The industries of data analytics and big data are expanding. We are already seeing a transition in corporate decision-making from gut instinct to data-driven conclusions. This is a fantastic chance for folks who enjoy numbers and enjoy doing research.
In learning analytics, finance professionals and business students have an advantage. They possess good quantitative abilities as well as a thorough comprehension of statistics. They also have a thorough understanding of corporate operations and financial health. They have an advantage over the competition because of their knowledge of finance and management. After all, numeracy is at the foundation of both analytics and business development.
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So what is Big Data?
Big data refers to massive, difficult-to-manage data volumes – both organized and unstructured – that inundate enterprises on a daily basis. But it’s not simply the type or quantity of data that matters; it’s also what businesses do with it. Big data can be evaluated for insights that help people make better decisions and feel more confident about making key business decisions.
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Big Data’s History
Big data is defined as data that is so massive, quick, or complicated that processing it using typical methods is difficult or impossible. The practice of collecting and storing vast amounts of data for analytics has a long history. Industry analyst Doug Laney defined the now-standard definition of big data as the three V’s: volume, variety, and velocity, in the early 2000s.Volume. Transactions, smart (IoT) devices, industrial equipment, videos, photos, audio, social media, and other sources are all used to collect data. Previously, storing all of that data would have been too expensive; now, cheaper storage options such as data lakes, Hadoop, and the cloud have alleviated the strain.
Velocity. Data floods into companies at an unprecedented rate as the Internet of Things grows, and it must be handled quickly. The need to cope with these floods of data in near-real time is being driven by RFID tags, sensors, and smart meters.
Variety. From organized, quantitative data in conventional databases to unorganized text documents, emails, movies, audios, stock ticker data, and financial transactions, data comes in a variety of formats.
What Is the Importance of Big Data?
The volume of data accessible isn’t the only factor that determines the value of big data. How you can use it determines its value. You can get answers that 1) streamline resource utilization, 2) increase operational efficiencies, 3) improve design and development, 4) help drive profitability and profit prospects, and 5) enable wise decision making by evaluating data from any source. When big data and high-performance analytics are combined, you can do business-related activities such as:
- In near-real time, determining the root causes of failures, difficulties, and flaws.
- Anomalies are detected faster and more correctly than the naked eye.
- Optimizing patient outcomes by transforming medical picture data into insights as quickly as possible.
- In minutes, whole risk portfolios can be recalculated.
- Increasing the ability of deep learning models to effectively categorize and respond to changing variables.
- Detecting fraudulent activity before it has a negative impact on your company.
A Distinctive Approach
The approach to analytics used by a commerce student is distinct. It expands on their knowledge of management and finance acquired throughout the program. They can extract necessary details from numerical data thanks to their knowledge of numbers. Their commercial knowledge also aids them in making better selections.
The example of the Spanish train operator RENFE is one example of this unique technique. RENFE’s fleet consists of hundreds of trains. It adopted a reactive maintenance’ (curative instead of preventative) approach to keep its fleet functioning, as did other operators. This method had one big flaw: trains would occasionally decompose en route. This resulted in substantial schedule disruptions as well as financial losses.
The data analysts in charge of resurrecting RENFE’s profitability had a thorough understanding of the company’s structure and operations. They were aware of the financials and how they affected operations and other parts of the company. They also had a thorough view of the connection between failures and profitability. They had a unique understanding of the scope of the company issue and realized they had to address it.
They resorted to a predictive modeling strategy to solve the problem. To implement this strategy, they gathered diagnostic data from each train. When a train began to show abnormal readings, it was dispatched for service. This avoided field breakdowns and allowed RENFE to maintain its trains on schedule. This resulted in lower operating costs, less downtime, higher revenues, profits, and greater customer satisfaction.
Impact of Data Sceince on Commerce’s Student!
Data science is based on quantitatively translating information derived from market data into actionable reports and solutions. Numbers play an important role in data science. Because most commerce degrees mix business and mathematics, a commerce graduate’s skills are valuable in data science.
To inform one or more business choices, reliable computations must be made using the available data. As a result, a professional with a degree in business brings to data science a strong understanding of algebra, probabilities, and statistics and a deep awareness of markets and profitability.
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Improvement of job-specific and soft skills
Commerce and management students have a thorough understanding of key abilities such as Excel and statistics during their graduate years. Enrolling in machine learning training allows them to hone these talents and blend non-technical and technical abilities in order to make data-driven and analytically driven management decisions.
Problem-solving, structure and rational thinking, intellectual curiosity, and good communication are all transferable soft skills acquired during your education. These abilities, once learned, aid in your overall development.
Help in the search for better career possibilities and in the pursuit of higher education:
Data science is preferred by students over other types of training since it can help them locate unique work prospects in both management and information science (India Today, 2020). Even if they do not want to pursue a career in data science, the information they obtain will offer them an advantage over other students when applying for higher school.
Foreign accounting, entrepreneurial, corporate secretaryship, travel and hospitality, and business intelligence are all popular post-graduate majors for management students that demand a good understanding of data as well as the ability to run firms.
