AWS Public Sector Blog

Building a data analytics practice across the data lifecycle

Data is an organization’s most valuable asset, and the volume and variety of data that organizations amass is always growing. Simpler data analytics, cheaper data storage, advanced predictive tools like machine learning (ML) and data visualization are necessary to make data-driven decisions and maximize the value of data. The new Data Lifecycle and Analytics in the AWS Cloud [Download here] guide helps organizations of all sizes establish and optimize a modern data analytics practice in their organization.

What is the data lifecycle?

As data is generated, it moves from its raw form to a processed version, to outputs that end users need to make better decisions. The five data lifecycle stages include data ingestion, data staging, data cleansing, data analytics and visualization, and data archiving. All data goes through this data lifecycle. Organizations can use AWS Cloud services in each stage of the data lifecycle to quickly and cost-effectively prepare, process, analyze, and present data in order to derive more value from it.

Data lifecycle stages - AWS infographic

Who is the reference guide for?

This guide was written primarily for information technology (IT) professionals as well as for chief data officers, data scientists, data analysts, and data engineers. Technical professionals can learn how to extract more value from their data by taking advantage of the AWS Cloud to support data-driven decision-making. Business leaders and managers will also benefit from the guide’s overview and customer case studies.

If you’re overwhelmed reading about all of the services available to turn data into insights, this reference guide can help. To simplify the learning process, the guide aggregates important definitions, workflows, and the relevant AWS services for each stage of the workflow. This guide will help you answer data lifecycle and management questions including:

  • How do you collect and analyze high-velocity data across a variety of data types – structured, unstructured, and semi-structured?
  • How do you scale up IT resources to run thousands of concurrent queries against your data – and then scale back down automatically to lower costs?
  • How do you analyze your data across platforms, so users can view, search, and run queries on multiple data repositories?
  • How do you cost-effectively store petabytes of data and share them on-demand with users around the world?
  • How do you get your data to answer questions about past scenarios and patterns, while predicting future events?

How is AWS Cloud relevant for data analytics?

Data within organizations – measured in petabytes – grows exponentially each year. IT teams are under pressure to quickly coordinate data storage, analytics, and visualization projects that get the most from their organizations’ data while also ensuring customer privacy and meeting security and compliance mandates. These challenges are addressed cost-effectively with cloud-based IT resources, as an alternative to fixed, conventional IT infrastructure (e.g. owned data centers and computing hardware managed by internal IT departments).

By modernizing their approach to data lifecycle management and leveraging the latest cloud-native analytics tools, organizations reduce costs and gain operational efficiencies, while enabling data-driven decisions.

AWS offers a complete cloud platform designed for big and small data across data lakes, databases, data warehousing, distributed analytics, real-time streaming, machine learning, and business intelligence services. These cloud-based IT infrastructure building blocks – along with AWS Cloud capabilities that meet the strictest security requirements – can help address a wide range of data and analytics challenges.

Learn more about data lifecycle and analytics by downloading the Data Lifecycle and Analytics in the AWS Cloud guide.