Data science & analytics trends to watch in 2023 

Modern day society is dominated by the extensive use of digital technologies which result in the generation of a large amount of data. Data science helps extract patterns and deduce valuable insights from it. There is perhaps no industry today where the need of data science is not felt. It is widely used in various domains including finance, healthcare, retail, insurance, and more. Organizations are adopting data-driven models to simplify their processes and make decisions based on insights derived from data analysis. 

With the availability of data, technological advances, and increase in the computing powers of machines, data science as a field is growing in leaps and bound. According to a consulting firm, 75% of organizations will have a dedicated data and analytics team by 2024. Anticipating the upcoming trends in this domain will help businesses drive innovation and navigate market uncertainty. The top data science trends of 2023 to lookout for are:

Adaptive Artificial Intelligence

Adaptive Artificial Intelligence (AI) is when AI models are continuously updated using real-time feedback due to changing environment as opposed to traditional AI models which are trained on a fixed, historical dataset. An example is the self driving car where the AI model adapts to changing real-world circumstances instantaneously based on new data and adjusted targets.

Industry Cloud Platforms

An industry cloud platform is a collection of cloud solutions and applications designed for a specific industry, like healthcare, banking, retail, government, or life sciences. Industry cloud platforms are an emerging trend because they create value for companies by offering adaptable and relevant solutions. Cloud services have evolved and are no longer just software and storage providers. They offer customized industry-specific solutions to help organizations meet their data and technology requirements. Cloud platforms now combine Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) alongside tools designed for the challenges companies in those industries face. For example, financial service companies need AI and advanced data analytics to gain insight into customer behavior. The financial services sector is also highly regulated, with specific mandatory information security measures. A cloud provider for financial services companies must meet these requirements for data protection and compliance along side providing the tailor made solution to their client. 

AI trust, risk and security management (AI TRiSM)

A consulting firm defines AI TRiSM as a framework that supports AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data privacy. They report that even though 20% of the global workforce and 40% of all economic productivity will be replaced by AI-driven machines by 2028, most of the AI models that get deployed are not explainable. For companies relying greatly on AI, managing security risks is extremely important. Models may not perform as predicted, there maybe security and reputational loss and privacy failures. In order to build resilient data science infrastructure, and attain better outcomes in terms of AI adoption and achieve business goals and user acceptance, organizations will need to manage AI trust, risk and security. AI TRiSM is the solution set to properly protect AI. 

AutoML/AutoAI

Automated machine learning (AutoML) and automated artificial intelligence (AutoAI) are processes that aim to make ML and AI more accessible to those with limited expertise to train quality models specific to their requirements. They automate the processes of selection, build and parameterization of machine learning models applied to real-world problems. This helps speed up and simplify the machine learning process and reduces training time of ML models. Automating the process of applying machine learning offers the advantages of producing simpler and more efficient solutions that can be deployed faster, thereby resulting in huge savings for organizations. Some common applications are fraud detection and risk assessment in finance, threat evaluation in cyber security, and auto prognosis in healthcare.

TinyML

Tiny machine learning refers to machine learning and deep learning models that run in tiny hardware’s like microcontrollers and microprocessors and operate on very low power. Bringing traditionally power intensive machine learning into the world of low power devices has the advantage of requiring low bandwidth to function and being energy efficient.

Edge computing

Edge computing refers to a range of networks and devices that are either near the user or the data source. Machine learning models on an edge device collect, process, and recognize patterns in the raw data. This enables processing large amount of data faster leading to reduced latency and quicker results in real time. Edge computing is often used together with TinyML in smart devices, drones or autonomous vehicles.

Conclusion 

The world of data and analytics is constantly shifting. As new trends emerge they bring with them fresh thinking on the best ways to put it to work. Moving to a data-driven business model- where decisions are taken based on real time data rather than that ‘gut feeling’ is fundamental to digital transformation in every industry. Data science helps organizations react with certainty in an otherwise unpredictable world.