Large organizations collect data for various purposes. Data has always been a strategic asset of every organization, as they can leverage data for improving their business. Data can be leveraged to improve their products and services, to increase their sales, to identify potential customers, to reduce their costs and risks, to identify and prevent frauds, and the list goes on..
Large organizations relentlessly collect data so that they can process this data for business benefits. With the recent digital revolution, collecting data in digital form has become very easy. There are various ways in which data is collected, and they can be broadly classified into the following four ways:
Explicit data collection
Organizations identify the need for collecting specific information for serving specific use cases, and deploy systems so that they can collect this data. Popular methods involve building digital surveys, creating websites for interests in subscriptions, subscribing to data enrichment libraries and platforms, procuring data from external data sources etc.
Exhaust data collection
Companies often collect vital customer data as a byproduct of certain activities like payments, enquiries, servicing etc, which is originally intended to perform the activity, but the information is stored and later processed for various other business use cases. There are strict regulations around how data can be used when it is not collected for the purpose for which it is being used.
Sneaky data collection
Companies many times resort to sneaky ways of collecting data, mainly to improve their products or to understand their customers. This involves capturing customer location, contacts, communications or their journey on the website, to understand the customers and accordingly position products to them. This method is popularly criticized and debated as a breach of privacy, but still being used by almost all popular websites and mobile apps. Data privacy rules are still not formalised across the world, and until that happens, sneaky ways of collecting data will continue to prevail and flourish.
Machine generated data collection
Popular example of machine generated data is that of new generation cars, which is used for enabling self driving capabilities. With internet of things, streaming data from various machines is easily available at the disposal of large companies to be consumed to improve business processes, monitor stuff etc.