Big Data and it’s impact on Business Decision Making.
Rapid development in information technology is a hallmark of the twenty-first century. Information technology has assimilated into many different industries as well as daily life.
Data is being produced at an alarming rate in this true information age. Often referred to as BIG DATA, this enormous amount of data.
Big Data is characterized as a compilation of information from numerous sources.
Think of the following:
We generate 2.5 quintillion bytes of data per day, according to IBM, which means that 90% of the data in existence today was only created in the past two years.
Example are:
Big Data analysis is being used by insurance companies to determine which house insurance applications may be handled right away and which ones require an agent visit to verify them in person.
At the federal, state, and local levels, the government is making data available so that consumers can create innovative apps that will benefit the public.
Evolution of Big Data
· Big data could be found in three forms:
STRUCTURED:
Structured data refers to any data that can be accessed, processed, and stored in a fixed format.
Structured data, for instance, can be found in a database’s “Employee” table.
EX- An ‘Employee’ table in a database is an example of Structured Data.
UNSTRUCTURED:
Unstructured data is any data whose shape or organization is unknown.
A common example of unstructured data is a large data source that combines plain text files with photos, videos, and other types of data.
‘Google Search’ output, for example.
SEMI-STRUCTURED:
Both types of data can be found in semi-structured data.
XML files, emails, etc.
DATA VOLUME:
· The volume of big data is defined by the word big itself. The amount of data available today is measured in petabytes (1015), and in the near future, it is anticipated to expand to zettabytes (1021).
· Data volume measures the total quantity of data that an organization has access to; it is not necessary for a company to hold all of this data.
· The value of various data records will decline as data volume rises in proportion to age type, richness, and quantity, among other criteria.
· The social networking sites currently in use produce data on the order of terabytes per day, which is undoubtedly too much for the traditional systems currently in use to handle.
DATA VARIETY:
· Data being produced is not of a single category as it not only includes the traditional data but also the semi-structured data from different resources like web pages, web log files, social media sites, e-mail, and documents.
· Data variety is a measure of the richness of the data representation — text, images, videos, audio, etc.
DATA VELOCITY:
· The idea of velocity in big data refers to the speed at which data from multiple sources is collected.
· This trait includes the speed at which data is gathered and processed as well as the speed at which it is received.
· The analyses that need to be done on continually changing data cannot be done by our old systems.
· The speed and depth of data used for various commercial activities (for instance, website clicks) has expanded significantly thanks to e-commerce.
· Managing data velocity involves much more than just bandwidth constraints.
DATA VARIABILITY:
• Data variability is the degree to which data points in a set of data are dispersed or clustered together.
• Data points with high variability are dispersed, whereas those with low variability are clustered.
• Variability aids in our comprehension of how dependable or erratic the data is.
• Statistics like range, variance, and standard deviation are frequently used to quantify variability.
IMPACT ON BUSINESS DECISION
Big data analysis has grown to be crucial for the industry.
Big data analytics uses predictive analysis to offer businesses specialized recommendations and suggestions that help them stand out from the competition and boost income.
Additionally, big data analytics enables companies to release new goods based on the requirements and preferences of their customers.
These elements increase revenue for firms, which is why big data analytics is being used by corporations. By integrating big data analytics, businesses may see a considerable rise in revenue of 5–20%.
Walmart, LinkedIn, Facebook, Twitter, Bank of America, and other well-known firms are some examples of those adopting big data analytics to boost their sales.
Government: Big data use and acceptance in governmental operations promotes cost, productivity, and innovation efficiencies.
Internal development: Big data offers a framework for manufacturing industry transparency, which allows for the disentanglement of concerns like variable component performance and availability.
Healthcare: By offering personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries, and dispersed point solutions, big data analytics has contributed to the improvement of healthcare.
Education: Business schools should train marketing managers to be well-versed in all the many methods employed in these subfields so they can see the broad picture and collaborate with analysts.
Media: It’s important to describe the media processing mechanism in order to comprehend how big data is employed in the media. It is employed in particular media contexts like newspapers, magazines, and television. The goal is to provide or present information that is (statistically speaking) consistent with the attitude of the consumer.
Happy learning! 😊
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