5 helpful data science and analytics trends to follow in 2023

The widespread use of digital technologies and the resulting generation of large amounts of data dominate modern culture. Data science enables the extraction of patterns and the derivation of important insights. Possibly no industry today doesn’t feel the need for data science. Finance, healthcare, retail, insurance and other industries use it extensively. In order to streamline their operations and make decisions based on the insights gained from data analysis, companies implement data-driven models.

Data science as a field is expanding rapidly due to the availability of data, advances in technology and the increase in the processing capacity of machines. By 2024, 75% of companies will have a dedicated data and analytics team, a consulting firm predicts. Businesses can foster innovation and manage market uncertainty by anticipating future trends in this space.

Adaptive artificial intelligence

Unlike traditional AI models, which are trained on a fixed, historical data set, adaptive artificial intelligence (AI) refers to the process by which AI models are continuously updated using real-time feedback based on changing environmental conditions. A self-driving car is an example where the AI ​​model instantly adapts to changing real-world circumstances based on new data and changed goals.

Image Source – Latent AI

AI TRISM (Trust, Risk and Security Management with Artificial Intelligence)

According to a consulting firm, AI TRiSM is a framework that enables privacy, robustness, effectiveness, fairness and trustworthiness for AI models. They predict that by 2028, AI-powered machines will replace 20% of the world’s workforce and 40% of all economic productivity, though most of the AI ​​models implemented are incomprehensible. Managing security concerns is critical for organizations that rely heavily on AI. Models may not work as expected, and privacy, security, and reputation issues may arise. Organizations need to manage the trust, risk, and security of AI to develop durable data science infrastructure, achieve better AI adoption outcomes, and meet business goals and user consent.

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Image Credit – Gartner

Cloud platforms for industry

An industrial cloud platform is a set of cloud services and programs designed for a specific economic sector, such as healthcare, banking, retail, or life sciences. Industrial cloud platforms are a new trend as they benefit businesses by providing flexible and relevant solutions. Cloud services have evolved and are now more than just providers of software and storage. To help companies meet their data and technology needs, they offer customized solutions for specific industries.

Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS) are now combined on cloud platforms with tools designed to solve the problems solve that companies face. For example, financial services providers need AI and cutting-edge data analytics to better understand customer behavior. The financial services industry is also heavily regulated, with certain information security requirements to be met.

A cloud provider for financial services companies not only has to offer its customers a tailor-made solution, but also adhere to certain standards for data security and compliance.

Image Source – Forbes

edge computing

A variety of networks and gadgets that are either close to the user or the data source are known as edge computing. On an edge device, machine learning models collect, process, and identify patterns in the raw data. This allows large amounts of data to be processed faster, reducing latency and delivering faster, real-time results. TinyML and edge computing are often combined in smart products, drones, and autonomous vehicles.

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Auto AI/Auto ML

The goals of Automated Machine Learning (AutoML) and Automated Artificial Intelligence (AutoAI) are to make ML and AI more accessible for people with little or no experience to train high-quality models tailored to their needs. They automate the processes of selecting, creating, and setting parameters for machine learning models used to solve real-world problems.

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This shortens the training time for ML models and speeds up and simplifies the machine learning process. Automating the machine learning application process has the benefit of generating faster, simpler, and more effective solutions that can save organizations a significant amount of money. In finance, fraud detection and risk assessment, cybersecurity threat assessment, and automated forecasting in healthcare are some of the common applications.

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