Different Types Of Data

By Author: Wisemonkeys
Total Articles: 277
Comment this article

Data are individual facts, statistics, or items of information, often numeric. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum is a single value of a single variable.

Following are the different types of data:

1 — Big data

A core favourite, big data has arisen to be defined as something like: that amount of data that will not practically fit into a standard (relational) database for analysis and processing caused by the huge volumes of information being created by human and machine-generated processes.

“While definitions of ‘big data’ may differ slightly, at the root of each are very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources and in different volumes, from terabytes to zettabytes.

2 — Structured, unstructured, semi-structured data

All data has structure of some sort. Delineating between structured and unstructured data comes down to whether the data has a pre-defined data model and whether it’s organized in a ...
... pre-defined way.

Mat Keep is senior director of products and solutions at MongoDB. Keep explains that, in the past, data structures were pretty simple and often known ahead of data model design — and so data was typically stored in the tabular row and column format of relational databases. As a result of all this polymorphism today, many software developers are looking towards more flexible alternatives to relational databases to accommodate data of any structure.

3 — Time-stamped data

Time-stamped data is a dataset which has a concept of time ordering defining the sequence that each data point was either captured (event time) or collected (processed time).

“This type of data is typically used when collecting behavioural data (for example, user actions on a website) and thus is a true representation of actions over time. Having a dataset such as this is invaluable to data scientists who are working on systems that are tasked with predicting or estimating next best action style models, or performing journey analysis as it is possible to replay a user’s steps through a system, learn from changes over time and respond,” .

4 — Machine data

Simply put, machine data is the digital exhaust created by the systems, technologies and infrastructure powering modern businesses.

“Machine data includes data from areas as varied as application programming interfaces (APIs), security endpoints, message queues, change events, cloud applications, call detail records and sensor data from industrial systems,” said Davies. “Yet machine data is valuable because it contains a definitive, real time record of all the activity and behaviour of customers, users, transactions, applications, servers, networks and mobile devices.” If made accessible and usable, machine data is argued to be able to help organizations troubleshoot problems, identify threats and use machine learning to help predict future issues.

5 — Spatiotemporal data

Spatiotemporal data describes both location and time for the same event — and it can show us how phenomena in a physical location change over time.

Temporal data contains date and time information in a time stamp. Valid Time is the time period covered in the real world. Transaction Time is the time when a fact stored in the database was known.

6 — Open data

Open data is a data that is freely available to anyone in terms of its use (the chance to apply analytics to it) and rights to republish without restrictions from copyright, patents or other mechanisms of control. The Open Data Institute states that open data is only useful if it’s shared in ways that people can actually understand. It needs to be shared in a standardized format and easily traced back to where it came from.

7 — Dark data

Dark data is digital information that is not being used and lies dormant in some form.

Analyst house Gartner Inc. describes dark data as, “Information assets that an organization collects, processes and stores in the course of its regular business activity, but generally fails to use for other purposes.”

8 — Real time data

One of the most explosive trends in analytics is the ability to stream and act around real time data. Some people argue that the term itself is something of a misnomer i.e. data can only travel as fast as the speed of communications, which isn’t faster than time itself… so, logically, even real time data is slightly behind the actual passage of time in the real world. However, we can still use the term to refer to instantaneous computing that happens about as fast as a human can perceive.

9 — Genomics data

Genomics data involves analysing the DNA of patients to identify new drugs and improve care with personalized treatments.

10 — Operational data

Colin Fernandes is product marketing director for EMEA region at Sumo Logic. Fernandes says that companies have big data, they have application logs and metrics, they have event data, and they have information from microservices applications and third parties.

The question is: how can they turn this data into business insights that decision makers and non-technical teams can use, in addition to data scientists and IT specialists?

“This is where operational analytics comes into play,” said Fernandes. “Analyzing operational data turns IT systems data into resources that employees can use in their roles. What’s important here is that we turn data from a specialist resource into assets that can be understood by everyone, from the CEO to line of business workers, whenever they have a decision to make.”

11 — High-dimensional data

High-dimensional data is a term being popularized in relation to facial recognition technologies. Due to the massively complex number of contours on a human face, we need new expressions of data that are multi-faceted enough to be able to handle computations that are capable of describing all the nuances and individualities that exist across out facial physiognomies.

12 — Unverified outdated data

This is data that has been collected, but nobody has any idea whether it’s relevant, accurate or even of the right type. We can suggest that in business terms, if you’re trusting data that you haven’t verified, then you shouldn’t be trusting any decisions that are made on its basis.

“Arguably even worse that unverified data, which may at least have some validity and which you should at least know that you shouldn’t trust, data which is out-of-date and used to be relevant..

13 — Translytic Data

An amalgam of ‘transact’ and ‘analyse’, translytic data is argued to enable on-demand real-time processing and reporting with new metrics not previously available at the point of action. Traditionally, analysis has been done on a copy of transactional data. But today, with the availability of in-memory computing, companies can perform ‘transaction window’ analytics.

At Wisemonkeys(https://wisemonkeys.info/), we are bunch of young minds trying to develop an environment to deliver knowledge to the society. From article submissions to blog writing and sharing to even question and answers. Post a question and get instant responses from experts online.

REGISTER NOW for FREE!(https://me.wisemonkeys.info/login)