1. Data Analytics Life Cycle
April 9, 2016
Team3
Amir Ataee
Professor Chuck Tappert, Ph.D.
Data Analytics Life Cycle
Brief overview of Big Data Analytics

2. Data Analytics Life Cycle
Big Data is defined as “extremely large data sets that have grown beyond the ability to manage and analyze them with traditional data processing tools
Dealing with big data has several problems such as acquirement, storage, searching, sharing, analytics, and visualization of data
To overcome these issues, we need a process which facilitated the analytical process of the Big Data.
For this purpose, the data analytics life cycle process was designed

3. Data Analytics Life Cycle- Continue
This life cycle, has 6 phases but they are not have to be in serial order
At any time, one or more phases can happen at the same time
most of these phases can either go forward or backward depend on what additional as new information is available
Model
Building
Data Prep
Planning
Operationalize
Communicate
Results 1 2 3 4 5 6
Discovery

4. Phase 1: Discovery The team learns the business domain
The team assesses the resources available to support the project
The team formulating initial hypotheses (IHs) to test and begin learning the data.

5. Phase 2: Data Preparation
It requires the presence of an analytic sandbox, in which the team can work with data
The team needs to execute extract, load, and transform (ELT)
The team needs to familiarize itself with the data thoroughly and take steps to condition the data

6. 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

7. Phase 4: Model Building
The team develops datasets for testing and production purposes
The team builds and executes models based on the work done in the model planning phase

8. Phase 5: Communicate Results
The team, in collaboration with major stakeholders, determines if the results of the project are a success or a failure
The team should identify key findings, quantify the business value, and develop a narrative to summarize and convey findings to stakeholders.

9. Phase 6: Operationalize
The team delivers final reports, briefings, code, and technical documents.
The team may run a pilot project to implement the models in a production environment.

10. Phase 6: Operationalize
Stakeholders
Expectation of the project
Business User
The benefits and implications of the findings to the business
Project Sponsor
Risks and returns of investment
Project Manager
To meet the constraints of the project
BI Analysts
To evaluate if the reports and dashboards is valid and complete
Database Admin
Needs to have technical document on how to implement changes
Data Scientist
Needs to share the code and explain the model to others

11. AMIR’s references
Big Data Analytics: Turning Big Data into Big Money , Frank Ohlhorst John Wiley & Sons, 2013
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Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data , EMC Education Services, John Wiley & Sons, 2015

12. Questions?