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The Top 10 Data and Analytics Trends in 2023

Joanna He
Senior Director of Global Growth
Oct. 03, 2023

The global big data analytics market is projected to grow from $307.52 billion in 2023 to $745.15 billion by 2030. Similarly, 56% of data leaders increased their budgets this year. But what are the latest trends and innovations in this fast-changing field? How can you stay ahead of the curve and leverage the power of data and analytics in 2023 and beyond?


In this article, you will learn about the top 10 data and analytics trends shaping the data & analytics industry in 2023. We will explore artificial intelligence analytics, augmented data management, small data, and customer data platforms. We will also cover their origin, current states, and tools powering these trends to help you build a data-driven business.


AI Analytics


The recent breakthrough in generative AI technologies has created a new way to gather, analyze, and extract insights from data. With the growing need for insights, businesses need a solution to analyze big data, especially unstructured data like pictures, voice, navigation data, and more. AI analytics creates simplified solutions that can quickly analyze large volumes of data and deliver quality results. It uses machine learning to process large amounts of data to identify patterns, trends, and relationships for driving business growth.


This has shaped how we interact with data and the information we can extract. It's also creating a new era of data democratization, where everyone in the organization can access data insights without having to go through IT or have extensive technical knowledge. Data analytics tools like Kyligence Zen embody AI analytics with features to identify root causes in record time under multiple data sets. This tool also serves as a centralized place for all business metrics , this provides easily digestible context of your business to the built-in AI copilot to assist any business users with data insights. thus fostering collaboration and productivity.


Natural Language Processing


Natural language processing (NLP) is a field that draws from linguistics and machine learning applications. It's gained prominence in the chatbot-creation industry due to machines needing to understand human language. Currently, NLP has evolved into having implementations in the data analytics space. There are data analytics tools with natural language processing capabilities, like the Kyligence Zen copilot feature.


These tools enable you to chat with data, like talking to a data analyst. The advancement of NLP from a theoretical concept into a practical implementation enables these tools to interpret human language input and deliver quality insights.


Kyligence Copilot Demo Video


It's as easy as typing, "Why are our sales down for Q2? ''and clicking "enter," and the analytics tools explain in human language. With generative AI like ChatGPT becoming mainstream and other new implementations, there's more to expect in this field of data analytics implementation.


Embedded Business Intelligence


The idea of embedded business intelligence stems from the need to simplify access to data-driven insights. Typically, businesses must rely on third-party tools separate from their business infrastructure to generate data and analytics. This option creates a decentralized and bulky tech stack management issue.


However, with embedded BI, you integrate data analytics directly into business applications, enhancing decision-making at every level. For example, Strikingly empowered 100,000 website builders with seamless built-in analytics using Kyligence as its embedded analytics.

 Image source: Strikingly website traffic metrics analysis screenshot

This feature allows website users to access Google Analytics-like website traffic analytics without setting up extra software. It's a fast, efficient, and cost-effective way to get AI Copilot, data query engine, and visualization all in one.


Data Catalog


A data catalog is like a Google for enterprise data. It offers a centralized metadata repository, helping users discover and understand different datasets. You can access everything from one place instead of having data scattered throughout other tools or databases.


Beyond the data governance capabilities, it also offers a secure way to centralize data without multiple layers for users to scale through. This trend has shaped how businesses manage data, offering a data analytics management setup for easy collaboration.


As the Business landscape becomes more competitive, more volumes of data will be generated. Subsequently, more companies embrace data catalog tools like Alation, Select Star, and Collibra to get quick data insight and establish data governance and security.


Augmented Data Management


The data management process is more time-consuming with the increasing volume and diversity of data companies generate. Consequently, data engineers spend hours on less productive tasks to deliver quality data. This doesn't only create bottlenecks; it also leads to employee fatigue, and most companies cannot hire more help. Augmented data management is a trend pivotal to recent data analytics tasks' efficiency, productivity, and simplicity. It combines AI and ML to provide solutions to automate and improve data management tasks that are traditionally performed manually.


The rise of AI has helped to create augmented data management tools like Kyligence, enabling users to automate most data analytics tasks. Consequently, more businesses can get valuable insight at a lower cost and more efficiently.


Low-code / No-Code


The Low-code/ No-code trend has been transforming the development and software engineering industry for years. However, it's found its way into the data analytics field. The need for low-code / No-code metrics platforms is built on business centralization and simplifying data analytics processes.


With Low-code/no-code implementation, companies can quickly access intuitive drag-and-drop interfaces and make data analysis workflows. It creates a decentralized system where non-technical and technical stakeholders can understand and gain insight from data. Companies can now focus on critical actions rather than data flow generation.


Small Data


The Big data conversation has been at the forefront of analytics for years. It was built on the idea or projection of a possible data explosion in the future. However, with the increasing data volume growth comes sophisticated hardware solutions. Additionally, in a post by Jordan Tigani, a former engineer on Google BigQuery, now Founder & CEO of MotherDuck, he makes a case that big data wasn't much of a threat.


In the article, he highlights insight from his time working on big data projects. He said that contrary to popular belief, most enterprises didn't have extensive data nor used a big chunk of their storage facilities.


Looking at the analysis and findings from the article, it’s safe to say companies are reverting to the age of managing small data. This is so because, majority of businesses deal with data in smaller volumes with the exception of large language model development companies that need large volumes to train their algorithm. Consequently, more companies focus on managing small data, as they handle and have the need to digest small data on a more frequent basis.


Is Hadoop Dead?

Interest over time

Hadoop is a methodology whose existence aligns with the big data movement. However, recent Google trends show a dwindling interest in Apache Hadoop, especially in the big data field. The field has evolved to a space with simpler, powerful and cloud-native alternatives, such as Apache Spark. The future of Hadoop is uncertain, but it is still one solution that almost all businesses and industries trust and are familiar with. Some experts predict that Hadoop will continue to be used in conjunction with other big data technologies. Regardless of the future of Hadoop, users are now considering cloud-native solutions and the separation of storage and computing as the standard capability for any big data technology to be adopted.


These are areas where Hadoop has limited functionalities. However, Hadoop is still alive and used by many of its early adopters. However, it’s difficult to ascertain the level of relevance it still has when emerging technologies become widespread.


Reverse ETL


According to research, 47% of marketers find it hard to access information due to data silos. Similarly, 51% of sales experts aren't satisfied with how their organization provides customers' data. Both reports give an overview of the difficulty experienced by non-technical employees in accessing data. With the business landscape becoming more dependent on data-driven insights, it's dangerous to have those bottlenecks. The reverse Extract Transform Load (ETL) trend returns data from warehouse setup to operational systems. This unique approach empowers organizations to leverage insights more effectively, bridging the gap between analytics and action. This trend will continue since data governance and democratization are becoming increasingly crucial for efficiency. Some companies powering the reverse ETL trend include Polytomic, Hightouch, and Census.


Customer Data Platform


According to reports, the customer data platform industry will hit $19.7 billion by 2027. The main driver of adopting these solutions is the need for businesses to deliver better customer experience. To achieve this feat, companies must be able to analyze customers' interests and interactions across all channels. The result is a complete overview that supports better customer support and customized messaging. The customer data platform provides a solution with a unified overview of all customer interactions and multiple touchpoints. Considering that customer satisfaction is now a significant part of a business's competitive advantage, we expect this trend to remain relevant for longer, with some modifications and variations. Some tools powering this drive include Amplitude CDP, Insider, Klaviyo, and Bloomreach.




Data and analytics are evolving thanks to many innovations and transformations. In 2023, the demand for more insightful and quality data has increased, and these ten trends have been at the forefront, shaping how organizations collect, process, and utilize data to gain a competitive edge. Whether it's harnessing the power of AI analytics, leveraging NLP for better communication, or embracing low-code development, staying abreast of these trends is crucial for businesses looking to thrive in the data-driven future.


Ready to improve your business growth with data-driven insights powered by AI and ML? Try Kyligence for free today.