Different types and structures of data exist within an organization. Here to go:
Master Data -Enterprise-level data entities that are of strategic value to an organization. These are typically non-volatile and non-transactional in nature. Typically example of such data are Customer, product, supplier, assets, System Name
Transaction Data-These are Business transactions that are captured during business operations and processes, such examples are PO, Invoices, PR , Payments etc .
Therefore, Data is intrinsically simple and can be divided into data that identifies and describes things, master data, and data that describes events, transaction data.
Reference Data- There are Internally managed or externally sourced facts to support an organization’s ability to effectively process transactions, manage master data, and provide decision support capabilities.
Typical example are as such :
Cities within States/Provinces/Territories
Street names within cities
Street types (Dr, Lane, Boulevard...)
Street direction (N, NE, E, S...)
Blocks & Block Groups
Postal codes within states
Months of the year
Unstructured Data—This is data found in e-mail, white papers like this, magazine articles, corporate intranet portals, product specifications, marketing collateral, and PDF files.
Metadata -This is defined as “data about the data.” These data are Typically Used as an abstraction layer for standardized descriptions and operations.
Analytical Data -These data are derivations of the business operation and transaction data used to satisfy reporting and analytical needs. In the organziation such data typically reside in data warehouses, data marts, and other decision support applications.
Big Data -Data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.
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