Contributors
As more organizations embrace the need to reinvent their businesses using AI, data, and tech, the world of data is evolving at enormous speed. A deep understanding of AI is now more critical than ever.
How will data analytics and cloud evolve in 2025?
We caught up with some of the brightest minds at Sigma Computing and BlueCloud to get the inside scoop! They reveal the game-changing trends, the hurdles businesses will face, and the skills leaders need to stay ahead.
- Cloud Platforms Will Adapt for AI – More AI services, better data access, and scalable storage.
- Security Will Rely on Trust & Automation – AI-driven compliance, real-time threat detection, and stronger authentication.
- RAG Will Supercharge AI Analytics – Real-time data ingestion for faster, smarter insights.
- Cloud Success Hinges on Integration – Seamless connectivity, ecosystem collaboration, and ethical AI.
- AI Will Democratize Data Access – Anyone can explore data without deep technical skills.
- Custom Workflows Will Drive Analytics – Personalized, scalable solutions over rigid software.
- AI-Powered Analytics Will Dominate – Automated prep, natural language, and real-time insights.
- Data Chaos Will Become Managed Complexity – Structured, optimized ecosystems for better business value.
Cloud platforms will evolve to meet the requirements of AI-driven workloads
Implementing a long-term, successful AI strategy can be a long and winding road. Many organizations are still figuring out where AI fits into their workflows and identifying the best use cases.
Ruchi Pandya, Product Manager of Data Apps and Use Cases at Sigma Computing, believes this creates many opportunities for cloud platforms to grow, especially in three key areas.
"First, we'll see more AI-specific services such as SageMaker, Azure ML, and others that streamline the AI lifecycle. Second, data accessibility will be a big focus. Cloud providers will likely race to make AI more accessible through low-code or no-code solutions, like what we've seen with Google's AutoML, AWS AI, and Azure AI. Finally, data storage in the cloud will remain a hot topic, as organizations need a reliable way to house and manage the data that trains their AI models," explains Pandya.
Pandya adds that at Sigma, AI is viewed as a partner that enhances the analytics experience. Features like Ask AI enable users to interact with AI to gain valuable data insights.
Governance, trust, and automation will define cloud security
Cloud security will be critical in 2025, fueled by real-time monitoring and stricter access controls. Pandya sees two major themes emerging.
First, governance will become a bigger priority.
"Organizations will become more selective about the data they use, making sure it's properly cleaned and verified before training AI models. This will lead to developing a hierarchy of trust in computing environments, shaping industry trends for years to come."
Abirami Karthikeyan, Data and Analytics Manager at BlueCloud, adds that with increasing regulatory scrutiny and concerns over data privacy, companies will need AI-driven compliance tools to ensure they are using data responsibly. AI will help track data lineage, access control, and security automation.
Second, third-party validation will play a more significant role.
"I believe automation in anomaly detection will be a major investment area. Organizations will rely on unbiased third parties to validate computing environments, data, and model outputs. Also, Open Authorization (OAuth) will be everywhere— an open standard that lets third-party apps access data securely—without users having to share their passwords," explains Pandya.
Finally, Zalak Trivedi, Product Manager, Embedded Analytics at Sigma Computing, emphasizes three key trends that will shape the future of cloud security:
- Stronger authentication – Traditional username-password logins will fade out. Expect widespread adoption of multi-factor authentication and zero-trust models.
- Data encryption – More companies will encrypt their data using their keys. Sigma, for example, recently launched customer-managed keys, allowing organizations to control their encryption for added security.
- Real-time anomaly detection – Organizations will need instant threat detection.
Retrieval Augmented Generation (RAG) will shape analytics in the cloud
RAG is a cost-effective way to introduce new data to large language models (LLMs). Pandya points out that RAG will be a game-changer for cloud analytics.
By allowing users to ingest time-sensitive data without retraining a complete model, RAG changes how teams interact with real-time data in the context of LLMs.
"RAG will make AI more accessible and practical, particularly for last-mile tuning of models that businesses may not have the resources to train themselves. I think we'll see this approach become widely adopted in 2025 as companies seek to improve AI efficiency and responsiveness," explains Pandya.
Karthikeyan points out that RAG and validation mechanisms will help increase confidence in AI-driven insights.
Integration, interoperability, and innovation will be key differentiators among cloud providers
Cloud providers must always be one step ahead in technological advancements.
Pandya thinks integration will be the key differentiator among cloud providers.
"As platforms focus on their niches, they'll gradually take on related capabilities. The top cloud providers will thrive by building strong ecosystems, integrating their services with those of their peers instead of trying to control everything themselves."
Karthikeyan explains, "Cloud providers have already won the debate over on-premises versus cloud. Now, they need to differentiate themselves in three main areas: innovation, interoperability, and trust (ethics)."
Busra Elmaci Uslu, Data and Analytics Leader at BlueCloud thinks AI and Business Intelligence (BI) will be central to differentiation. Pre-trained AI models, advanced data analytics, and built-in BI layers will become standard offerings.
AI is breaking down barriers, making data more accessible to everyone
Today, there's a hierarchy in data management—data engineers process data, analysts interpret it, and business users consume insights. However, we're seeing a shift toward democratizing data access, which means every user—not just data experts—can leverage high-quality data.
"AI is going to make simple things much easier and hard things possible for data engineers, data analysts, and business users alike. We're heading toward a world where anyone can be a data analyst."
Trivedi believes that AI will democratize data access across the board.
Trivedi points out that everyone will become a data analyst. "AI-driven tools are breaking down barriers, allowing users across all levels to analyze data effortlessly. For example, tools like Sigma make analytics accessible to store managers, executives—anyone, really."
With data democratization, people want custom workflows tailored to their specific needs.
"Historically, when you bought software, you had to adapt to its limitations. But in 2025, we'll see more tools that allow users to create personalized workflows without needing deep technical expertise. The ability to customize, personalize, and scale workflows efficiently will redefine how organizations approach cloud analytics," explains Pandya.
AI-powered augmented analytics tools will lead the way
Uslu envisions a future where AI automates data preparation, generates insights, and even explains findings through natural language.
"Imagine a system where you can simply talk to your data, receive automated charts and graphs, and get real-time recommendations. This would free analysts to focus on high-value tasks and drive innovation. We're already seeing companies like Sigma leading the way," says Uslu.
Karthikeyan explains that a critical factor here is self-service analytics.
"In the near future, users will be able to interact with data using natural language queries, similar to how they use Spotify or LinkedIn—without needing technical expertise."
Transitioning from data chaos to managed data complexity
Chaos and complexity are everywhere—today, we are drowning in data. It's stored across multiple databases, documents, and cloud storage solutions. The explosion of data can feel overwhelming, making it difficult to extract real insights while ensuring security and cleanliness.
Trivedi identifies three root causes of the data chaos: explosion of data, fragmented tooling, and evolving stakeholder needs. He also suggests that organizations need three key elements to manage this complexity.
Uslu believes that Data Mesh, observability, metadata management, and catalogs can help reduce data chaos.
"Traditionally, data was handled by a centralized data team. However, Data Mesh decentralizes data ownership, allowing different business teams to manage their data as a product. This ensures better quality control and accessibility."
Data observability gives us an X-ray vision of our data pipelines. Finally, metadata management and data catalogs help maintain organization, clarity, and compliance, making data easily accessible and understandable.
Karthikeyan highlights that customization, collaboration, and agility will be crucial for managing data chaos effectively in 2025.
"Today, organizations no longer must reshape their business processes to fit rigid products. Instead, product companies and customer organizations are working together to develop new customized capabilities, ensuring continuous business value, whether for data teams or end-users."
The winners in this space—Snowflake and Sigma—demonstrate how continuous innovation in data platforms is crucial for handling complexity.
The BI of the future will be actionable, self-service, and multimodal
Luke Stanke, Product Evangelist at Sigma Computing, highlights two key shifts in the future of BI:
- Enabling direct data modification within BI tools
- The evolution of BI into a multimodal platform
"What we're going to see, and what Sigma is pushing toward, is the ability to analyze data and take action within the BI tool itself. That means embedding workflows, allowing users to modify and enrich data directly. This shift will enable more powerful, purpose-built data applications on top of analytics."
Second, on the diverse user needs side, we're moving toward a world where BI tools have to cater to different skill sets.
"Some users are SQL-heavy, some are spreadsheet wizards, and others are more comfortable with Python. The future of BI is multimodal, allowing all these different personas to work together seamlessly on the same platform."
Karthikeyan also argues that one of the biggest transformations will be in self-service BI.
"AI will make data interaction more accessible to non-technical users. With AI, particularly through NLP and generative AI, we will see a true self-service experience where users can simply ask questions and receive meaningful insights."
"The future of analytics is self-service and user-friendly. The BI tools of tomorrow will be as intuitive as social media apps, making it easier for businesses to harness data's power without extensive training."
Semantic layers will drive responsible AI in complex data environments
Stanke believes that one of the biggest trends we'll see in 2025 is the resurgence of semantic layers. "Semantic data layers help ensure consistency, security, and compliance, no matter where or how data is accessed."
Stanke emphasizes that we also need governance frameworks to ensure responsible AI use. That means better data lineage tracking, auditability, and compliance measures to prevent biased or incorrect outputs from AI-driven analytics.
"Organizations will need to balance agility—getting data to users fast—with strong governance policies to maintain trust and security," says Stanke.
2025 will be the year of AI Agents
AI is already changing the way analysts work. Stanke predicts that in 2025, AI assistants will become a standard feature in analytics tools. These assistants will help analysts generate queries, clean datasets, and surface insights faster.
"One of the most interesting developments is agentic AI, which acts as an assistant that can autonomously explore data, test hypotheses, and even suggest next steps for analysis. Sigma has already implemented AI Query, allowing users to interact with data more naturally," explains Stanke.
AI will transform data analysis
AI will automate many tedious tasks that data analysts and scientists currently handle, such as cleaning and preparing data.
"According to recent stats, 90% of data scientists dislike data preparation, so AI's role in streamlining these processes will be significant. This automation will free up analysts' time, allowing them to focus on more valuable tasks like interpreting results and deriving meaningful insights," says Uslu.
AI will also enhance our ability to detect hidden patterns in large datasets—patterns that human analysts might not be able to identify. Moreover, AI-powered tools enable users to ask plain-language questions and receive clear answers, much like a Google search but integrated into BI tools. This will be a game-changer for data analytics.
Which industries will see the biggest cloud and data analytics impact in 2025?
Cloud and data analytics will lead to the most significant transformations in highly regulated industries, and AI-driven analytics will be the catalyst.
"Cloud infrastructure adoption has already taken off in startups and mid-market companies, but now we're seeing large enterprises in sectors like healthcare, finance, and energy finally making the shift," explains Trivedi.
Healthcare: AI-driven analytics will enable more personalized treatments and better diagnoses, optimizing workflow and resource allocation and reducing hospital costs.
Finance: AI-driven analytics help detect real-time fraud and improve personalized financial offerings. For instance, if someone frequently books beach vacations, they might receive tailored credit card offers for travel perks.
Energy: Predictive analytics will revolutionize asset maintenance. AI-powered data insights, like carbon tracking, can reduce unplanned outages and optimize mandatory sustainability efforts.
Uslu believes the most significant impact will be in healthcare, finance, and retail.
"Imagine an Apple Watch that alerts you to potential health issues before they manifest or an AI-driven personalized treatment plan based on your medical history. Also, AI-driven fraud detection will become smarter and more accurate, helping banks combat fraud with predictive models, and Robo-advisors will revolutionize personal finance. Finally, personalized shopping recommendations will become even more sophisticated, leveraging data analytics and AI to anticipate customer preferences."
Karthikeyan adds that marketing, advertising, and media will profoundly transform.
"These industries thrive on data-driven decision-making and are well-positioned to embrace AI advancements. For example, with the rise of generative AI, companies can create hyper-realistic marketing content that blurs the line between reality and AI-generated visuals."
What capabilities will be critical for cloud and analytics teams to thrive in 2025?
Pandya believes that in 2025, cloud analytics teams need to focus on efficiency without compromising accuracy. In a world where data is ubiquitous, analyzing and acting on information fast is critical.
"Velocity and vigilance are key skills analytics experts must develop in 2025. Security threats are evolving, and teams need to be highly aware of metadata, anomalies, and potential risks. Developing a strong mental model to assess trends, get insights quickly, and verify those insights will be a vital skill in 2025."
Karthikeyan believes agility and trade-off analysis will also be critical skills in 2025.
"Leaders and teams need to be flexible, adapt quickly, and continuously refresh their technical fundamentals. On the other hand, they must balance factors like time, cost, and service quality while making data-driven decisions."
Uslu says it's no longer enough to specialize in just one cloud platform like AWS or Azure—you need to be comfortable with multiple cloud providers and hybrid environments. AI and machine learning skills are also a big plus, especially in responsibly building, deploying, and managing AI models.
Beyond technical expertise, Uslu adds that leadership in cloud analytics requires bridging the gap between technical teams and business units.
"Clear communication, collaboration, and an innovation-driven mindset set great leaders apart."
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Recommended resources:
Challenges of AI adoption and how to overcome them, an interview with Bill Tennant
Key Data and AI Trends for 2025
Transforming Decision-Making with a Strong Data Foundation Framework: A Customer Success Story
