The exponential growth in data that we have witnessed since the beginning of our digital era is not expected to slow down anytime soon, and this is just the tip of the iceberg. As it is spoken, the coming years will bring about a deluge in data making the job of the Data Scientist evolve and grow.
With the availability of data and the need for data professionals, the job of the data scientist as we know today will be barely recognizable in the next 5 to 10 years. This will mark a new beginning where data professionals belonging to different economic sections will work with data science software to ease the way non-technical people currently work with Excel. In fact, technology may push these data science tools to become just another tab in Excel in the coming years. Surprised? Well, this may be soon a reality!
Would Data Science be replaced by Automation?
Technology has made the average business user not to get affected even if a knowledgeable Data Scientist is not around as tech innovations increasingly empower the ordinary staff with tools to conduct their analysis and come with actionable insights. In the latest industry survey, about 51 percent of respondents believe that the full automation of Data Science will happen within the next 10 years. However, only about 25 percent think this change will happen in either 50 years or never.
The current generations of Data Scientists cannot let go of the ML-powered business systems, where many laborious human tasks are automated and taken up by tools or bots. So far as Data Scientists are concerned, that is a good new, as this leaves the human mind to be engaged in more critical issues.
Automation of Data Science will make machines undertake the mundane and complex tasks of data cleaning, data integration, and basic data modeling on their own. This will enable the Data Scientists to have plenty of time concentrating on complex algorithms that is difficult for machines to deliver embracing open-source tools to pursue day-to-day Data Science activities.
The automation wave will bring about-
• AI projects turn their attention to crowd-sourced data to enable useful solutions like flood warnings and road-accident prevention, pointing to the requirement of Data Scientists to work in teams.
• Data Security regulations (GDPR) becoming imperative to enterprise operations which making Data Scientists feel the necessity to be trained in GDPR regulations.
• Seasoned and Knowledgeable Data Scientists, continuing to be in high demand into each organization designing, developing, and managing enterprise-wide data strategy.
• Data Analytics vendors to focus on simplifying their systems for broader and faster adoption.
Riding the New Trend
The data science business is growing. Leading research firm Forrester states that by 2020, data-driven businesses will be worth $1.2 Trillion, a poignant growth from $333 billion in 2015.
This growth brings a huge opportunity for Data Science professionals who will be at the helm of governing and managing these huge data troves. The amount of Data is only increasing with each year. Here are some numbers-
The current daily data output is 2.5 quintillion bytes and roughly 90 percent of that data is created in the last two years, implying-
• Instagram users post 46,750 photos.
• Every minute, 15,220,700 texts are sent.
• Americans use 2,657,700GB of data every minute
• Every minute, Google conducts 3,607,080 searches.
The democratisation of Data Science has given way to Machine-learning-as-a-service (MLaaS) which is now a reality as more companies are utilising them instead of creating their own technology infrastructures.
Experts predict that data science and machine learning jobs will continue to grow in the foreseeable future as companies seek to fulfill the vacant and new positions in a journey to embrace modern technology.
The Changing Times
The Data Scientist Job will see a new light in the future. The traditional roles will give way to automation and there will be an urgent need for professionals to get re-skilled and sharpen their knowledge into statistical and machine learning modeling with Python or R and other tools. The new technologies in the block will be the growth of Big Data, Edge Computing, Blockchain and Digital Twins, with the market witnessing more demand for skills in Apache Spark, SQL, NoSQL databases and relational database management systems (RDBMS).
On one hand, advanced technologies and tools will probably erase the need for data scientists, a profession trending today while at the same time; mainstream business users may not be able to gain any insights without the intervention of data professionals.
It is clear that the technology is evolving integrating new changes and will continue to evolve through the coming years bringing some major changes to the data scientist profession.