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Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services

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12

The Endgame, or Putting It All Together

Key Concepts

Communicating and operationalizing an analytics project

Creating the final deliverables

Using a core set of material for different audiences

Comparing main focus areas for sponsors and analysts

Understanding simple data visualization principles

Cleaning up a chart or visualization

This chapter focuses on the final phase of the Data Analytics Lifecycle: operationalize. In this phase, the project team delivers final reports, code, and technical documentation. At the conclusion of this phase, the team generally attempts to set up a pilot project and implement the developed models from Phase 4 in a production environment. As stated in Chapter 2, “Data Analytics Lifecycle,” teams can perform a technically accurate analysis, but if they cannot translate the results into a language that resonates with their audience, others will not see the value, and significant effort and resources will have been wasted. This chapter focuses on showing how to construct a clear narrative summary of the work and a framework for conveying the narrative to key stakeholders.

12.1 Communicating and Operationalizing an Analytics Project

As shown in Figure 12-1, the final phase in the Data Analytics Lifecycle focuses on operationalizing the project. In this phase, teams need to assess the benefits of the project work and set up a pilot to deploy the models in a controlled way before broadening the work and sharing it with a full enterprise or ecosystem ...

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