How the data scientist will evolve over the next decade
As the world continues to generate staggering amounts of data, data scientists are at no risk of losing out to AI.
When the digital revolution hit, it forever changed the parameters of data science. To cope with the demands of big data, there has been an emergence of new technology, systems and data science roles. As the world continues to generate staggering amounts of data, how will data science evolve over the next decade?
An increase in the demand for data scientists
If you are thinking of becoming a data scientist, the outlook is rosy. Glassdoor ranks data scientist as the best job in the US in 2019, earning a median salary of $108,000. It is one of the hottest careers at the moment and the demand for data scientists is set to increase.
With an exponential increase in data comes a greater demand for data scientists. It is estimated that the entire digital universe will reach a mind-boggling 44 zettabytes by 2020. To put 44 zettabytes into perspective, it means there will be 40 times more bytes in cyberspace than there are stars in the observable universe. That is hard to fathom.
Currently, there are not enough data scientists to adequately cope with the predicted explosion in data. Maryville University says that the demand for data science experts already outweighs the supply. By 2020 it is projected that there will be more than 2.7 million data science and analysis jobs available in the US alone.
More clearly defined data science roles
In the past, the term data scientist was broad and encompassed a range of tasks from data capturing to data insights. Today the role has diversified. Managing big data requires a team of experts, each with a specific set of skills. Companies are realizing that they need to shift from employing a single, often overworked, data scientist to creating a multi-skilled data team.
Companies will strengthen their data-driven strategies
Many businesses still underestimate the power of data, even though McKinsey estimates data-driven retailers see 60% larger profit margins than competitors. They fail to manage their data effectively and those that do have a data scientist do not use their skills to the fullest. A weak data strategy can kill a business. Data does not exist in a vacuum. It influences every aspect of a business including marketing, customer relations, finance and strategy and can identify threats, weaknesses and opportunities.
Gone are the days when data scientists were relegated to the back office. More organizations are starting to see the value of data science and realize that developing a solid data-driven strategy is a priority, with 80% of UK companies planning to hire a data scientist in 2019, according to Deloitte. When assessing your company’s data strategy, every head of department should participate because each one knows the type of data that is vital to their department.
To implement a new data strategy may mean hiring additional staff. When hiring data scientists, businesses ought not to skimp on recruitment. To find the best talent, source candidates through a recruitment agency that specializes in data and analytics jobs. If, however, your business is not at a point where you can create an in-house data department, there is the option to outsource data management.
AI and machine learning will play a bigger role
There are fears that AI and machine learning will make the role of the data scientist obsolete. This is unlikely as machine learning is merely one subset of data science. It cannot remove all the roles in data science. FileCloud and IBM both agree that replacing human intuition is far beyond the present ambitions of automation, for example.
With the massive amounts of data companies collect, AI will become a valuable tool to assist data scientists in processing this data. To understand the scope of data management, these statistics by Raconteur give you an idea of how much data is generated every day:
– 500 million tweets
– 294 billion emails
– 4 petabytes of data are created on Facebook
– 5 billion searches
– Every connected car generates 4 terabytes of data
– 65 billion messages are sent on WhatsApp
We are generating so much data that by 2025 it is predicted that 463 exabytes of data will be created each day globally. Realistically, the data scientist alone cannot manage and process these vast volumes of data. Rather than fearing AI, data scientists should embrace it. AI will not eliminate their jobs – 38% of companies responding to a survey conducted by PwC said that AI would lead to an increased headcount in their organization, compared to 19% who said it would lead to cuts.
Instead, AI will make data scientists’ jobs easier by cutting down the 80% of their time that is spent on repetitive or tedious tasks. This will allow them to spend more time on identifying and communicating opportunities presented by their AI assistants.
As such, data scientists remain an essential investment. Over the next decade, data science will shift from being an afterthought to playing a more prominent role in business. The insights that data science provides is already proving highly valuable to corporations, government, healthcare and even non-profit organizations. In the future, it will be the driving force behind strategic business decisions.
By: Deevra Norling
5 ways Data Analytics is a Game Changer for the Insurance Industry
Data Analysis is a process through which we intensively inspect, transform, and model unstructured data, reducing it to a structured form
3 Ways to Leverage Analytics to Enhance Your Digital Business Model
Back in the late ’90s when the internet was in its infancy, the concept of a company “going digital” meant that they created a basi
How Big Data Impacts The Insurance Industry And Beyond
The insurance industry has always been quite conservative. However, the adoption of new technologies is not just a modern trend but a nec