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Data Analytics
// Data Analytics
01.
Data Discovery and Formation
This phase is all about defining the data’s purpose and how to achieve it by the end of the data analytics lifecycle.
02.
Data Preparation and Processing
In this phase, the experts’ focus shifts from business requirements to information requirements. One of the essential aspects of this phase is ensuring data availability for processing.
03.
Design a Model
This phase needs the availability of an analytic sandbox for the team to work with data and perform analytics throughout the project duration. The team can load data in several ways.
04.
Model Building
The team develops testing, training, and production datasets in this phase. Further, the team builds and executes models meticulously as planned during the model planning phase.
05.
Result Communication and Publication
This phase aims to determine whether the project results are a success or failure and start collaborating with significant stakeholders.
06.
Measuring of Effectiveness
In this final phase, the team presents an in-depth report with coding, briefing, key findings, and technical documents and papers to the stakeholders.
01
Machine Learning
Support and Evolution
02
Artificial Intelligence
Support and Evolution
03
Augmented Reality
Support and Evolution