The Future of Data Science – Trends and Predictions

Today, we live in a world where digital experiences are in the driving seat for nearly every aspect of our lives. From businesses to government services, people are increasingly reliant on digital channels to get what they need and make lives easier. This has resulted in a dramatic increase in the volume of data produced globally. Studies predict that, by 2025, nearly 463 exabytes of data will be generated daily.

The biggest takeaway from this data generation scenario is that this very same data is leveraged for accurate decision-making by businesses as well as other entities. While analytics, artificial intelligence, machine learning, etc., are ways that drill deep into piles of data to uncover hidden insights and possibilities, the real mastermind behind the success of such large-scale data initiatives is data science.

Data science is responsible for keeping any set of actionable data ready for analysis and processing. The role of data scientists has become a critical part of nearly every major business in all sectors. Together with other technology leaders, data scientists work to build new working models for the large gamut of data collected from across an organization’s operational domain.

Data science has been around for quite some time now, and organizations are seeking ways to utilize data science as a key enabler of success and ROI in their business initiatives. So how does the future look for data science? Let us explore some of the top trends and predictions:

The AI Takeover

Over the last couple of years, and especially since generative AI technology like ChatGPT became a hot topic of interest, there has been widespread fear that machines will take over several jobs. While ethical implications and economic concerns associated with job losses are more likely to prevent or slow down a massive workforce transformation, it is certain that several job roles will undergo a series of transformations to amp up the skill quotient.

Data scientists, too, will experience a part of this transitional ordeal as they may have to acquaint themselves with powerful AI-powered tools that can build data models faster and more efficiently than when done manually.

Of course, the oversight needed is still a critical area where data scientists can exert their dominance, as most AI algorithms that work on data modeling are still in their learning stage. With industries like weather and banking being reliant on prediction models developed with core data science principles, the window for errors is non-existent. In that light, it might take a while before AI can significantly contribute to the core sophisticated functions. But it sure is invaluable in complementing data scientists’ workflow.

Rise of Quantum Data Science

In the next decade or so, we will surely witness a massive shift in the way computing power is considered for any kind of analytical initiatives courtesy of quantum computing becoming mainstream. Today, data scientists can build effective data models

only through limited sequential matching of scenarios and data patterns. In other words, if a couple of inputs must be run across different scenarios for modeling, it should be done one by one.

With quantum computing, however, it becomes possible to run them all parallelly without worrying about the performance of the underlying computing infrastructure. The key takeaway here is that, with near unlimited computing power, data scientists can build larger, more expensive, and more powerful models that can be leveraged to build state-of-the-art digital solutions powered by analytics run through these models.

Build More from Models

Traditionally, data scientists worked on translating complex business workflows and transactional processes into accurate data models that can be used for automation and analytics-driven decision-making. However, building data models is no longer the main ingredient of successful analytics initiatives.

The quest is to operationalize these proven models across the business at the earliest opportunity to prevent any competitive loss in the market. Once these models are implemented, the next attempt will be to scale them.

Leveraging of Tools

Adding on to the previous point, the secret behind successful data science initiatives is the ability of data scientists to accurately identify which data needs to be where and how it should be processed. Today, all these activities are somewhat automated and handled through no-code and low-code applications.

Even businesses with very less technical-focused employees can build extensive data models and scale them to meet the needs of modern problems.

In fact, Gartner predicts that, by 2026, about 80% of low-code users will be developers working from outside the formal IT departments. This is a critical development for the data science space, as it’s directly associated democratization of resources and capabilities.

Computing on the Cloud

For data scientists, the future is highly dependent on how efficient their cloud provider is. From data migration to modern-era no-code and low-code tools, there is a rising dependence on SaaS applications and platforms that can cater to the needs of people hooked to their digital universe.

Besides, the evolving capabilities of cloud platforms that can accommodate data engineering, ML engineering, data analytics, business intelligence, AI governance, etc., make way for enterprises to leverage the cloud for their rigorous data science initiatives.

Wrapping Up

As you can see, the future of data science is bright, as there are several areas where data science can be applied to bring about transformational changes. However, managing the entire transition to cloud and other emerging technologies is a critical task that necessitates maximum care and supervision. This is where Rubiscape can become the key game changer. Get in touch with us to know how we can help supercharge your data science programs.