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Data Analytics Life Cycle

The document outlines the six phases of a data analytics life cycle: discovery, data preparation, model planning, model building, communicating results, and operationalization. The phases involve learning the business domain, assessing available resources, extracting and transforming data, determining analytic methods, building and testing models, communicating findings to stakeholders, and operationalizing results.

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K Anantha Krishnan
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1K views8 pages

Data Analytics Life Cycle

The document outlines the six phases of a data analytics life cycle: discovery, data preparation, model planning, model building, communicating results, and operationalization. The phases involve learning the business domain, assessing available resources, extracting and transforming data, determining analytic methods, building and testing models, communicating findings to stakeholders, and operationalizing results.

Uploaded by

K Anantha Krishnan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Data Analytics Life Cycle

Data Analytics Life Cycle


Phase 1—Discovery
• The team learns the business domain, including relevant history such
as whether the organization or business unit has attempted similar
projects in the past from which they can learn.
• The team assesses the resources available to support the project in
terms of people, technology, time, and data.
• Important activities in this phase include
• framing the business problem as an analytics challenge that can be addressed
in subsequent phases and
• formulating initial hypotheses to test and begin learning the data.
Phase 2—Data preparation
• Phase 2 requires the presence of an analytic sandbox, in which the team
can work with data and perform analytics for the duration of the project.
• The team needs to execute
• extract, load, and transform (ELT) or
• extract, transform and load (ETL) to get data into the sandbox.
• The ELT and ETL are sometimes abbreviated as ETLT.
• Data should be transformed in the ETLT process so the team can work with
it and analyze it.
• In this phase, the team also needs to familiarize itself with the data
thoroughly and take steps to condition the data
Phase 3—Model planning
• The team determines the methods, techniques, and workflow it
intends to follow for the subsequent model building phase.
• The team explores the data
• to learn about the relationships between variables and
• subsequently selects key variables and the most suitable models.
Phase 4—Model building
• The team develops datasets for testing, training, and production
purposes.
• The team builds and executes models based on the work done in the
model planning phase.
• The team also considers whether
• its existing tools will suffice for running the models, or
• if it will need a more robust environment for executing models and workflows
(for example, fast hardware and parallel processing, if applicable).
Phase 5—Communicate results
• The team, in collaboration with major stakeholders,
• determines if the results of the project are a success or a failure based on the
criteria developed in Phase 1.
• The team should
• identify key findings
• quantify the business value
• develop a narrative to summarize
• convey findings to stakeholders.
Phase 6—Operationalize
• The team delivers final reports, briefings, code, and technical
documents.
• Also, the team may run a pilot project to implement the models in a
production environment.

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