Big Data Demystified by David Stephenson

Author:David Stephenson [Stephenson, David]
Language: eng
Format: epub
Publisher: Pearson Education Limited
Published: 2018-01-22T16:00:00+00:00

After each scoping meeting, the analytics expert should update the corresponding project entry on the Analytics Effort document and add a proposed minimum viable product (MVP) to the project description.

The MVP is the smallest functional deliverable that can demonstrate the feasibility and usefulness of the analytics project. It should initially have very limited functionality and generally will use only a small portion of the available data. Collecting and cleaning your full data set can be a major undertaking, so focus in your MVP on a set of data that is readily available and reasonably reliable, such as data over a limited period for one geography or product.

The description should briefly describe the inputs, methodology and outputs of the MVP, the criteria for evaluating the MVP, and the resources required to complete the MVP (typically this is only the staff time required, but it might entail additional computing costs and/or third-party resources). Utilizing cloud resources should eliminate the need for hardware purchases for an MVP, and trial software licenses should substitute for licensing costs at this stage.

Feed this MVP into whichever project management framework you use in your company (e.g. scrum or Kanban). Evaluate the results of the MVP to determine the next steps for that analytics project. You may move the project through several phases before you finally deploy it. These phases might include:

several iterations on the MVP to converge on the desired result;

further manual application with limited scope;

documented and repeatable application;

deployed and governed application; and

deployed, governed and optimized application

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