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Dimension Conventional Analytics Advanced Analytics

Data age Historical Historical, Live

Data sources Traditional such as ERP, EPM, CRM, … More, including social media and sensor

data

Data storage Data Warehouses Data Warehouses, Event Hubs, Data Lakes

Data processing Batch Batch, Streaming

Data format Structured data Structured, Semi-Structured and

Unstructured data

Data coverage Only your most critical data Possibly all of your available data

Data proximity In the data center In the data center, at the edge

Processes Well defined Exploratory

Skills Available Lacking

Hardware Mostly Specialized Mostly Commodity

Software Mostly Proprietary Mostly Open Source

Scalability Mostly Vertical Mostly Linear

Support Vendors Vendors, User Community

Time to insight Usually Longer Usually Shorter

Agility Schema-on-write Schema-on-read, Schema-on-write

Flexibility Static, Rules-based Dynamic, Patterns Identification

How Advanced Analytics complements Conventional Analytics?By @SlimBaltagi