We live in the digital age. With so much information packed in bits and code, managing such a massive volume of data is increasingly critical. Because of the rapid growth of technology, terminology such as Artificial Intelligence, Big Data, Business Analytics, and specifically Data Analytics are becoming increasingly common. There has recently been a scarcity of data science professionals in such a context. The need for a data analytics professional is now on the rise. A data analytics professional is needed in almost every company or firm. As a result, persons with a degree in data analytics or a related area have several career choices. The industry is in desperate need of data science and analytics expertise. Not only that, but because there is such a great need for specialists in this field, the compensation is very substantial. Indeed, given the current state of affairs, it would not be an understatement to say that this is the best time to consider a career in data analytics.
Data analytics is an excellent professional path. There has never been a better moment to work with data. Every day, around 2.5 quintillion bytes of data are created—and the rate is only increasing (Eastwood, 2021). The revolution of data is driving the sector that uses it; as businesses’ data collecting develops in extent and complexity, they will inevitably want to use that data, and Data Analysts are at the forefront of this movement. In 2017, IBM anticipated that the number of data professionals in the United States alone would increase by 364,000 to 2,720,000 by the end of 2020 (Hajkowicz & Dawson, 2018). Other sources support the trend of firms investing heavily in big data; according to a recent Dresner Advisory Services survey, big data usage in enterprise enterprises increased from 17 percent in 2015 to 59 percent in 2018 (Naganathan, 2018).
As data analytics becomes more widely used, so does the breadth of its applications since many sectors are on the verge of being completely transformed by big data. A recent McKinsey analysis anticipated how digital analytics would revolutionize marketing, operations, and manufacturing, with the promise of data-activated, one-to-one marketing engagements. More industries have yet to understand the possibilities of this technology fully. Another McKinsey research reveals that if the healthcare industry in the United States utilized big data to enhance effectiveness and reliability, the industry could generate more than $300 billion in value, and a large retailer using big data to its maximum potential could boost its operating margin by more than 60% (Grover et al., 2018). In other words, we do not see a halt in growth anytime soon.
High demand for Data Analysts corresponds with a rise in salary. Many Data Analysts’ incomes are comfortably above $70,000, even in junior jobs, with senior and highly specialized roles frequently exceeding $100,000. According to Glassdoor (2022), the average base salary for a data analyst in the United States in December 2021 was $69,517. This is determined by one’s seniority, workplace, and other factors. On the other hand, data analysts are in high demand. The World Economic Forum placed it second in the United States regarding job growth. According to the Bureau of Labor Statistics, related jobs are growing alarmingly fast. Between 2020 and 2030, operations research analyst employment is expected to grow by 25%, market research analyst posts by 22%, and mathematicians and statisticians by 33%. The overall employment growth rate is 7.7 percent (Dubina et al., 2021). Working as a data analyst can open doors to new opportunities.
Many people who start as data analysts advance to become data scientists. Data scientists, like analysts, use statistics, mathematics, and computer science to analyze data. On the other hand, a scientist may use advanced techniques to develop models and other tools for forecasting future trends. According to the US Bureau of Labor Statistics, the average salary for a data scientist is $100,560. Moving into management is another popular path for data analysts. Starting as a data analyst, an individual may advance to analytics manager, senior analyst, director of analytics, or even chief data officer. As of January 27, 2022, the average salary for a chief data officer was $230,990. In the United States, Eastwood (2021) highlights that the average annual salary for a data analytics consultant is $91,755. Aside from the high demand and appropriate income, Data Analysts can collaborate and contribute to the decision-making process at the highest level, which may convert into an opportunity to advance into higher management roles. Many Data Analysts also enjoy the freedom to travel and work remotely or relocate, even globally. The nature of the employment itself is totally up to the person, but the income, benefits, and job stability are substantial.
A career in data analytics can take many different forms. In line with this, four different areas of data analytics add value to a business. According to Odynski (2022), predictive analytics, diagnostic analytics, descriptive analytics, and prescriptive analytics are the different areas of data analytics. Descriptive analytics examines historical events, such as quarterly sales, monthly earnings, and annual website traffic. These assessments allow a business to detect anomalies. Contrary, diagnostic analytics explains the occurrence of events by correlating descriptive data sets to identify links and patterns. This aids a corporation in discovering the fundamental reason for a positive or negative outcome. On the other hand, predictive analytics attempts to forecast results by identifying descriptive and diagnostic data trends. This allows a company to be proactive, such as contacting a client who is hesitant to extend a contract. Finally, prescriptive analytics seeks to determine what corporate action should be taken. While this sort of research adds tremendous value in terms of being able to handle prospective issues or remain ahead of market trends, it frequently necessitates the employment of complicated algorithms and modern technologies. It is worth noting that the consultancy PwC (2022) discovered that descriptive analytics are unsuitable for informed, data-driven decision-making in a 2016 survey of more than 2,000 company leaders. As a result, diagnostic and predictive analytics are becoming increasingly vital to businesses.
In a firm, a data analyst is responsible for various tasks irrespective of the area they are positioned. A data analyst develops and monitors data systems and databases, including debugging coding errors and other data-related concerns. Additionally, data analysts are in charge of extracting data from primary and secondary sources and organizing it so that both people and computers can easily interpret it. They also use statistical methodologies to investigate data sets, focusing on patterns that may be important for diagnostic and predictive analytics operations. Data analysts, according to Aleryani (2020), are in charge of developing reports for top level management that efficiently describe tendencies, patterns, and projections utilizing pertinent data. Odynski (2022), on the other hand, assert that it is their role to collaborate with engineers, corporate directors, and programmers to identify possibilities for operational efficiencies, push for system modifications, and develop data governance standards. Finally, data analysts are obliged to produce appropriate documentation that helps stakeholders comprehend the data analysis process and, if necessary, replicate or redo the analysis.
Due to the wide spectrum of roles that data analysts take, data analytics is a professional path that allows one to employ a wide range of complicated talents to assist overall business processes. Strategic analysis, Critical thinking, and cross-functional communication abilities are necessary, as well as a strong sense of focus and commitment to pore over extensive datasets daily (Dong & Triche, 2020). Professionals in data analytics are adept at obtaining critical information, asking probing questions, and applying great business acumen to deliver critical insights. As a result, data analysts must have technical capabilities. Data visualization apps such as Tableau, Database languages such as SQL or Python, and spreadsheet tools such as Microsoft Excel or Google Sheets are necessary. Mathematical and statistical skills are also important for data collection, measurement, organization, and analysis. Skhvediani et al. (2021), on the other hand, emphasize the importance of technical and leadership qualities in data analysts. A data analyst’s managerial skills prepare them to take on the responsibilities of solving organization’s problems and participate in decision-making. These skills help analysts to plan strategically about the tools that will assist stakeholders in making data-driven business choices while also effectively communicating the value of the information.
According to Sedkaoui (2018), data analysts are being hired by companies today to help them make sense of the increasing volume and variety of data they create and gather. Getting actionable answers from data has become a critical business skill. All types of businesses collect big data to using it to make or enhance choices. Businesses use data analytics in industries as diverse as B2B and B2C commerce, health care, manufacturing, and marketing to improve operations and increase profitability (Eastwood, 2021). For instance, the healthcare sector employs data analytics in clinical research to forecast the efficacy of medications and survival rates. On the other hand, factories are continuously seeking methods to boost production yields – even a one-to-two percent increase in yield may mean millions of dollars to a chip or pharmaceuticals company. As a result, professions in data analytics are not going away anytime soon.
Data analytics is a career that will always exist because as long as a company is running, it will need to know why certain processes are working and why some are not. As a result, my future career aspiration is to become a big data analyst. With all of the benefits of becoming a data analyst, I aim to pursue a career as a data analyst after finishing my degree. My interest in this professional path stems from technological improvements gradually transforming how businesses are done. Any institution that does not adapt to such developments may be forced to close its doors in the near future. According to this research paper, an information science-based job will be the best option because many prospective employers will want to stay up with current technological advances in data analysis. Nonetheless, I understand that as a data analyst, I might work in various fields, including the local and national government, education, criminal investigation and forensics, and NHS administration. As a data analyst, regardless of where I end up working, I will always be responsible for producing dynamic analyses and objective and logical presentations in a limited period.
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