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Lesson 3 — Two Process Views and Strategy

Chapter 14 contains two compatible process views. Do not force them into an identical list.

Nine management activities

  1. Define Big Data Strategy & Business Needs
  2. Establish Big Data Environments
  3. Choose Data Sources
  4. Acquire & Ingest Data Sources
  5. Develop Hypotheses & Methods
  6. Integrate / Align Data for Analysis
  7. Explore Data Using Models
  8. Communicate Output to Stakeholders
  9. Deploy and Monitor

Seven-step Data Science loop

  1. Define strategy/business need
  2. Choose sources
  3. Acquire/ingest
  4. Develop hypotheses/methods
  5. Integrate/align
  6. Explore/model
  7. Deploy/monitor

The broader management view makes environment establishment and stakeholder communication explicit. The seven-step figure focuses the iterative model-development loop.

Strategy before platform

The first formal activity is not “buy a lake” or “pick an algorithm.” It is to define the problem, expected value, data needs, required timing, governance, and roadmap.

Useful strategy questions: - What problem are we solving? - What decision changes if the analysis works? - Which sources are credible and relevant? - What latency is actually actionable? - What is the impact on existing data structures and core subjects? - What benefit justifies the cost of acquisition, processing, and operations?

Changed fact drill

A vendor proposes real-time architecture because it is technically possible. If the business can act only once daily, the need for extreme low latency disappears. The correct response is to reassess cost/benefit and architecture fit, not automatically select the fastest design.

Source: pp. 473–476, 484–485, 495–497.

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