Data Science has caused a revolution all across the world. Data Scientists have eventually become an integral part of every organisation. Many fresh graduates and working professionals have pursued the AIML courses to get into the job roles offered in this domain.
Automation refers to technological applications in which human input is minimal. This covers business process automation (BPA), IT automation, personal applications such as home automation, and other similar technologies. Automation technologies, processes, and techniques improve the efficiency, reliability, and/or speed of many previously conducted tasks by humans.
Automation is employed in various industries, including manufacturing, defence, transportation, operations, facilities and more recently, in information technology.
Artificial intelligence(AI) automation is the most complex degree of automation. With the inclusion of AI, computers may “learn” and make decisions based on previous scenarios that they have analyzed. For example, in customer service, virtual assistants powered by AI can cut expenses and result in optimal customer engagement and experience.
The successful implementation of automation technology cuts manual chores dramatically. It can integrate machine learning with real-world situations, hence removing the need for data scientists.
Will data scientists be replaced by Artificial Intelligence(AI)?
What impact will automation have on the career of data science professionals? This is a question that commonly arises at IT conferences or webinars these days, whether from the ones already employed or prospective professionals with a Data Science certification considering a career in Data Science. Also, companies looking to reap the benefits of data science at a reduced cost are interested in this discussion.
Data scientists are expensive to hire, and there is a shortage of this talent because it is a relatively newer field. Many companies try to find alternate solutions. A few AI algorithms have recently been developed to analyze data and provide experiences similar to those provided by data scientists.
Observers generally point out that automation in data processing or data visualization will only make it easier for business professionals to get what they need without requiring human intervention. Indeed, Gartner had anticipated that by 2020, 40 percent of data science work would be automated. As a result, detractors argue that demand for data scientists can only fall.
It’s no surprise that AI can outperform data scientists. AI tools outperform data scientists in terms of speed and risk management. However, automated devices have not yet attained the intelligence of the human brain. Humans, for example, could offer nuance, insight, and imaginative problem-solving capability to the process that machines cannot match.
We argue that automation, AI, and machine learning will not replace or even lessen the need for data scientists. It creates more opportunities for them! It will, however, help fuel data science workflows and support data scientists, allowing them to focus on jobs that are more demanding, creative, and tough to automate.
Here are some reasons why automation is unlikely to obviate the need for data scientists.
- Understand the business problem and human judgment
The most crucial question in data science is not which machine learning technique to use or to clean your data. These questions must be answered before writing a single line of code: What data do you select? What questions do you pose to that data?
What is absent in artificial intelligence is the creativity, inventiveness, and business understanding required for those activities.
This is the requirement that only humans can fill: the ability to comprehend the business challenge. In a recently contributed essay, data scientist Michael Li, founder of The Data Incubator, summarized it this way: ” Real-world data is notoriously dirty, and many assumptions have to be made to bridge the gap between the data we have and the business or policy questions we are seeking to address. These assumptions [are] highly dependent on real-world knowledge and business context.”
Data scientists accurately assess and address the business problem, prepare data sets, clean them, suggest which algorithms to use, and analyze them. Data visualization frequently necessitates the use of a human because, the results to be shared with all stakeholders must be highly customized, depending on the audience’s technical understanding. This requires human judgment, which no automated technology can provide.
- Automation is just a way to do things faster.
Alexander Gray, vice president of AI at IBM Research, defined AI automation as the mechanization of time-consuming tasks. In an interview published last year on the IBM website, he described automation technologies as ” a timesaving benefit that data scientists embrace because they seemingly enjoy thinking more than tedium.”
Gray sees automation as providing data scientists with more innovative and more powerful tools to help them do their jobs. Of course, just as the advancement in technology has transformed the careers of office employees, more capable data science tools will inevitably change the way data scientists work.
As a result, automation can only supplement human abilities. Not only will automation enable data scientists to perform more, but it will also boost the impact and value of their work to the business organization.
- Resolving automation errors
Another reason people will not be phased out anytime soon is the inability of automated systems to detect when anything is wrong and causing problems. Contrary to popular opinion, automation is prone to errors. While automation can improve and accelerate processes, it also has the potential to spread human errors if the science underlying it is flawed.
Human oversight will always be required for the most critical applications. Therefore, data scientists will always be needed as they have a better understanding of the fundamental principles. Data scientists are responsible for ensuring the findings of automated tools are correct and the models are performing optimally. As AI and algorithms embed into every aspect of our life, any bloat will directly impact the bottom line.
Man Vs Machine
When it comes down to it, data science cannot be automated. Highly qualified and experienced data scientists will always be in demand for their ability to manage the data source, craft data-handling code, and design the best algorithms to extract the insights required by the organization.
Automation will be used as a supplement to increase data science jobs and make them more productive. Redundant and repetitive tasks can be handled by bots, whereas, data scientists can handle problem-solving duties. Furthermore, the mix of human problem-solving and automation will enhance data scientists rather than endangering their careers.
As automation advances, productivity is expected to rise. This can lower expenses, bringing data science within reach of more enterprises and, as a result, increases the demand. Ironically, this also suggests that automation will ultimately boost but not decrease the demand for data scientists.
There is a clear historical precedent that implies that automation will not eliminate the need for data scientists. This paradox — that automation boosts productivity while decreasing prices and ultimately increasing demand — is not new; we’ve seen it in disciplines ranging from software engineering to financial analysis to accounting. Data science is no exception, and automation will very certainly increase the need for it.
A career in data science is a safe bet for a well-paying job that will be in demand for decades. They also benefit from various businesses such as healthcare, marketing, e-commerce, education, retail, and financial services.
To pursue a profession in data science, you must first acquire the necessary skill set. With a degree or diploma in data science, you can grasp data science tools/techniques and technical know-how. There are institutes in India and worldwide that provide certificate courses, degrees, and post-graduate data science and analytics courses. There are many online data sciences courses available to help you enhance your career in this sector. Do find the best Data Science course, and enroll yourself today.