Trends shaping the next generation of analytics platforms
Business Intelligence
Jun 10, 2025
Explore how AI integration, real-time data processing, and improved accessibility are transforming analytics platforms for smarter business decisions.
By 2025, analytics platforms are transforming how businesses operate - faster insights, smarter decisions, and AI-driven tools are becoming the norm.
Here’s what’s driving this shift:
AI Integration: 75% of businesses now use AI analytics, with 80% reporting revenue growth. Predictive and prescriptive analytics are turning data into actionable strategies.
Real-Time Data Processing: Companies like Netflix and UPS leverage real-time analytics to optimize operations, save costs, and improve customer experience.
Data Accessibility: Tools like natural language interfaces and no-code platforms make analytics user-friendly, enabling non-technical users to generate insights.
Governance & Ethical AI: With stricter regulations, businesses are prioritizing data privacy and fairness in AI systems to build trust and ensure compliance.
Quick Takeaways
AI is revolutionizing decision-making, with 75% of companies planning to adopt AI analytics by 2026.
Real-time insights are essential - 80% of new apps will include real-time data processing by 2025.
Data tools are becoming simpler and collaborative, empowering teams to make faster, data-driven decisions.
Governance matters - compliance with privacy laws and ethical AI practices is critical for long-term success.
Analytics platforms aren’t just tools anymore - they’re proactive partners in shaping business strategies. Dive into the full article to explore how these trends are reshaping industries and what’s next for analytics.
Data Analytics Trends You Need To Know - 2025
AI-Driven Analytics: Changing How We Work with Data
AI is reshaping how businesses interact with data, making insights more accessible across entire organizations. This shift emphasizes real-time, intelligent analytics that empower every team to make data-driven decisions.
AI-Powered Querying and Natural Language Interfaces
Natural language data interfaces (NLDIs) are transforming how organizations handle data by allowing users to query databases in everyday conversational language instead of relying on technical commands [3]. The numbers back up the trend: the global natural language processing (NLP) market, valued at $29.1 billion in 2023, is expected to skyrocket to $92.7 billion by 2028, with an annual growth rate of 26.1% [2]. By 2025, over 85% of large organizations are expected to adopt NLP technologies [4].
These interfaces rely on generative AI, particularly Large Language Models (LLMs), to eliminate the need for strict syntax or technical knowledge [5]. For example, platforms equipped with natural language query tools let users type questions in plain English and instantly generate visual reports [5]. Querio is one such platform, offering AI-powered data agents that allow anyone - regardless of technical expertise - to ask questions and receive actionable insights.
This evolution paves the way for even more advanced analytics, including predictive tools and agentic AI systems.
Predictive and Prescriptive Analytics
With the advancements in data querying, predictive and prescriptive analytics are taking things further by turning historical data into forward-thinking strategies. Predictive analytics uses past data, statistical models, and machine learning to forecast future outcomes, while prescriptive analytics goes a step further to recommend specific actions [9]. The market for predictive analytics alone is projected to hit $23.4 billion by 2030 [8].
Here’s how they work: predictive analytics focuses on forecasting outcomes using structured data, while prescriptive analytics analyzes a broader range of data to suggest next steps [6][8]. For instance, an e-commerce team could use predictive analytics to anticipate traffic surges, ensuring adequate server capacity while keeping costs in check [7]. However, successfully implementing these tools requires robust data governance, strong encryption, explainable AI, and seamless system integration [6].
Agentic AI: Independent Intelligence in Analytics
Agentic AI represents a leap forward, moving from reactive querying to proactive, independent intelligence. These systems can plan, execute, and adapt to complex objectives - all without constant human oversight [11]. According to a Capgemini survey, 50% of businesses plan to implement AI agents this year, up from just 10% currently. That number is expected to climb to 82% within three years [11]. Similarly, Deloitte predicts that by 2025, 25% of enterprises using generative AI will adopt autonomous AI agents, with adoption reaching 50% by 2027 [12].
What makes agentic AI so powerful? These systems understand business contexts, handle complex or ambiguous queries, and automate multi-step analyses. They also work around the clock to detect anomalies and recommend next-best actions [10]. For example, Amazon employs autonomous agents powered by Kiva robots and machine learning to manage stock replenishment and inventory audits. The result? Lower operating costs and faster inventory management [12].
"Every employee acts as a manager overseeing AI agents" - NVIDIA CEO Jensen Huang [11].
This shift is pushing organizations beyond simply asking what happened. Now, they’re getting answers about what changed, why it matters, and what actions to take next. AI is no longer just a tool; it’s becoming a proactive partner in decision-making.
Real-Time Data Processing: Getting Instant Insights
Real-time data processing brings the speed businesses need to act on information immediately, perfectly aligning with AI-driven insights. Instead of waiting hours or even days for batch reports, companies can analyze data as it flows in. This shift has become a game-changer across industries.
Why Real-Time Data Streams Matter
Real-time analytics is reshaping how businesses operate by enabling decisions based on up-to-the-minute information. Consider this: 80% of businesses now compete primarily on customer experience, and 73% of customers rank experience as a key factor in their buying decisions, just behind price and product quality [14]. Companies relying on outdated data risk falling behind as customers increasingly expect immediate, tailored responses.
This capability allows businesses to spot trends or anomalies as they occur and act right away [13]. For example, in 2023, consumers lost over $10 billion to fraud [14], underscoring the importance of real-time fraud detection systems in the financial sector.
"Unlike traditional data analytics, real-time analytics is about more than informing future decisions; it enables whole new ways of doing business by letting teams take action in the moment." - Jeff Erickson, Tech Content Strategist, Oracle [13]
The impact of real-time data processing is evident in success stories across industries:
Netflix used real-time analytics to grow its revenue from $3.2 billion in 2011 to $33.7 billion by 2023 [15].
Delta Air Lines invested over $100 million in real-time baggage systems, cutting mishandled baggage by 71% between 2007 and 2014 [15].
Macy's streamlined inventory and order management, freeing resources for strategic growth [14].
UPS Capital integrated real-time data to reduce package theft risks, improving decision-making and predictive accuracy [14].
Shopitize, a mobile app, reduced its report generation time from days to minutes by leveraging real-time retail intelligence [17].
As Graham Halling, Commercial Director at Shopitize, explains:
"Tableau has become an integral part of our real-time actionable insights offering, which provides brands with a direct mobile channel to consumers. Tableau helps us learn about their behavior, and we provide consumers with targeted personalized offers based on these real-time learnings." [17]
Achieving this level of responsiveness requires a strong technological foundation.
Infrastructure Needs for Real-Time Analytics
To enable real-time insights, businesses need infrastructure that can handle the rapid collection, processing, and storage of data from multiple sources [19]. This involves three critical components: data sources, processing systems, and storage solutions.
Cloud computing plays a central role in real-time analytics, offering scalable resources on demand. By 2025, over 95% of new digital initiatives will use cloud-native platforms, up from less than 40% in 2021, according to Gartner [18].
Edge computing tackles latency issues by processing data closer to its source, reducing delays and improving security. By 2025, 75% of enterprise-generated data will be processed at the edge, compared to just 10% today, driven by the rise of IoT devices, which Statista estimates will reach 75 billion worldwide by 2025 [18].
Streaming platforms and real-time databases are the heart of real-time architectures. Tools like Apache Kafka, Apache Flink, and Apache Spark Streaming enable continuous data analysis, offering a speed advantage over traditional batch processing [18]. Businesses must choose platforms that fit their throughput and query needs.
These technologies are already driving innovation:
JPMorgan Chase's COIN platform uses machine learning to analyze legal documents in seconds [18].
Walmart processes billions of real-time data points to optimize inventory, reducing out-of-stock items by 45% [18].
Visa evaluates 500 risk attributes in milliseconds with its predictive analytics platform, improving fraud detection by 30% while minimizing false positives [18].
The University of Pennsylvania Health System uses real-time analytics to predict sepsis onset, increasing early intervention rates by 39% [18].
Singapore's Smart Nation initiative processes data from 50,000 sensors to manage traffic and energy, cutting congestion by 15% [18].
BNP Paribas Bank Polska quadrupled conversions with real-time marketing campaigns [22].
"Real-time analytics gives businesses an immediate understanding of their operations, customer behavior, and market conditions, allowing them to avoid the delays that come with traditional reporting." - Simson Chow, Sr. Cloud Solutions Architect at Striim [16]
For companies looking to adopt real-time analytics, success depends on assessing current capabilities, identifying opportunities, and investing in the right tools. This includes real-time stream processing engines, in-memory databases, and robust security measures like encryption for data both in transit and at rest [20][21].
Better Accessibility and Team Collaboration
Modern platforms are reshaping decision-making by focusing on accessibility and team collaboration. Powered by AI-driven analytics, these tools aim to make data accessible to everyone, encourage cross-department teamwork, and speed up decision-making within a more inclusive and data-focused culture.
Simple User Interfaces and Natural Language Tools
Gone are the days when SQL expertise was a must for data analysis. With advancements in Natural Language Processing (NLP), users can now ask plain-language questions like, "What were our top-selling products last quarter?" and instantly get data-backed answers [25].
Generative AI takes it a step further by producing clear, concise data narratives, making even complex insights easy to understand for non-technical users [25] [26].
No-code and low-code platforms empower users to create visualizations, build data pipelines, and even develop AI models without diving into complicated coding. Drag-and-drop interfaces simplify advanced data operations, making these tools accessible to a broader audience [24].
For developers and analytics engineers, AI simplifies platform setup by using natural language to configure interfaces and manage permissions. AI chatbots and virtual assistants also help users navigate large datasets and identify anomalies with ease [23] [25].
These advancements ensure that data tools are not just powerful but also user-friendly, creating a solid foundation for better collaboration across teams.
Collaboration Tools for Team Productivity
Data insights have the most value when they’re shared effectively. That’s why modern analytics platforms now include features like real-time data analysis, interactive dashboards, and collaboration tools [27]. These tools encourage open discussions and knowledge sharing, helping teams make informed decisions together [28].
Shared workspaces, task management systems, and communication channels are becoming standard, streamlining workflows and enhancing teamwork. Features like version control, feedback loops, and centralized data repositories ensure that teams can collaborate without missing a beat [30] [32] [27] [30]. The numbers back it up: 96% of small and medium-sized businesses report that intelligent tools help streamline tasks [29], and the global data analytics market is expected to hit $132.9 billion by 2026 [27].
Collaboration multiplies collective intelligence.
Platforms are also transforming meeting cultures by integrating analytics with real-time interventions, creating more productive and meaningful discussions [31]. These collaborative tools pave the way for AI agents to make data literacy more accessible to everyone.
AI Agents for Data Literacy
AI agents are playing a critical role in improving data literacy across organizations. By enabling employees at all levels to interact with data meaningfully, these tools help unlock insights from unstructured data - an area where only about 10% of enterprise data is currently utilized [33]. AI agents aim to tap into the remaining 90%, making data more actionable for everyone [33].
These tools integrate seamlessly into existing workflows, eliminating the need for complex dashboards or heavy reliance on analysts. For example, Volkswagen Group of America introduced the myVW Virtual Assistant, which uses AI to sift through technical manuals, FAQs, and visual data, allowing drivers to diagnose issues through natural conversations [33].
The importance of data literacy is growing rapidly. A survey found that 79% of organizations believe data will play a bigger role in decision-making over the next year, and 46% of organizations making strides in data-driven approaches are heavily investing in improving data literacy [34].
"Data literacy and data training are very, very important in an organization. It's not just for data analysts or business analysts or data scientists. It has to go all the way to the executive management, right up to the CEO. And the CEO needs to understand the importance of data." – Srinivasan Sankar, Enterprise Data and Analytics Leader, Insurance Industry [34]
AI-driven platforms also personalize data education based on an employee’s role, helping build confidence in using insights effectively [35]. Companies that invest in training their workforce, simplify their data platforms, and reduce reliance on specialists not only accelerate AI adoption but also position themselves as leaders in the next wave of innovation. For instance, IBM's GCDO implemented a unified data platform that delivered $1.3 billion in business benefits and achieved a 10× ROI within just 18 months [34].
"Bridging the gap between AI potential and real-world impact requires a strategic investment in data literacy. Organizations that train their workforce, simplify their database platforms, and reduce dependence on specialists will not only accelerate AI adoption, but they'll lead the next wave of innovation." – Akmal Chaudhri, Senior Technical Evangelist at SingleStore [35]
Data Governance, Privacy, and Ethical AI
With the rise of advanced analytics platforms, the importance of strong data governance and ethical AI practices has never been clearer. Data breaches are not just technical mishaps - they're expensive, with the average cost reaching $4.88 million in 2024. On top of that, 72% of consumers say they would stop supporting companies that fail to protect their data [40][38]. The stakes are high, with noncompliance fines totaling $1 billion in 2024 alone. Meanwhile, the AI governance software market is expected to grow from $890 million this year to nearly $6 billion by 2029 [38][37]. This growth highlights the pressing need for frameworks that ensure innovation aligns with accountability.
Data Governance in Analytics Platforms
Today's analytics platforms demand governance systems that go beyond traditional data management. A concerning statistic: one in three data breaches involves "shadow data", or data stored outside of formal oversight [40]. To tackle this, AI-powered governance platforms are stepping in, offering tools to scan and identify sensitive data across various types [37]. These platforms enable automated data discovery, classification, and the creation of detailed audit trails. They also enforce granular access controls and map data lineage to show how changes impact compliance.
Starting small can be a smart strategy. Focused pilot projects that address specific business challenges often yield quick wins, demonstrating value and paving the way for larger initiatives. These efforts are especially crucial as organizations strive to meet the increasingly strict requirements of privacy laws in the United States.
Meeting US Privacy Law Requirements
The US privacy landscape is fragmented, with state-specific laws setting the tone [39]. Unlike the GDPR, which emphasizes privacy as a fundamental right, US laws often center on safeguarding data for both consumers and employees [36]. Most state privacy laws grant consumers four key rights: access, correction, deletion, and portability of their personal data. California's CPRA goes a step further, granting consumers control over how their data is used in automated decision-making systems [45].
Consumer Rights | States Providing This Right |
|---|---|
Right to Access | All 20 states with privacy laws |
Right to Correct | 18 out of 20 states (Iowa and Utah excluded) |
Right to Delete | All 20 states with privacy laws |
Right to Portability | All 20 states with privacy laws |
Jurisdiction | Opt-Out (Sale) | Limit Use (Profiling) | Limit Use (Targeted Ads) | Limit Use (Sensitive Data) |
|---|---|---|---|---|
California: CPRA | X | X | X | X |
Colorado | X | X | X | Opt-in required |
Connecticut | X | X | X | Opt-in required |
Florida | X | X | X | Opt-in required |
Virginia | X | X | X | Opt-in required |
For businesses, this means being able to locate personal data quickly, verify identities, and process consumer requests efficiently. AI technologies are proving invaluable here, streamlining data discovery and compliance processes [39].
"The key features of privacy software will help companies find sensitive data across their systems, understand how it flows, and handle requests from customers to access their information. Pre-set compliance options for major laws like GDPR will also be a must-have to make regulatory compliance a breeze." - Jedd Macosko, CEO of Academic Influence [39]
To stay ahead, organizations should conduct formal risk assessments, craft clear privacy policies, and implement robust security measures. Regular audits are also essential to keep up with the ever-changing regulatory environment [36][43]. Once privacy compliance is in place, the next challenge is ensuring fairness and accountability in AI-driven decision-making.
Ethical AI: Balancing Progress and Responsibility
As regulatory frameworks tighten, organizations are under increasing pressure to strike a balance between innovation and ethical responsibility. With 72% of businesses now using AI and 78% of consumers expecting companies to prioritize ethical AI practices [44], the spotlight is on transparency, fairness, and accountability.
Bias in AI is a particularly thorny issue. It can creep in during model development or deployment, leading to unfair outcomes. For instance, a news outlet's AI system produced biased summaries due to unbalanced training data, while facial recognition systems have shown higher error rates for people of color. Both cases were addressed by curating more diverse datasets and applying fairness metrics [41].
Addressing these challenges requires a combination of diverse data, algorithmic adjustments to reduce bias, and ongoing human oversight [41][42]. The regulatory landscape is evolving, too. By 2026, half of all governments worldwide are expected to enforce responsible AI regulations. However, only 58% of organizations have taken the first step of conducting AI risk assessments [46]. The EU AI Act, which came into effect in February 2025, has set a global benchmark with penalties as high as €35 million or 7% of annual turnover for noncompliance [44][46].
"AI governance will evolve as quickly as AI itself. The future will involve self-regulation, real-time auditing, and AI that explains its own decision-making processes." - James, CISO, Consilien [46]
To navigate this landscape, companies must perform ethical impact assessments, establish clear governance policies, and focus on explainable AI techniques. Nearly 70% of businesses using AI plan to increase their investment in governance over the next two years. This isn't just about meeting regulations - it's about building trust and ensuring that AI serves everyone fairly [44].
Conclusion and Future Outlook
Analytics are reshaping the way businesses interact with data. With AI-powered tools delivering more advanced insights and real-time processing becoming the norm, companies are gaining a competitive edge. At the same time, improved accessibility is making data analysis tools available to everyone, empowering employees across all levels.
Key Points from Current Trends
Looking at today’s analytics landscape, several trends stand out. According to McKinsey, businesses leveraging AI analytics are making decisions and operating five times faster than their competitors [48]. This isn’t just about speed - it’s about smarter, more informed decision-making.
Real-time analytics has shifted from being a luxury to an essential tool. IDC estimates that by 2025, 80% of new applications will include real-time data processing capabilities [49].
Another game-changer is the growing use of augmented analytics. Gartner predicts that by 2025, 75% of organizations will use these tools, allowing even non-technical users to query data using natural language [49]. This is breaking down silos between data teams and business users, making insights more accessible.
However, as analytics capabilities expand, governance and ethics are becoming critical. Forrester projects that by 2025, 90% of large organizations will establish dedicated AI ethics boards [49]. Responsible AI practices are quickly becoming a cornerstone of sustainable growth.
What's Next for Analytics Platforms
Looking ahead, analytics platforms are poised for even greater transformation. By 2030, experts predict that AI-driven analytics tools will dominate, comprising over 70% of business intelligence solutions [50]. The prescriptive analytics market is also set to grow at an impressive compound annual growth rate (CAGR) of 31.8% from 2024 to 2030 [50].
Agentic AI is on the horizon, promising to automate complex analytics tasks [47]. This advancement will allow platforms to function with minimal human input while delivering increasingly sophisticated insights.
Edge intelligence is another exciting development, bringing AI processing closer to where data is generated. This will enable real-time decision-making, particularly for organizations managing IoT data streams or mobile applications [52].
Emerging technologies like blockchain and advanced cloud computing will further elevate business intelligence capabilities [51]. Simultaneously, "Green AI" practices are gaining traction as companies aim to innovate responsibly, balancing technological progress with environmental concerns [49].
"AI adoption is progressing at a rapid clip, across PwC and in clients in every sector. 2025 will bring significant advancements in quality, accuracy, capability and automation that will continue to compound on each other, accelerating toward a period of exponential growth." [53]
For businesses preparing for this future, the message is clear: the era of gradual adoption is over. With 92% of companies planning to increase their AI investments [54] and the AI market projected to hit $1,339 billion by 2030 [1], early adopters stand to gain the most.
Querio is leading the charge by blending cutting-edge AI capabilities, user-friendly design, and strong governance. Companies that embrace these trends while staying focused on practical outcomes and ethical practices will be the ones to succeed.
The analytics revolution is here, and the time to transform your data strategy is now.
FAQs
How can businesses maintain data privacy and comply with regulations when using AI-powered analytics platforms?
To safeguard data privacy and meet regulatory requirements, businesses should implement a privacy-by-design strategy. This means incorporating data protection measures directly into their AI systems from the very beginning. Key steps include employing advanced encryption techniques, performing regular audits, and being clear about how data is collected and used.
Staying updated on shifting regulations, such as GDPR in Europe and state-specific privacy laws in the U.S., is equally important. With potential federal AI regulations on the horizon, preparing in advance can reduce risks. Taking these proactive measures not only ensures compliance but also strengthens user trust through responsible data management practices.
How do natural language interfaces (NLIs) make analytics more accessible for non-technical users?
Natural language interfaces (NLIs) transform analytics tools by letting users interact with data through plain, everyday language instead of requiring complex queries or technical know-how. This approach opens the door for non-technical users to easily ask questions and quickly discover insights, removing barriers to making data-driven decisions.
By streamlining how people work with data, NLIs significantly cut down the learning curve that comes with traditional analytics platforms. Teams can pose questions conversationally, get straightforward answers, and shift their focus to making faster, informed decisions. This not only makes data more accessible but also enhances productivity, enabling businesses to act on insights with greater ease and efficiency.
What do businesses need to set up real-time data processing and gain instant insights?
To implement real-time data processing and gain immediate insights, businesses need an infrastructure built for speed, efficiency, and reliability. Here’s what that entails:
Data ingestion systems: These are designed to continuously collect and stream data from sources like IoT devices or applications, ensuring minimal delays.
Stream processing frameworks: These tools analyze, filter, and transform data in real-time, allowing businesses to act on timely and relevant insights.
High-performance databases: Handling large data volumes while enabling fast queries and low-latency access is critical for smooth operations.
On top of that, having intuitive analytics tools is key. These tools should make it easy to visualize data and support quick, informed decision-making. To keep up with growing demands, systems must also be scalable and resilient, ensuring operations run smoothly without interruptions. With the right setup, businesses can harness real-time analytics to make smarter, faster decisions.