What are the Future Trends in Data Science for 2023

What is Data Science?

“Data science is the discipline of making data useful.”

Data science aims to build, clean, and organize datasets. By creating, leveraging algorithms, statistical models, and custom analyses, data scientists turn structured and unstructured data into meaningful insights and apply it across a range of application domains.

According to Harvard Business Review, Fortune 1000 companies are investing in data and AI initiatives.

• 91,9% of companies report that the pace of investment is accelerating
• 62% report investments of more than $50 million.

So, finally, what are trends in data science? Let’s move forward to discover.

Data science trends 2023

Big Data on the Cloud

This trend appeared as a solution to the problem of collecting, cleaning, structuring, formatting, and analyzing a tremendous amount of data in one place. Up to 45% of businesses turned towards cloud-based data storage, processing, and distribution. One of the emerging trends in data science is the use of private and public cloud services for big data predictive analytics. This year, 90% of data and analytics innovation will demand public cloud services.

Today, any sort of information can be maintained in cloud storage and the amount of this information is growing. In two years, there will be 163 zettabytes of data (with one zettabyte equals 1 billion terabytes or 1 trillion gigabytes)

Augmented analytics

According to Gartner, augmented analytics is an approach of enabling ML, AI, and NLP to automate the data analysis (data preparation, insight generation insight explanation) in real-time.

Augmented analytics helps businesses get value from their data without technical skills or expertise and in less time than usual which puts the technology on the Data Science trends list. Other benefits:

• Increased speed to insight
• The result is more accurate
• Automated operational tasks
• Powerful analysis in the context

Augmented Analytics Features

• Automatic data identification
• Statistical algorithms ( forecasting, clustering, etc)
• Natural language interactions

Examples of Augmented Analytics use cases

• In supply chain management it helps to understand why a particular location isn’t delivering products as scheduled.
• Travel agencies find personalized offers to sell customers.
• Marketing agencies explore the effectiveness of ad campaigns and find variables that are hidden in the data.

Robotic process automation

The rise of digital solutions, mobile payments, and cryptocurrency forced financial institutions to implement new technologies to maintain a competitive advantage and provide better services to their customers. So, among other data science trending topics is RPA, or robotic process automation that is used by financial institutions to automate manual business processes. The revenue from RPA in Fintech is going to grow by $1 Billion by the next year, compared to $200 million in 2018.

RPA use cases include risk assessments, credit card processing, security checks, data analysis and reporting, compliance processes, mortgage processing, report automation, fraud detection as well as other administrative activities. This gives financial institutions more time and workforce to perform their core responsibilities.

Top benefits of RPA:

• Saved time and money
• Automated processes
• Augment workers with a digital workforce
• Controlled compliance costs
• Improved Customer Experience

Discover more on data science and finance!

Data-driven сustomer experience

The future of data science includes providing valuable, and enjoyable experiences, to clients, by businesses. Finding new strategies for leveraging client data into better customer service and new customer experiences led to a wave of innovation and greater levels of personalization in goods and services in the eCommerce industry.

In this case, data-driven customer experience means personalized product recommendations, demand forecasting, Personalized experience, help from AI chatbots, more user-friendly interfaces in the software we use, etc.

AI-As-a-Service platforms

With data science evolving, AI-as-Service platforms and services are becoming one of the data science industry trends. According to International Data Corporation worldwide spending on AI is expected to reach $110 billion by 2024 compared to $50.1 billion in 2020 and $12 billion in 2017.

AIaaS refers to AI tools that enable companies to implement and scale AI techniques at half of the cost of a full, in-house AI. Common types of AIaaS include chatbots & digital assistance, cognitive computing APIs, Machine Learning frameworks. Among well-known companies that have brought AIaaS are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. And also, Salesforce, Oracle, and SAP.

Using AIaaS allows to:

• Minimize the risk of investment
• Stay focused on core business
• Gain more value from your data
• Improve strategic flexibility
• Make cost transparent

Platforms like Shopify, Lightspeed Square enable businesses optimized with AI to evolve faster.

Small Data and Scalable AI

As data usage and data types became more complex and diverse in the last years, among the latest data science market trends is a move away from big data. Gartner forecasts that by 2025, 70% of organizations will turn their focus to small and wide data. Despite the fact small data is limited in volume, it offers even more effective insights. Scalable AI means the ability of data models, algorithms, and infrastructure to perform at the size, speed, and complexity required for a particular task.

Blockchain

Blockchain has already become a familiar term in fintech, healthcare, and the supply chain and now it made it to the data science latest trends list. From a data science perspective, it is also a source of high-quality data that can be employed to resolve a range of problems using statistics and Machine Learning.

Both data science and blockchain deal with data and both use algorithms created to manage interactions with different data segments. But with blockchain, there is no need for a central perspective where all data should be brought together as it used to be. Now it’s a decentralized approach where data can be analyzed outside of individual devices. Also, blockchain smoothly integrates with advanced technologies, like cloud systems, AI, and IoT.

Use cases in blockchain analysis:

• Blockchain analysis for competitive intelligence
• for confidentiality and security
• in research
• for forensic investigations

The adoption of Python

R used to be a primary programming language of data. And now the focus is on Python. It requires fewer lines of code to achieve the same results, is suitable for a range of business types, and is more accessible to those who have less experience. The diversity of this codding language allows you to write all your data analysis in the same language( from blockchain applications to machine learning models) In the application development market deep learning algorithms becoming popular, so professionals have driven the adoption of Python programming language within an industry.

Consider Inoxoft Your Trusted Partner

Inoxoft is a company providing top-notch data science development services and machine learning development services that include:

Our main focus here is to follow the trends and impact the market with big data-driven software development.

Learn more about big data analytics in finance and check the examples of predictive analytics in retail

Final Thoughts

Our daily life is driven by Data Science as every Google search triggers different Data Science processes. We all are captured with smart solutions and recommendations of what to buy based on products we bought in the past. From AI-As-a-Service Platforms and Augmented Analytics to other latest technology in data science, the industry is transforming every year, continuing to influence the market.

You can get powerful tools to build a product that will change the way of doing business and transform industries. Do you want to lead? Ask us how!