Reflections and predictions on how Data will evolve in 2024, by Siarhei Oshyn, Head of Data at Godel Technologies.

What are the biggest trends in data you have seen over the past year?  

Trends in Cloud-Agnostic Solutions 

Rise of Serverless Architectures 

The cloud is no longer a mere storage space; it’s becoming the beating heart of data infrastructure. Serverless architectures, with their promise of cost-efficiency and scalability, have gained prominence in the data engineering landscape. Organisations are leveraging serverless computing to dynamically scale resources, ensuring optimal performance without the hassle of managing infrastructure. 

Multi-Cloud Orchestration Platforms 

The complexity of managing applications across different clouds has led to the rise of multi-cloud orchestration platforms. These platforms provide a unified management layer, enabling organisations to deploy, monitor, and optimise applications seamlessly across multiple cloud providers. As organisations increasingly adopt multi-cloud strategies, the demand for robust orchestration solutions is on the ascent. 

The Integration of Machine Learning and AI in Analytics Tools 

Automated Data Preparation 

Augmented analytics begins its journey by revolutionising the often time-consuming and complex task of data preparation. Machine learning algorithms are employed to automate the process of cleaning, structuring, and integrating data from diverse sources. This not only saves valuable time but also ensures that analysts can focus on deriving meaningful insights rather than grappling with data wrangling. 

Natural Language Processing (NLP) 

One of the hallmarks of augmented analytics is the integration of natural language processing (NLP). This feature empowers users to interact with analytics tools using plain language. Through NLP, non-technical stakeholders can pose complex queries and receive actionable insights, democratising access to data-driven decision-making. 

Augmented Analytics and Decision-Making Processes 

Smart Data Discovery 

Augmented analytics facilitates smart data discovery by automatically identifying patterns, trends, and outliers within datasets. Machine learning algorithms analyse vast amounts of data at speed, enabling analysts to uncover hidden insights that may have been otherwise overlooked. This, in turn, enhances the accuracy and depth of decision-making. 

Predictive and Prescriptive Analytics: 

The integration of AI in analytics tools extends beyond descriptive analytics to predictive and prescriptive analytics. Augmented analytics leverages predictive modelling to forecast future trends and prescriptive analytics to recommend optimal actions. This foresight empowers decision-makers to proactively address challenges and capitalize on emerging opportunities. 

Has anything surprised you in your technology this year? 

Convergence of AI and Explainability in Data Analytics 

In the realm of Data Analytics, the fusion of artificial intelligence (AI) and explainability has been a surprising and welcome development. As AI models become increasingly sophisticated, there has been a growing concern about their ‘black box’ nature—making it challenging to interpret their decisions.

The surprise of 2023 lies in the concerted efforts to integrate explainability into AI models. This not only addresses ethical concerns but also enhances trust and transparency in data-driven decision-making. 

Broader Adoption of AI Technologies 

OpenAI released new tools or APIs, there was increased adoption of these technologies across industries. Businesses and developers leverage OpenAI’s solutions to enhance their applications and services. 

What are your tech predictions for 2024?  

Integration of Machine Learning Operations (MLOps) 

MLOps practices will mature, facilitating the seamless transition of machine learning models from development to deployment. This includes enhanced collaboration between data scientists, data engineers, and IT operations. 

Explainable AI (XAI) Becomes Standard 

Explainability in AI models will gain more prominence, with organizations prioritizing transparency and interpretability in machine learning algorithms, especially in sensitive applications like finance and healthcare. 

Ethical AI and Responsible Data Science 

Increased emphasis on ethical considerations in data science, with organisations adopting frameworks for responsible AI. This includes addressing bias in algorithms, ensuring fairness, and respecting privacy in data-driven applications. 

Advanced Data Governance and Compliance 

In the wake of increasing data regulations and privacy concerns, 2024 is expected to see a significant focus on advanced data governance and compliance. Organizations will invest heavily in robust frameworks and technologies to ensure the ethical and lawful handling of data. This includes the implementation of granular access controls, encryption protocols, and auditing mechanisms to safeguard sensitive information. 

Rise of Responsible AI Audits 

With the increasing integration of AI in various aspects of business and society, there will be a growing need for audits focusing on the responsible use of AI. Organizations will conduct regular assessments to ensure that AI systems adhere to ethical guidelines, avoid biases, and align with regulatory frameworks. Responsible AI audits will become a standard practice to maintain trust and accountability in the deployment of AI technologies. 

Human Augmentation in Data Analysis 

The role of humans in data analysis will undergo augmentation with the integration of advanced technologies. Augmented analytics will not only automate routine tasks but also enhance the cognitive abilities of analysts. Machine learning algorithms will assist analysts in uncovering insights, and advanced visualization tools will make data interpretation more intuitive. This human-machine collaboration aims to leverage the strengths of both to achieve more accurate and impactful data-driven decisions.