The world of data analytics is on the brink of a revolution, with several emerging trends set to redefine how businesses operate and compete. One of the most significant developments is the rise of agentic AI, which performs independent tasks and is expected to be a game-changer in 2025. Additionally, the shift towards cloud-based platforms and a heightened focus on data ethics and governance are reshaping traditional analytics practices. With the estimated amount of worldwide data projected to reach 175 zettabytes by 2025, businesses must adopt advanced analytics tools to handle the volume, variety, and speed of data. Additionally, data exploration tools like Apache Superset and Looker Studio are becoming essential for businesses to effectively analyze and interpret their data, enhancing organizational insights and performance.

Four key trends set to dominate the data analytics landscape in 2025 include predictive and prescriptive analytics, edge analytics for real-time insights, explainable AI, and data fabric integration. These trends offer businesses unique opportunities to gain actionable insights, enhance operational efficiency, and maintain a competitive edge.

Exploring these trends in detail reveals their implications for the future of data analytics.

Cloud-based solutions and the democratization of data analytics are transforming how businesses access and utilize data. Data democratization allows all users, regardless of technical expertise, to access and analyze data, promoting a culture of informed decision-making across organizations.

With the rise of augmented analytics, AI and machine learning are simplifying data preparation and insight generation for users without deep technical skills. This trend is empowering non-technical users to extract actionable insights, bridging the gap between technical and business teams.

This section explores the benefits of cloud-based solutions and data democratization, emphasizing scalability, flexibility, and empowering non-technical users. These advancements are not only making data analytics more accessible but also enabling businesses to scale their operations and make data-driven decisions more effectively.

Retail analytics is the cornerstone of modern retail businesses, providing actionable insights that can significantly enhance customer satisfaction and streamline decision-making processes. Systematic data analysis in retail analytics boosts revenue, cuts overhead costs, and optimizes profit margins. Imagine being able to refine item orders, pricing strategies, and marketing efforts based on solid data rather than guesswork; this is the competitive edge that retail analytics offers.

Savvy retail executives find that retail analytics drives operational efficiency. It streamlines inventory management, prevents overstock and stockouts, and enhances customer loyalty through personalized strategies. Successful retailers leverage customer analytics to synthesize data from various sources, creating a holistic view of their operations. This data-driven approach not only improves profit margins but also fosters a competitive advantage in a crowded market.

Retail analytics involves a comprehensive analysis of sales data, customer transactions, and market trends, enabling retailers to make informed decisions that drive growth and efficiency. Understanding customer shopping patterns and correlating in-store with web analytics enables retailers to enhance customer engagement and optimize business strategies. Ultimately, retail analytics helps retailers synthesize complex data, leading to more effective decision-making and improved overall performance.

The Snowflake platform provides a simple and elastic alternative for SAP customers that simultaneously ensures an organization’s critical information is protected. Snowflake was built from scratch to run in the cloud and frees companies from cloud provider lock-in.

COI began to deliver benefits almost immediately on its launch in 2017. It enabled a new integrated planning process, giving staff across the business full visibility into the supply chain improving the ability to manage their respective operations
in a collaborative environment.

“Everything related to operations is now under COI’s umbrella,” says Nascimento. “It covers the mines, the ports, railroads, shipping and sales and freight negotiations. COI enables planning and optimization across the supply chain.”

Users can now define and build their own dashboards, while corporate dashboards also enable insights and support decisions at board level. COI’s value is neatly encapsulated in Vale’s videowalls, giant room-sized panels featuring custom dashboards that enable cross-functional collaboration. “Everybody’s in the same place,” says Reis.

“They can talk to each other and see the same information on different dashboards updated in near real time. That’s the kind of interaction Qlik is enabling.” 

Nascimento also highlights Vale’s asset monitoring center, which uses a similar and connected operating model to COI that combines with other tools to provide insights into asset lifecycles, enabling preventive maintenance and extending the efficiency and working lives of machinery, plant, vehicles and more. 

“It’s not just about the speed of the decisions, but that we can make different types of decisions,” Nascimento explains. “We can now adjust production in line with logistical capacities, for example. And that’s transformational.”

Your data management processes should minimize production impact and increase efficiency as your data volumes and supporting infrastructure grow. Quantities of hundreds or thousands of data sources affect implementation time, development resources, ingestion patterns (e.g., full data sets versus incremental updates), the IT environment, maintainability, operations, management, governance, and control.

Here again organizations find automation reduces time and staff requirements, enabling staff to efficiently manage ever- growing environments. Best practices include implementing an efficient ingestion process, eliminating the need for software agents on each source system, and centralizing management of sources, targets, and replication tasks across the enterprise.

With Qlik Replicate, your organization can scale to efficiently manage data flows across the world’s largest enterprise environments. Our zero-footprint architecture eliminates the need to install, manage, and update disruptive agents on sources or targets. In addition, Qlik Enterprise
Manager™ (formerly Attunity Enterprise Manager) is an intuitive and fully automated, single console to configure, execute, monitor, and optimize thousands of replication tasks across hundreds of end points. You can track key performance indicators (KPIs) in real time and over
time to troubleshoot issues, smooth performance, and plan the capacity of Qlik Replicate servers. The result: the highest levels of efficiency and scale.

Once data is integrated, it can provide the basis for a broad range of analytics and AI. By supporting these analyses and data science, ISVs and data providers can extend the value of their capabilities and therefore increase their revenue opportunities. Choosing a data integration platform that also supports analytics and AI will make it easier for enterprises to capture this revenue. In fact, our research shows that reports and dashboards are the most common types of analytics used by more than 80% of enterprises. However, when considering analytics providers, look at those that support other newer techniques as well, such as AI/ML and natural language processing, which are projected to be required by 80% of enterprises in the future.

Enterprises need to use data to help drive actions. Data can help them understand what has happened and why, but they ultimately need to process what they have learned and then take action. In many situations, however, there is simply no time to review data to determine what
course of action to take. ISVs and data providers can help their customers derive more value from data by using real-time information to trigger the appropriate actions. 

ISVs and data providers are using technology to add value to business processes. While all business processes typically require data, data integration itself is merely a means to the end. If the process is not done properly, it can detract from the overall approach, so it requires careful design and development. Enterprises should ideally spend their time on core competencies, not on developing data integration technology. By using a full-featured, purpose-built data integration platform, they can ensure that the data needed by ISVs and data providers is robust and available in a timely manner.