Business Intelligence Dictionary: Big Data - Definition And Sources | NSBI
Big Data term is one of the most trending topics nowadays but do we really understand what is behind? This article will clarify the big data concept, how big data is entering our world and how we can monetize data.
Big Data | Definition
The NSBI Dictionary defines Big Data as a class of data typical with its:
- (a) big volume (cannot be handled by a simple relational database),
- (b) high velocity (data is generated faster, requiring much processing power) and
- (c) great variety (often unstructured, data in various format and sources).
NSBI Tutorials aim to make Business Intelligence (BI) and Data Warehousing (DWH) attractive to non-technical people as well as to those who are now entering the field and are excited by the numerous ways data is changing our world. NSBI Tutorials are written and delivered by Nick Shopov, (BI Software Developer & DWH Consultant).
1. (Web) User Applications Big Data | Example
Most common and most trending source of Big Data are the (web) user applications. A saying goes "if you use a product for free, you are not longer an user, but rather the product itself". Our online behavior is tracked and converted to big data, then analyzed and used most commonly for marketing and advertisement purposes. Smart phone user applications are also tracking your location, messaging patterns, etc.
2. Sensor Big Data | Example
Sensors are another way to digitalize big data as it is around us. Sensors are measuring environmental conditions, recording the world through camera binary information, etc. Recently a top soccer player got a sensor installed on his shoes, so statisticians can analyze his moving patterns and apply it in the competition trainings. We may very soon see self-driven cars that are using sensors to for a feedback look to "see" what is around them on the street.
3. Machine Logs Big Data | Example
Machines may produce an overwhelming volume of data during their normal work cycle. Car manufacturing for example requires tracking of numerous indicators like temperature, color levels, hardness, etc. on each of the items later assembled in what we drive.
4. Digitalized Big Data | Example
One of the huge challenges nowadays is also digitalizing the world knowledge that now can only be observed in written paper form. Scanning a book is only the top of the iceberg. Organizations also have an impressive historical volume of accounting records in different format that may need to be digitalized.
5. Structured Big Data | Example
Last, but not least comes the regular structured big data that may come from a Data Warehouse for example. This may not be treated as a regular big data source since its low velocity (refer to the definition above) but I am to put it here since integration from various data warehouses and operational databases is always the case and imposes new challenges itself.