We generate massive amounts of data each day: from where we go and what products and services we use, to who we communicate with and what media we consume. All of this information can be used by companies and organisations to better understand their consumers and optimize their business strategies and practices. With the increasing volume of data that’s now available, more and more businesses from almost every industry are realising the value of data science.
In fact, the last two years have proven to everyone across the world how powerful data is when it is used ethically, effectively, and for the betterment of people. The COVID-19 outbreak showed us all how vital data and data science are when it comes to understanding how the virus spread, predicting infection spikes, and vaccine research and rollout,
As Artificial Intelligence Luminary Andy Veluswami says, “What excites me about data science are the sheer possibilities. It’s like solving a puzzle, but a puzzle that’s meaningful and that has real-world implications, and that…can really help change the world.”
So what is data science, and exactly why is it so powerful for the future of almost every person, business, and industry on the planet? Let’s take a look.
What is data science?
Data processing. Algorithms. Computer science. Machine learning. If you were to ask a data scientist what their job entails, the answer might be a combination of these elements. For those of us who like to keep things simple, TechTarget defines data science as “the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies.”
A practical example of this in action would be Buxtunco, a data science company that records what certain customers are doing at particular locations. By using data, Buxtunco can help businesses choose the most potentially profitable location for their stores on the ground.
Another example would be how data is being used by hospitals to determine why certain people are readmitted. Data can go beyond knee-jerk assumptions to grant useful insights into the actual hidden problems of public health, including whether there are correlations between readmission and socioeconomic data points such as income, physical address, crime rates, and air pollution.
With more industries becoming increasingly data-driven, it’s likely that the field of data science will undergo some drastic changes to fit the changes within the industry.
Here are 3 changes that we predict for the coming years.
Prediction #1: Diversification of data science job titles
Data science is a growing field with hundreds and thousands of jobs being created each year. In the US alone, it has been predicted that there will be 11.5 million jobs in data science and analytics by 2026. In South Africa specifically, the data scientist role is understood to be one of the most secure and lucrative jobs you can get, and one that has only grown in demand as a result of the COVID-19 pandemic.
Right now, the job title of data scientist is an umbrella term for a range of different jobs, including data engineering, full stack data science, jobs that entail running reactive SQL queries, research and as one Quora contributor puts it, “100 other things.” In the future, job titles may diversify to describe more accurately what each “branch” within data science entails.
This diversification will enable better recruitment processes and hiring accuracy because companies will be able to narrow skills down to a specific business need (and therefore job position) and recruit for that particular set of skills.
Prediction #2: Learning on the job
Executive recruitment company, Burtch Works predicts that the data scientists of the future will be at the forefront of the world’s technological progression. Big data in a sense, is the key to decoding human behaviour, and the more we understand about ourselves and the world around us, the “smarter” the world will become in terms of the innovations that humans can catalyse.
For this reason, the data scientists of the future will need to be ongoing learners. Data science is not a two-step, learn-and-apply process. It’s a learn-apply-repeat process, and the most influential scientists in the industry will need to ensure that their skills and knowledge remain up to date and marketable in today’s evolving society.
Prediction #3: Greater focus on ethics
As Andy Veluswami explains: “Teaching a machine how to do mathematics, we’ve done that. But teaching a machine good manners, we haven’t done that yet. Once we start getting there, and we already are, we’re going to start making a lot more progress.”
The world is just discovering the impact that big data is having on everyday life and realising its potential for the future, and as it discovers the good, so too will it discover the pitfalls of how this data is used. Ethics will play a major role in the future of data science, as issues around privacy, and the freedoms of movement and speech continue to spark debate in the field.
Whose data can be used, and when? How do we know if a person has consented to share their data? In what ways can data be used for nefarious or harmful purposes? These are the questions the regulatory bodies, lawmakers, and professionals within the industry should be looking to answer.
Data science is an evolving field, driven by new technologies, research, and questions around how and when to collect and analyse data. It has become so much more than crunching numbers – and today’s data scientists will have to adapt to these changes and new trends if they’re to keep ahead of the curve of Big Data’s rapidly evolving future.
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