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

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

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

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.

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

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 and production purposes The team builds and executes models based on the work done in the model planning phase

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.

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.

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

AMIR’s references Big Data Analytics: Turning Big Data into Big Money , Frank Ohlhorst John Wiley & Sons, 2013 http://www.brainyquote.com/quotes/authors/l/lao_ tzu.html Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data , EMC Education Services, John Wiley & Sons, 2015

Questions?