What Is the Big Data Pyramid? Why Do We Need It?
The DIKW pyramid refers to a model that represents skeletal and functional relationships between data, information, knowledge, and wisdom.
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The big data analytics (BDA) market, which is valued at $10 billion today, is forecasted to grow to $40.6 billion by 2023. There is tremendous growth in the field of big data every day. The most popular ones being Internet of Things, artificial intelligence, cloud computing, and automation.
And this means only one thing.
Big data is here to stay, and whether you like it or not, it is going to permeate most aspects of human activity soon. However, before we get into the details of the changes big data is going to bring in our lives, first let us understand what is big data?
What Is Big Data and Why Do We Need It?
Big Data is a data-processing field that provides ways to study, analyze, and extract relevant information from data sets too large. We need big data because these data sets become too complex for our traditional data processing software.
The data sets could be anything from the details we fill in random online forms to the data collected by smart refrigerators at our homes. It could be as random as the promotional offers in our inbox to as specific as a GPS location.
We need big data because it helps corporations build their product better, administrators overhaul their administration, politicians amend the outdated policies, and the government upgrades their governance. In short, big data will help us generate better results with less wastage of time and resources
Principles of Big Data
Since big data is all about handling tremendous data sets, there are certain principles it works upon. These principles must be considered while diving into the intricacies of such data sets.
Volume: This refers to the magnitude of data sets
Velocity: It refers to the rate at which data is being generated.
Variety: Variation and diversity in the amount of data generated. Keep in mind, almost every data set fed into big data processing is unstructured.
Variability: Not to be confused with variety, variability refers to the discrepancies of similar types of data fields, incongruent data types, and anomalies in the data present.
Veracity: Major aspect for big data Analysis, veracity refers to the origin and reliability of data, its relevance to the outcome expected, and its context with the analysis. It tends to drop as the previous metrics increase.
Validity: Accuracy of the data along with Veracity, among the most important metrics for analysis.
Vulnerability: Susceptibility of data to privacy and security breaches.
Volatility: The data becomes obsolete soon due to the fast-paced times we live in. The volatility of data suggests the relevance of data sets held before they become obsolete.
Visualization: Probably the most complex part of big data. Visualization refers to making the unstructured, complex, volatile data comprehensible in a way it is easy to translate into actions.
This includes inferring stories and conclusions with the help of graphs and charts. it is also responsible for the insights everybody adores big data for. This is the Mjolnir to Big Data’s Thor.
Value: Like I said, big data is here to stay. Even If the ROI on big data analytics does not give results, the practitioners would all still rely on opinions and best practices.
The Big Data Pyramid (DIKW Pyramid)
The DIKW pyramid (Data Information Knowledge Wisdom) refers to a model that represents skeletal and functional relationships between data, information, knowledge, and wisdom.
Jennifer Rowley promulgated the theory of DIKW Pyramid as the wisdom hierarchy in the Journal of Communication Science, 2007 explained the pyramid with this:
“Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge”
Data alone is completely meaningless. It becomes valuable through productive analysis necessary to feed the best possible outcomes.
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