Geolocation — A drop in the ocean of Data
Modern data has evolved way past Shannon’s thesis of implementing Boolean algebra into electrical circuits, which formed the basis of modern-day communication. As we stand on the precipice of digital transformation and cloud computing that drives our always-on über-connected lifestyles, data is not just a binary code of 0s and 1s, which tells a machine what to do; it governs lives.
Hence, it surely warrants us to understand different data types that intertwine our daily lives to know how it impacts us.
1 — BIG Data —
A core favourite, big data, has arisen to be defined as the behemoth of raw data that will not fit into a standard database for analysis and processing due to the vast volumes of information created by human and machine-generated processes.
Big data is the engine that drives things like machine learning and forms the building blocks of artificial intelligence (AI).
By digging into (and analyzing) big data, data scientists can discover patterns to understand better why things happened. They can also use AI to predict the future and prescribe strategic directions based on these insights.
2 — Time-stamped Data
Time-stamped data capture data points in either captured (event time) or collected (processing time) format.
This type of data typically collects behavioural data (for example, user actions on a website), which accurately represents actions over time. Having a dataset such as this is precious to data scientists working on systems tasked with predicting the next best action style models.
3 — Machine Data — A digital exhaust
What is one of the first things you do in the morning when you wake up?
If your go-to response was — Check my phone! You are not alone.
With the increased digital integration, we are generating machine data at an unprecedented rate, from driving to the office in your Bluetooth & wifi enabled car, logging on to your computer, making phone calls, responding to emails, accessing applications checking your sleep cycle on the cool widget in your hand. We are creating a wealth of machine data in an array of unpredictable formats.
Simply put, the digital exhaust of all machines that power modern businesses worldwide creates machine data.
It is valuable because it contains a real-time record of all the activity and behaviour of customers, users, transactions, applications, servers, networks and electronic devices.
4 — Spatial-Temporal Data
Spatial-temporal data describes both location and time for the said event, such as when you go and buy a sandwich in your favourite café — and it can show us how phenomena in a physical location change over time.
Decision-makers can also run backend calculations to find summary statistics on events contained within specified locations.
5 — Real-time Data
One of the most critical trends in analytics is the ability to stream and act around real-time data. Even though real-time data is slightly behind the actual passage of time in the real world, with 5G gaining momentum, the response time will become instantaneous or as fast as a human can perceive.
Real-time data can also provide a link between consumers and brands, allowing relevant offers to be delivered at precise moments based upon location, needs and personal preferences.
6 — Biometrically Inferred Data
This data is a collection of datasets resulting from physical, psychological, behavioural and other nonverbal communication methods.
Some examples of biometric identification techniques that potentially contribute to BID are Facial recognition, Fingerprint verification, Iris scanning, Retinal analysis, Voice recognition, Ear shape recognition, Keystroke analysis, Handwritten signature analysis, Gait analysis and the like.
An organization can make possible inferences from these data sets: your age, potential health conditions, mental health, skills, cultural background, mental workload, and much more.
The above list is by no means meant to be exhaustive, but it does give an insight into the wealth of data available for organizations to utilize. Hence we need to start taking a holistic approach towards Data and ensure that data is decentralized for the greater good.