Data Science and AI Trends 2022: The Interoperability Opportunity

Future Data Science Trends 

Introduction

Data science is a stimulating field for knowledge workers as it increasingly interconnects with the future of how organizations, society, industries, governance, and policy will function. It is quite easy to define the term “data science”.

Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to gain insights and knowledge from unstructured and structured data, and apply that knowledge and actionable insights across a broad range of application domains. Thus, data science is where science meets an AI.

Data science is now turning out to be the most promising and in-demand career paths for skilled professionals. Today, successful data professionals have understood the fact that they must surpass the traditional skills of examining huge amounts of data, programming skills, and data mining. Read on to understand what more we can expect from this promising field known as data science.

What To Expect 

Following are a few scenarios of what one can expect from data science in the coming years.

1. Augmented Data Management:

In the future, the AI-human hybrid workforce, i.e how users manage and deal with data will be highly integrated. Essentially augmented data management will allow active metadata to streamline and combine architectures and also increase automation in repetitive data management tasks. As big data optimization occurs, automation will turn easier in various human fields, thus, reducing task loads and generating AI-human architectures of human activity.

2. Scalable AI

As data science grows  both AI and ML are influencing all sectors. According to a survey conducted by Nvidia, there are around 12,000 AI start-ups globally. Thus, it is quite obvious that this will result to scalable AI and behavior modification at scale in humans making adjustments to this new reality.

3. AI-As-a-Service Platforms

With both data science and ML evolving, more B2B and AI-as-Service platforms and services will now become possible. This will slowly help democratize AI capabilities and expertise so that small entrepreneurs can also access such incredible tools.

Platforms such as Square, Shopify, and Lightspeed are going in this direction to enable new small businesses optimized with AI to develop faster. Meanwhile bigger technology firms are getting into the B2B market with their own spin on AI products that other businesses may require.

4. The Democratization of AI

As data science talent becomes more popular globally, a slight re-balancing of the business benefits is taking place in more nations. The democratization of Artificial Intelligence will certainly take a very long time; however, data science will eventually be more equally distributed around the world, leading to more social and wealth equality, availability to economic and business opportunities and AI for good. However, we are a long way away from this goal.

5. Improved Data Regulation By Design

If data science is fueling a world full of data, analytics, predictive analytics and big data optimization the way we handle data needs to improve and this means better cybersecurity, data privacy protection and a whole range of things.

6. Top Programming Language of Data Science Will Still Be Python

ML and data science professionals have driven adoption of the Python programming language. Python’s community, libraries and support system online is implausible and showcases how data science is a comprehensive community of practitioners and learners. This nurtures the collaborative spirit of the internet towards improved data and AI systems in society.

Conclusion

We may need to step outside our comfort zones to take on the challenges and opportunities that this digital gold brings. As data continues to grow and ML algorithms get smarter, we will need to adapt.