Today the term big data draws a lot of attention, but behind the hype there’s a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of non-traditional, less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Decreases in the cost of both storage and compute power have made it feasible to collect this data – which would have been thrown away only a few years ago. As a result, more and more companies are looking to include non-traditional yet potentially very valuable data with their traditional enterprise data in their business intelligence analysis.
To derive real business value from big data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data types and analyze them alongside your existing data to find new insights and capitalize
on hidden relationships.
Big data typically refers to the following types of data:
- Traditional enterprise data – includes customer information from CRM systems, transactional ERP data, web store transactions, general ledger data.
- Machine-generated /sensor data – includes Call Detail Records (“CDR”), weblogs, smart meters, manufacturing sensors, equipment logs (often referred to as digital exhaust), trading systems data.
- Social data – includes customer feedback streams, micro-blogging sites like Twitter, social media platforms like Facebook
Big data Growth
The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020.