the reality of the backend.


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Spotlight

Providing a full end-to-end solution requires not only evaluating a dozen or so technologies for each tier, but also looking at their many permutations. And at each juncture, we must remember we’re not just building technology for technology’s sake we’re hoping to provide analytics (often in near real time) that drive insight, action, and better decision making. Making decisions based on “data, not opinions” is the end game, and the technologies we choose must always be focused on that. It’s a dizzying task. Only by looking at the past, present, and future of the technologies can we have any hope of providing a realistic view of the challenges and possible solutions. This article is meant to be both an exploration of how the analytics ecosystem has evolved into what it is, as well as a glimpse into the future of both analytics and the systems behind it. By starting from both ends, we hope to arrive in the middle the present with a pragmatic list of requirements for an analytics stack that will deal with anything you can throw at it today and tomorrow. Before we can look at the requirements for a frontend analytics tool, however, it’s paramount that we look at the reality of the backend.

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