Marked as one of the highest paying jobby Glassdoor, the field of Data science has witnessed an immense growth in recent years. Employers are in the search of data scientists more than ever. A report by Indeed indicated a 29% increase in the demand of data scientists in a year. However, the increase in people skilled in data science grew at a slower pace which observed a rise by 14% only. The gap in the demand and supply has increased the value of a data scientist in the market.
Not only is the rise of data science prevalent in the realm of software but also in fields of marketing, education and manufacturing. Internet acted as the catalyst in the very beginning. Actions taken by internet users were trackable which led to enormous amounts of stored data. This further encouraged research where computer scientists indulged in real-time analytics. They deciphered how people used certain products.
One other reason which helped data science gain limelight was the rise in the related fields of Artificial Intelligence and Machine Learning.
The demand for professionals in data is increasing due to the rise in the popularity of data-driven decision making. Where people used Excel to work on data earlier, tools like Hadoop have nowadays secured a place for managing Big Data. With frequent advancement in technology, several other tools find meaningful use for organizations to make impactful decisions. The usage of a few is listed below:
Apart from the above-mentioned uses, data science has proven to solve many complex real-time problems. A few examples of the real-world problems which have found peace in the modern-day data-driven solutions are:
In addition to these, social networking sites like Facebook generates revenue via ad by showing content according to the user’s preference. They utilize users’ data to personalize their feed. On similar basis functions the system of ad recommendation of Amazon. Amazon stores the data of products searched by consumers and displays relevant ads on its website which attracts customers. Google, on the other hand, has redefined the data ecosystem by making use of it in every domain. From their search engine to Youtube to advertisements, everything runs on data.
A pinch of sugar in the ocean, these aren’t sufficient to describe what the vast field of data science incorporates. Even though the rise of data science isn’t sudden, the potential in this field is here to stay. Where until lately, only large enterprises were willing to invest in data scientists, now, almost every firm is. The growth is tremendous which obviously has a considerable effect on an individual’s growth aspects, the ones skilled in this discipline.
There are multiple factors involved in the meteoric rise of data science. First, the amount of data being collected keeps growing at an exponential rate. According to recent market research from the IBM Marketing Cloud (https://www- 01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=WRL12345GBEN) something like 2.5 quintillion bytes are created every day (to give you an idea of how big that is, that's 2.5 billion of billion bytes), but yet only a tiny fraction of this data is ever analyzed, leaving tons of missed opportunities on the table.
Second, we're in the midst of a cognitive revolution that started a few years ago; almost every industry is jumping on the AI bandwagon, which includes natural language processing (NLP) and machine learning. Even though these fields existed for a long time, they have recently enjoyed the renewed attention to the point that they are now among the most popular courses in colleges as well as getting the lion's share of open source activities. It is clear that, if they are to survive, companies need to become more agile, move faster, and transform into digital businesses, and as the time available for decision-making is shrinking to near real-time, they must become fully data-driven. If you also include the fact that AI algorithms need high-quality data (and a lot of it) to work properly, we can start to understand the critical role played by data scientists.
Third, with advances in cloud technologies and the development of Platform as a Service (PaaS), access to massive compute engines and storage has never been easier or cheaper. Running big data workloads, once the purview of large corporations, is now available to smaller organizations or any individuals with a credit card; this, in turn, is fueling the growth of innovation across the board.
For these reasons, there is no doubt that, similar to the AI revolution, data science is here to stay and that its growth will continue for a long time. But we also can't ignore the fact that data science hasn't yet realized its full potential and produced the expected results, in particular helping companies in their transformation into data-driven organizations. Most often, the challenge is achieving that next step, which is to transform data science and analytics into a core business activity that ultimately enables clear-sighted, intelligent, bet-the-business decisions.
Not only is the rise of data science prevalent in the realm of software but also in fields of marketing, education and manufacturing. Internet acted as the catalyst in the very beginning. Actions taken by internet users were trackable which led to enormous amounts of stored data. This further encouraged research where computer scientists indulged in real-time analytics. They deciphered how people used certain products.
One other reason which helped data science gain limelight was the rise in the related fields of Artificial Intelligence and Machine Learning.
The demand for professionals in data is increasing due to the rise in the popularity of data-driven decision making. Where people used Excel to work on data earlier, tools like Hadoop have nowadays secured a place for managing Big Data. With frequent advancement in technology, several other tools find meaningful use for organizations to make impactful decisions. The usage of a few is listed below:
- Tools like Python and R have witnessed exceptional improvements in their codes. These allow users to solve complex problems with only a few lines of codes.
- Google Analytics is another effective tool for the marketing department.
- Tools like Tableau, Microsoft Power BI and Sisense have found relevance in the business intelligence departments for the purpose of data visualization.
Apart from the above-mentioned uses, data science has proven to solve many complex real-time problems. A few examples of the real-world problems which have found peace in the modern-day data-driven solutions are:
- Advancement in the field of data science has made it is easier to detect fraud and abuse in insurance firms now. The credit card fraud detection system works on similar grounds. It protects the security of customers, thus, minimizing losses due to fraud.
- Automated piloting: The concept behind self-driven vehicles runs on data science. Still in its nascent stage, this will change the functioning of the automobile industry entirely.
- Prediction of short term (local) and long term (global) weather.
In addition to these, social networking sites like Facebook generates revenue via ad by showing content according to the user’s preference. They utilize users’ data to personalize their feed. On similar basis functions the system of ad recommendation of Amazon. Amazon stores the data of products searched by consumers and displays relevant ads on its website which attracts customers. Google, on the other hand, has redefined the data ecosystem by making use of it in every domain. From their search engine to Youtube to advertisements, everything runs on data.
A pinch of sugar in the ocean, these aren’t sufficient to describe what the vast field of data science incorporates. Even though the rise of data science isn’t sudden, the potential in this field is here to stay. Where until lately, only large enterprises were willing to invest in data scientists, now, almost every firm is. The growth is tremendous which obviously has a considerable effect on an individual’s growth aspects, the ones skilled in this discipline.
There are multiple factors involved in the meteoric rise of data science. First, the amount of data being collected keeps growing at an exponential rate. According to recent market research from the IBM Marketing Cloud (https://www- 01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=WRL12345GBEN) something like 2.5 quintillion bytes are created every day (to give you an idea of how big that is, that's 2.5 billion of billion bytes), but yet only a tiny fraction of this data is ever analyzed, leaving tons of missed opportunities on the table.
Second, we're in the midst of a cognitive revolution that started a few years ago; almost every industry is jumping on the AI bandwagon, which includes natural language processing (NLP) and machine learning. Even though these fields existed for a long time, they have recently enjoyed the renewed attention to the point that they are now among the most popular courses in colleges as well as getting the lion's share of open source activities. It is clear that, if they are to survive, companies need to become more agile, move faster, and transform into digital businesses, and as the time available for decision-making is shrinking to near real-time, they must become fully data-driven. If you also include the fact that AI algorithms need high-quality data (and a lot of it) to work properly, we can start to understand the critical role played by data scientists.
Third, with advances in cloud technologies and the development of Platform as a Service (PaaS), access to massive compute engines and storage has never been easier or cheaper. Running big data workloads, once the purview of large corporations, is now available to smaller organizations or any individuals with a credit card; this, in turn, is fueling the growth of innovation across the board.
For these reasons, there is no doubt that, similar to the AI revolution, data science is here to stay and that its growth will continue for a long time. But we also can't ignore the fact that data science hasn't yet realized its full potential and produced the expected results, in particular helping companies in their transformation into data-driven organizations. Most often, the challenge is achieving that next step, which is to transform data science and analytics into a core business activity that ultimately enables clear-sighted, intelligent, bet-the-business decisions.
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