A Promising Field: Data Science
A general search on the internet on the world’s most promising majors both now and the future will possibly land you on a list that would be incomplete without the mention of computer science and statistics. But what happens when statistics meets computer science? They give rise to the field of data science. The world is buzzing with a lot of data every day. The last decade has seen the explosion of data massively. In this age of massive technological advancements and ever increasing internet coverage, data is virtually being generated every microsecond or even nanosecond. There is someone in need of information every second and with the great deal of sensors, processors, communication, gadgets etc. there is no doubt that there is a lot of information exchanging hands that most people can ever imagine or picture. The question then that arises is how useful is the data that get to our reach? What data is useful to one individual and not another? How does this data revolutionize industries and the various sectors of the economy from corporates, to transport sector, to agriculture, health, education and many more. The solution is data science, where Information Technology meets statistics. Data science makes us understand the source of data, the destination of data and how best we can make use of the data. It seeks to turn data into something valuable and that’s just what most people in the world needs: data that makes sense and is valuable to the one in its possession. All these factors show the necessity and importance of the field of data science and the great career prospects it presents to those who seek to pursue the field.
Data Science: Definition, Advantages, Challenges and Career Prospects.
Data science can be defined as the study of information: where information comes from, what it represents, and how it could be transformed into a priceless resource for individuals, businesses, and governments. The field of data science incorporates statistics, computer science, and mathematics disciplines and brings together other fields such as machine learning, data mining, cluster analysis, and data visualization (Insights on governance, risk and compliance, 2014). Therefore, by mining huge quantities of unstructured and structured data, it identifies patterns that could help an organization to find new markets, improve on its efficiencies, improve on its costs and increase its competitive advantage (Insights on governance, risk and compliance, 2014).
The Impact of Big Data on Business
Every single day, terabytes of data are being generated from digital technologies and information systems. Businesses and organizations are basically being run on data that is transiting very fast from their sources to their intended destinations and sometimes unfortunately to unintended destinations (Jafar Raza Alam, 2014). This has led to the growth of what is today referred to as big data. Big data denotes the gauge and different volumes of data produced by machines, applications, and people. It refers to sets of huge and complex datasets that may be hard and probably impossible to handle and process using the traditional data processing applications and management tools. They could be structured or unstructured and could be in petabytes or more. The main aim of big data analysis is to process high velocity, high volume, high variety, and high veracity data (Rahman, 2017)
The Role of Data Scientists in Unlocking Business Value
The incompetence of traditional data analysis tools and methods necessitates the use of data science concepts in handling big data. By 2015, big data had been estimated to have bypassed 25 billion. The increase in the amount of data being generated in modern times necessitates the need for data scientists who enable organizations to process raw data into meaningful and valuable information. Data scientists possess analytic statistical and IT skills needed to handle this information (Rahman, 2017). Apart from interpreting and managing the huge amounts of data, they also create models of data visualization that help the organizations they work for discover the business value that comes with digital information. They employ their wide range of mathematical, statistical, programming, and other wide range of skills to uncover the business solutions found in data analysis and use these solutions to enables businesses tackle challenges and meet their business goals. Data science helps eliminate intuition by enabling decisions to be made through data-driven insights that influence market strategies, customer satisfaction, product profitability, customer acquisition and retention and operations management (Rahman, 2017).
Data Science Challenges and Security Risks
Competition amongst businesses has been practically reduced to data and information. This is because these two enable businesses to make factual, better and real-time decisions. This demand for information has been a major contributor to the growth of big data and data science. Big data changes the manner in which businesses operate and compete. Companies investing in data and get value from the data available to them have a distinct head start ahead of their competitors (Insights on governance, risk and compliance, 2014). This competitive advantage due to access to and proper use of data is poised to grow over time. Over the past years, the ability to capture data and store it has grown, but data that is captured and stored may not be of any help to the organization if not analyzed well. Rapidly changing technology has led to an increase in the potential of employing data-driven results into organizations and using them to achieve even greater results. Companies have continued to be aware of the success that comes with integration of processes, people, data, and technology. This implies the ability to incorporate data in business strategies, business routines, and other day to day operations (Insights on governance, risk and compliance, 2014).
Data Science Career Prospects
In the changing business environment, it becomes necessary to predict future happenings. Data science enables this to be achieved by use of data visualization techniques. This employs the use of predictive models while help improve the business’s agility and enable the business keep in touch with both present and future market conditions hence improving how they prepare and strategize for the future. This enables the business to keep up with new market trends and demands (Acharjya D P, 2016). Data science helps the management keep track of their past performances and analyze them effectively comparing them with future prospects for the business. This is advantageous compared to traditional methods that were rigid and would hardly predict the future offering only the possibilities of making decisions based on past records and nothing of the future. Data science also enables businesses to choose the best workers out of the job market. It also enables the company to retain customers and meet their needs. Data about customer needs, trends, and demographic distribution (Rahman, 2017). These enable businesses to employ the best customer retention strategies and know who to employ to meet this objective. As compared to the traditional advertising methods that would mostly be based on the assumptions of the business on what they feel the customer wants, data science enables businesses to better identify customer demands (Rahman, 2017).
Conclusion
Even though data science is changing the way businesses are viewing data, there are still challenges that are posed by data science. Two greatest risks are those of privacy and security. Any data is sensitive data and it is critical that it doesn’t get into the wrong hands (Acharjya D P, 2016). This necessitates the businesses that employ data science to put in place strategies that would ensure that the data is secure and private and does not fall into the wrong hands. Where data mined falls into the wrong hands or privacy is violated, effects could be disastrous such as accusations leveled against Facebook for giving out customer data. However, even though data science possesses these risks, there are already sufficient methods developed in the current world to protect data against cybercrimes even though they may not be a hundred percent effective. The benefits that come with data science override these risks. Therefore, data science techniques continue to be more advantageous compared to traditional methods (Acharjya D P, 2016).
Some of the companies that employ data science in their core operations include Netflix that uses data mining to know what customers are interested in most by using viewers’ history, banking institutions that employ data science to detect fraud, and shipment companies like DHL and UPS which determine the best delivery routes using data science. Google’s search engine is also based on data science same to social sites such as twitter and Facebook (Columbus L., 2017).
What therefore are the prospects of an individual who decides to venture into the field of data science? There are very many fields one can specialize in when it comes to data science. These include Business Intelligence Analyst, Data Mining Engineer, Data Architect, and Data Scientist. The demand for these skills in the market has continued to rise especially given that it is still a developing field with a lot of future prospects (Columbus L., 2017). It is estimated that there has been a six hundred and fifty percent increase in data scientist roles since the year 2012. It is estimated that it would have created about 11.6 million jobs worldwide. Glassdoor (2018) reports lists six data science and analytics jobs among top fifty best jobs in America. It also lists data scientist as the best job in America for three consecutive years (2016 – 2018) with about 4,534 job openings yearly (Louis, 2018).
Traditional systems cannot keep pace with the ever-changing world today. This is because they are inflexible and quite slow and are also unable to handle the large volumes and complexities that come with big data. A key ingredient of today's successful businesses is to be able to get the right information at the right time and to put the information into valuable use. It is no doubt that the rapidly growing data in the world calls for swifter and more efficient methods to handle and process this data, hence putting into perspective the great need for data science. It is no doubt that the field of data science will be with us for a long time. With the growing complexities of data, the field will continue to grow and develop. As long as there will be data, then it is safe to assume that there will be data science. While there are risks that come with big data, measures already in place to handle these risks can at the end of the day guarantee that the merits outweigh the risks. Data science is not only a field for today but also for tomorrow. It shall continue to evolve but never go extinct.
References
Insights on governance, risk and compliance. (2014). Big data: Changing the way businesses compete and operate. EY.
Acharjya D P, K. P. (2016). A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools. International Journal of Advanced Computer Science and Applications, .
Columbus, L. (2017, December 11). LinkedIn's Fastest-Growing Jobs Today Are In Data Science And Machine Learning. Retrieved from Forbes: https://www.forbes.com/sites/louiscolumbus/2017/12/11/linkedins-fastest-growing-jobs-today-are-in-data-science-machine-learning/#2e0d072e51bd
Jafar Raza Alam, A. S. (2014). A Review on the Role of Big Data in Business. International Journal of Computer Science and Mobile Computing.
Louis, C. (2018, January 29). Data Scientist Is the Best Job In America According Glassdoor's 2018 Rankings. Retrieved from Forbes: https://www.forbes.com/sites/louiscolumbus/2018/01/29/data-scientist-is-the-best-job-in-america-according-glassdoors-2018-rankings/#1946c2e15535
Rahman, N. (2017). Big Data Analytics for a Sustained Competitive Advantage. Student Research Symposium.
One page summary
With the rapidly increasing data that is being generated every day, the question of handling, managing and usage of this data is key. In this age of internet and information, there seems to be millions of terabytes of data being transferred every other second. Data can open up a world of possibilities if handled and used well. Whereas statistics can be said to be the major that moistly deals with data, the traditional statistical methods of handling data may not fit well into today’s rapidly changing society. This is because these traditional methods are rigid and slow hence may pose a challenge especially where data is real time and decision making needs to be fast. With most businesses going for agile methods in their governance, strategies and operations, there is need to merge Information Technology / Computer Science with statistics. The merger of these two majors gives rise to one of the most progressive developments in 2018: Data science. Data science involves the handling, managing and productive use of data. It involves various concepts such as data mining and data analysis to use data to inform crucial decisions in both businesses and governments. The advantages that come with data science are huge and there is already widespread use of data science tools from social sites like Facebook, to shipping companies like DHL and UPS and search engines such as Google. Data science presents a lot of benefits to organizations and enables them to widen their customer base, inform their business goals and reduce their operational costs hence maximizing profits. Whereas data science comes with a lot of benefits to the companies, it presents two major risks that is privacy and security. It is key in the handling and accessing of data that the data is secure and private so as not to get into the wrong hands as the results could be disastrous. However, data science still offers a large amount of benefits that outweigh the risks. Data science is here to stay as long as there will be data.
