Three technologies that will change how data scientists work
As of 2019, data scientists are one of the most desirable jobs on the market. Organisations desperately need skilled personnel who can comb through data for valuable findings. There’s a severe shortage in the market for skilled data scientists, and as the law of economics states, when demand exceeds supply, the price increases. But that doesn’t mean it will stay that way forever.
Technology is constantly evolving; machine learning and AI are advancing and taking on more complex tasks. Meanwhile, technology once hidden behind a thick barrier to entry, is now becoming exposed as analytics, and machine learning becomes more accessible. All these factors will change the role of data scientists and what they will do in the future.
Trends that will change the role of data scientists
Since there is a huge demand for data scientists, some organisations are turning to automation to ease the burden. The decision makes sense – even if organisations can afford the high salaries, many organisations can’t get the number of qualified scientists they need for the job.
As of right now, automation is primarily used for more routine, tedious tasks. For example, a data scientist spends most of their time cleaning and organising data for analysis. But with assistance from AI, scientists can move on to more advanced tasks, allowing them to be more productive and add more value. Perhaps, they will generate even more valuable findings because they spend more time on advanced techniques like model experimentation.
One reason why there is so much demand for data scientists is because of their variety in skillset. A highly sought after data scientist is skilled in machine learning, coding, data preparation, statistics, databases, data visualisation and communication.
On the other hand, it leads to a stressful work environment. New research reveals that most data scientists suffer from some sort of work-related stress. One of the main reasons identified is that they are expected to balance the technical, business and communication aspects of their role.
In the future, a data scientist’s responsibilities will become more specific as they begin to specialise. The next few years will see new roles spring from the data scientist spectrum, such as data science leaders, data translators and perhaps domain-specialist data scientists (though the last one is highly debated).
Keep in mind that we are still in the early years of big data. As organisations collect more data, the industry will start to mature and grow. Under such circumstances, it’s impossible for one person to fulfil all the needs of a company, which means responsibilities will be broken down into more specialised roles.
When websites were first introduced, it was impossible to develop one without the help of an experienced programmer. Then, WordPress was created, allowing people to create their own websites without the need for developers. Now, we see a similar trend play out with data analytics.
Several new technologies allow professionals to use analytics, AI and machine learning, even if they don’t have a background in programming. Self-service analytics allows professionals to use analytics platforms without a data specialist present. Low code or no code software development programs use graphical interfaces to make coding more accessible, while pre-trained AI models allow smaller companies to gain the advantages of artificial intelligence without a specialist. However, it goes without saying that the platforms that generate the most value and bang for buck will always be the entities that have existed and thrived over the years – most notably, platforms like SAS.
These changes do not mean that data scientists are going to be irrelevant. After all, professional web developers are still needed, despite the availability of a plethora of drag-and-drop web design and CMS tools. However, the democratisation of data and machine learning tools is going to affect their role. Perhaps, data specialists will act as consultants or will only be called upon only when high-end tasks need to be performed. At this early juncture, it’s hard to predict how their roles will change.
Data scientists are in high demand today because organisations need skilled personnel to draw valuable insights from their data. However, the future is going to change the current status quo, automation, the need for specialisation and the democratisation of data will affect what a data scientist does, and how they do it. It’s important to be aware of these trends because our relationship with data will change, and so will the role of a data scientist.
That’s why organisations that provide specialist consultation data analytics services are increasing in popularity. Instead of firms having to spend exorbitantly on expensive in-house staff, they can now access the expertise of an entire team of consultants.
What other developments do you think will change the responsibilities of data scientists?
Want to learn more analytics, AI and machine learning? Check out our blog.