Data Analytics
Definition
Data Analytics involves examining large datasets to uncover patterns and insights, empowering organizations to make informed, data-driven decisions.
Use Cases
- Netflix: Analyze viewing behavior to understand engagement and improve content and product decisions — Netflix has publicly described using Amazon Web Services for large-scale data processing and analytics, including running analytics workloads in the cloud and using data pipelines to transform event data for analysis. (Faster, data-driven decisions on product features and content performance using large-scale analytics.)
- The Home Depot: Enterprise analytics to support merchandising, supply chain, and customer experience reporting — The Home Depot has publicly discussed using Google Cloud BigQuery as part of its data and analytics platform to centralize and analyze large datasets for reporting and insights. (Improved access to analytics across teams and faster querying of large datasets to support operational and business decisions.)
- Adobe: Customer and product analytics to support digital experience and marketing measurement — Adobe has publicly described using Microsoft Azure, including Azure Synapse Analytics, to support data and analytics workloads and integrate data for reporting and insights. (Scalable analytics to support reporting and insight generation across large datasets.)
Provider Equivalents
- AWS: Amazon Redshift
- Azure: Azure Synapse Analytics
- GCP: BigQuery
- OCI: Oracle Autonomous Data Warehouse
Frequently Asked Questions
- What's the difference between Data Analytics and Data Science?
- Data analytics focuses on answering specific business questions using techniques like SQL queries, dashboards, and trend analysis (for example: "What caused revenue to drop last week?"). Data science often goes further by building predictive or automated models (for example: "What will revenue be next month?" or "Which customers are likely to churn?") using statistics and machine learning.
- When should I use Data Analytics?
- Use data analytics when you need to measure performance, find patterns, or make decisions based on evidence. Common triggers include: tracking KPIs (sales, retention, latency), investigating anomalies (a sudden spike in refunds), segmenting customers, optimizing marketing campaigns, and forecasting demand using historical trends.
- How much does Data Analytics cost?
- Cost depends on (1) data storage size, (2) compute used to query/process data, (3) data movement/ingestion, and (4) tooling (BI licenses, orchestration). In cloud warehouses, you typically pay for stored data plus query/compute usage (on-demand per query or provisioned capacity). Costs rise with frequent large scans, inefficient queries, high concurrency, and long data retention; they can be reduced with partitioning, clustering, materialized views, and right-sizing compute.
Category: data
Difficulty: basic
Related Terms
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