Variety - Data Structure and
Types
“The goal is to turn data into information,
And information into insights”
-Carly Fiorina (Ex CEO of Hewlett-Packard)
What is Data Variety?
Data Variety refers
to the diversity of data collection or issue area, along with data volume,
velocity, and veracity it is regarded as a critical feature of data complexity. The big data analytics market is set to reach $103 billion by 2023. Poor data costs
the US economy up to $3.1 trillion yearly. In 2021, every person will generate 1.7 megabytes in
just a second. Internet users generate about 2.5 quintillion bytes of
data each day. (25+
Big Data Statistics - How Big It Actually Is in 2021, 2021)(25+ Big Data Statistics - How Big It Actually Is in 2021?, 2021)
The following are some typical feature examples of data variety-
1. Structure
Data: A wide range of data models and data kinds are available, for
example a company may have hundreds of databases, each with its own data model.
2. Unstructured
Data: Is the data which are not structure in a manner which machine
can read.
3.
Natural Language: This
means how a natural language can change things if not mentioned properly can
change the meaning of the sentence and makes difficult for machine to
understand things, for example some words or sentence is positive, but machine
might view as a sarcastic comment and makes the meaning negative.
4.
Media: There
are extreme variety in media such as audio, photos, videos, and GIF etc.
5. Complex Systems:There
are observations for complex systems. For example, there is a self-driving car
where the destination is the city center, they should deal with the great
variety of things that might occur in between.
Let
us have a look what are the 5V’s of Big Data:
1. Volume:
The volume of data refers to the
size of the data sets that must be evaluated and processed, which are today
regularly larger than terabytes and petabytes. The sheer volume of data
necessitates processing technologies that are distinct and distinct from
traditional storage and processing capabilities. In other words, the data sets
in Big Data are too large to be processed on a standard laptop or a personal
computer processor All credit card transactions in Europe on a given day are an
example of a high-volume data set. If we take Starbucks, looking into the
considerations Starbucks have immensely grown in the last few decades, this video
explains about how data has played an important role for Starbucks to grow from
1970 when the coffee café was initiated to 32660 in 2021. Starbucks solely acquiring
57% of the coffee market. This video by CNBC was published in the year 2019, January
10th, talks about how volume plays in important role in today’s
world to establish.
2. Velocity:
The rate at which data is
generated is referred to as velocity. High velocity data is generated at such a
rapid rate that it necessitates distinct (distributed) processing techniques.
Twitter messages or Facebook posts are two examples of high-velocity data. For instance,
if we take an example of Coca-Cola how it experiments which allowed customers
to make their own flavored drinks which helps in gaining customer attraction and
helped Coco-Cola to make decisions on real data. This video was published by
Bernard Marr in the year 2019, March 11th talks more about how data
playing important role in market research.
3. Variety:
Big Data is truly massive due to
its diversity. Big Data comes from a wide range of sources and is generally
classified as one of three types: structured, semi-structured, or unstructured
data. The variety of data types frequently necessitates specialized processing
capabilities and algorithms. CCTV audio and video files generated at various
locations are an example of a high variety data set. This video was published
in the year 2019, March 25th by SAP Analytics gives more insights
about how digitization helps the beverage industry how data is processed in different
region across the globe and how big data plays crucial role in market analysis.
4. Veracity:
The quality of the data being analysed
is referred to as its veracity. High-quality data contains many records that
are useful for analysis and contribute significantly to the overall results.
Data with low veracity, on the other hand, contains a high percentage of
meaningless data. Noise refers to the non-valuable in these data sets. Data
from medical experiment would be an example of a high veracity data set. Lights on the data published
this video on 21st November 2019 to explain the importance and
impact of data veracity in market research and gives a in depth insights.
5. Value:
A data value domain is the
definition of an explicit set of restrictions on a set of values within a data
type, and it is extremely useful for data validation. A set of semantic rules
is an addition to the restrictions placed on the set of valid values for an
attribute, which are expressed as a subset of the allowed structural values. Multi-view
publishes this video on January 29th, 2020, sharing the in-depth detail on the importance of data
value.
Conclusion :
There are several Multi National Companies who does data utilization from the available data in the market. With the types of data variety it becomes easy to classify data and structure them
Author- Ritika Tiwari
[14th June 2021]
Focus keyword: Data types, Data Variety, Data analysis, Big Data, Beverages Industry, Data utilization in beverage industry, 5V of data
#beverages #datatypes #5vofdata #dataanalysis #datavelocity #datavolume #datavariety #dataveracity #datavalue
References:
Spacey, J., 2021. 6 Examples of Data Variety. [online] Simplicable. Available at:
<https://simplicable.com/new/data-variety> [Accessed 12 June 2021].
Framework, B., 2021. The Four V's of Big Data |
Enterprise Big Data Framework©. [online] Enterprise Big Data Framework©.
Available at: <https://www.bigdataframework.org/four-vs-of-big-data/>
[Accessed 12 June 2021].
Spacey, J., 2021. 6 Examples of Data Variety. [online] Simplicable. Available at:
<https://simplicable.com/new/data-variety> [Accessed 12 June 2021].
Nice info!! Keep it up
ReplyDeleteWas helpful
ReplyDeleteNice
ReplyDeleteWonderful explanation. Thank you so much!
ReplyDeleteGood info
ReplyDeleteGood information. Easy and simple to understand.
ReplyDeleteVery insightful content
ReplyDeleteInteresting
ReplyDeleteIt’s very interested see how data is important to all type of business, especially in beverage industry it’s possible to how business can successfully grow if strategy and decisions are constantly based on data analysis. Variety has made big data analytics accessible to all business. There is no doubt that this technology is already revolutionize the beverage industry and the way of business drive decision. The Starbucks cases is a good way to realized business moves after analysis of data. From the CEO level to the route supplier level for completing orders at retail outlets, data analysis has become a fundamental element of business decision-making.
ReplyDeleteThis comment has been removed by the author.
ReplyDeleteThat was a great article to read about the 5V's of Big Data and how the companies are using it to structure and analyze their data. In addition to the authors piece of words, would like to share about the worlds largest beverage company "Coca Cola" which has over the years has embraced 5 V's of Big Data to drive its strategic decisions. According to a Forbes article "Outside of the tech sector, Coca-Cola was one of the first widely renowned brands to adopt Big Data. “Social media, mobile applications, cloud computing, and e-commerce are combining to give companies like Coca-Cola an unprecedented toolset to change the way they approach IT,” said Esat Sezer, the company's chief big data officer, in 2012. Big data, at the heart of it all, provides the intelligence to bring it all together.”
ReplyDeleteSo its grand to see that companies like Coca-Cola is counting on Big Data Variety to stay relevant in an increasingly dynamic environment with changing customer behavior.
- Mohit Jain
Very well explained. As my understanding, the most important aspect of Data Variety is 360-degree consumer view and improved business intelligence.
ReplyDeleteAs technology becomes more pervasive, our digital footprints become more visible. The digital trail continues to develop, from clicks and views on websites and mobile apps to sensor data obtained from real-world and virtual systems. Organizations that effectively use large data sets learn more about their customers, users, patients, and citizens, and then apply that knowledge to meet individual requirements. Advanced analytics software and dashboards powered by big data provide a more complete view of customer interactions and behaviors; many enterprises are combining data from various internal and external sources to improve customer service, increase sales, optimize marketing, improve products and services, and generally inject more real intelligence into their operations.
Exactly
ReplyDeleteStructured data is data that follows a pre-determined data model and is thus easy to analyze. Structured data is organized in a table with relationships between the rows and columns. Excel spreadsheets and SQL databases are common examples of structured data. There are organized rows and columns in each of these that can be sorted.
A data model - a concept of how data can be stored, processed, and accessed – is required for structured data to exist. Each field is discrete and can be accessed independently or in conjunction with data from other fields thanks to a data model. Structured data is highly powerful since it allows you to swiftly gather data from many locations.