What Are the Futuristic Prospects of Data Science?

by June 16, 2020

data science

We are living in the age of data and functions in a data-driven culture. All around us is data and technologies to harness its true potential. From healthcare to e-commerce, almost all industry is running on data. The advancements in technology are also driven by the demand for better data-harnessing analytics tools. Business Intelligence, Artificial Intelligence, Machine Learning, Natural Language Processing, and significant others – all have one thing in common – data as their lifeblood. This takes towards and important question that – What is the future of data and data science?

For the future of data, we are quite sure that it will keep on increasing at an unprecedented rate and so will the value of data science. By 2025, it is predicted that 463 exabytes of data will be created each day globally — that’s the equivalent of 212,765,957 DVDs per day! With the enhancement of this field, the stakes are that respective skills will also increase in demand with data scientists becoming one of the highest-paid professions across the world, even more than now.

Owing to the rising popularity, data scientists, in turn, are building systems that can speak, predict, anticipate, and give real results.

The bubble around data science skills isn’t set to burst. On the contrary, the introduction of data-driven strategies will continue to gain prevalence. More people will look at data, gain insights from it, and so it may lead to the use of the data science team as an integral part of any successful organization or, at least most of them. It may even cause competitiveness and desire to be on the top.

According to Towards Data Science, over time, it will become clearer that there will be a lot of data scientists who are proficient in Python or R. But, what about the ability to sell ideas to the management, ability to convince that your insight is worth pursuing? Visualization does half the job, but the other half is plain old marketing. Consequently, we may see a shift towards those who know how to place critical conversations around product challenges. So, those who can combine hard skills with soft skills will always be in demand.

Realistically, the data scientist alone cannot manage and process these vast volumes of data. Quite possible then that AI will become a valuable tool to assist data scientists in processing this data. Automated tools for statistical analysis and machine learning will become “smart” enough to replace data scientists for routine tasks, such as exploratory data analysis, data cleaning, statistical modeling, and building machine learning models.

Undoubtedly, the future belongs to Data Science and Artificial Intelligence, where the combination of the two will offer a new vision into the future of what proliferation data has to offer. Another aspect to bring into consideration is that, with the rise in data the demand-supply chain for cloud and IoT technologies will also meet new dynamics of the market to support better storage and connectivity among their nodes.