Stages of the Analytics Life Cycle

Across industries, the applied use of data to inform business decisions has become a foundational (arguably critical) element of business. The end goal is to ultimately derive actionable insights from vast amounts of data being collected but it takes a series of activities/tasks to arrive at this juncture. Here is a description of each stage of the analytics life cycle:

• Data Acquisition – This may involve scraping data, interfacing with APIs, querying relational and non-relational databases, or defining strategy in relation to what data to pursue.
• Data Cleaning/Transformation – This may involve parsing and aggregating messy, incomplete, and unstructured data sources to produce datasets that can be used in analytics and/or predictive modeling.
• Analytics – This involves statistical and machine learning-based modeling in order to understand, describe, or predict patterns in the data.
• Prescribing Actions – This involves interpreting analytical results through the lens of business priorities, and using data-driven insights to inform strategy.
• Programming/Automation – In many cases, data scientists are responsible for creating libraries and utilities to operationalize or simplify various stages of this process. Often, they will contribute production-level code for a firm’s data products.

_{*Source: Burtch Works}

Big Data Analytics Lifecycle

Big Data analysis differs from traditional data analysis primarily due to the volume, velocity, and variety of characteristics for the data being processed. This is a helpful overview I found on the high-level steps involved with acquiring, processing, analyzing, and repurposing data with the end goal of performing Big Data analysis.

_{*Source: InformIT}