In the past decade, data science has evolved from a term that had most people scratching their heads, to one of the most in-demand skills worldwide. It’s now used by engineers, geneticists, businesses, and governments. You’ll find it in finance, economics, medicine, and social sciences.
The question? Will data scientists still be enjoying this surge of popularity in ten years? Or will technologies like artificial intelligence take over most of their jobs?
Here are a few things we can expect from the evolution of data science over the next ten years:
Up until now, datasets mostly included information like purchase, sales, or clickstream data. In the next few years, we can expect to see data scientists finding valuable insights from the Internet of Things. This will include sensor-generated data from offices, vehicles, retail environments, personal devices, manufacturing lines, and more.
Right now, the title ‘data scientist” has been incredibly helpful when it comes to demonstrating the value of hiring smart people to make sense of data. But the title can incorporate a variety of functions. These include everything from running basic queries to research and writing, to data engineering. They also include machine learning roles, full-stack roles involving analysis and experimentation, and a multitude of other functions.
This makes things confusing- both for the data scientists themselves and the businesses hiring them. We can expect to see a greater diversity of titles in the future to make it easier to pinpoint exactly what type of work a data scientist can do and the expertise they have.
Artificial intelligence is quickly reaching a point where many of the tasks data scientists currently do will be handled by machines. According to Gartner, we can expect more than 40% of the tasks that data scientists do to be automated by 2020. Gartner also projected that the amount of analysis done by ‘citizen data scientists’ and machines will overtake the analysis done by data scientists as soon as next year.
This definitely doesn’t mean that data scientists will find themselves out of work. While AI will be able to handle increasingly complex tasks, many will remain beyond the reach of machines. AI can’t convert raw data into a form which can be easily understood. This ‘data wrangling’ requires an experienced (human) data scientist.
It also can’t take findings and present them in a way that business leaders and executive can understand and use (known as data visualization). This means that data scientists still have a very bright future.
While AI will take over lower-level collection and analysis work, this simply means more time for data scientists to develop models and algorithms. AI and machine learning will improve analysis, but will also give data scientists more time to devote to the type of complex tasks that require a human brain and decision-making skills.
The demand for data scientists is no longer restricted to large tech firms like Facebook Google. The most successful companies making data-driven decisions were 6% more profitable and 5% more productive than their competitors. While SMEs aren’t quite churning out the same amount of data as these organizations, finding meaningful insights from their data is already giving many businesses a competitive advantage.
Unstructured data is growing at a rate of 40-60% annually for businesses. This includes both rich media, research data, and IoT data. Businesses of all sizes will need to analyse this data if they’re to remain competitive in an increasingly data-centric world.
Are you looking for data scientists to help you get the insights you need to grow your business? Get in touch today to learn how we can help.