Key Data & BI Trends from 2025 Events Shaping the 2026 Data Landscape

If one thing was clear from the whirlwind of events this past year, it’s that the data landscape isn’t just evolving—it’s converging. Our team spent the last few months on the road and in the virtual trenches, from dissecting product launches to connecting with partners and peers. This recap isn't just a travelogue; it's a map of the currents shaping our industry and a look at how they directly inform the work we do.

Sigma’s Fall 2025 Launch: AI-Driven BI & Workflow Automation

Sigma's Fall 2025 Launch saw the company weave together artificial intelligence, data analysis, and marketing intelligence into a single, actionable fabric that reflects the real-life needs of elite data teams.

• AI in BI is maturing from novelty to utility. Features like the enhanced "Ask Sigma" focus on accelerating exploration with context and governance, not replacing analysts. This signals a shift toward practical, assistive AI that integrates into existing workflows.
• The line between analytics and operations is blurring. New capabilities in Pixel-Perfect Reporting and Data Apps with workflow logic mean BI platforms are becoming execution engines, allowing teams to trigger actions and automate processes directly from insights.
• The industry is solving for real-world complexity. By addressing entrenched pain points like manual Excel-based reporting and disjointed approval workflows, Sigma’s roadmap shows a focus on reducing operational friction and legacy tool dependency.

Sigma’s EMEA Partner Enablement Tour: Building the Sigma Community

Sigma's EMEA Partner Enablement tour showcased a strategic push in which artificial intelligence, data analysis, and marketing intelligence converge through partnerships and localization.

• Global success requires deep regional investment. Sigma's strategy of local leadership, GDPR-compliant data infrastructure, and extended support hours proves that scaling a modern data platform demands hyper-localized go-to-market and compliance strategies.
• The partner ecosystem is the new competitive moat. Building a connected community of implementation experts is critical for translating a platform's features into tangible business value across diverse markets. This highlights the rising importance of co-selling and knowledge-sharing networks.
• Democratization depends on trust and accessibility. Combining a user-friendly interface with robust regional partnerships will help Sigma (and its users, like the Brooklyn Data team) better serve an international customer base.

Databricks Data + AI World Tour: The Future of Data Work in Atlanta

Databricks is steadily evolving from a data engineering workbench into a comprehensive data and AI platform, and its Data + AI World Tour event in Atlanta shone a spotlight on many features designed to achieve that vision.

• Databricks is evolving into a unified platform for all users. Embedding business intelligence and AI agent frameworks directly into its core serves everyone, from engineers to executives, with an interest in data-driven decision making.
• Analytical and operational data are converging within Databricks, with features like Lakebase aiming to eliminate complex data pipelines by supporting both workloads on a single, governed platform. Databricks is focused on breaking down data architecture silos.
• Open, cross-platform governance is becoming the essential fabric. As demonstrated by Unity Catalog, which prioritizes pragmatic control over data wherever it lives rather than locking it into a proprietary system, allowing external assets to coexist is key to the future of data governance.

Coalesce 2025: Big Moves and Transformation at dbt

Perhaps the biggest news of our conference circuit came at Coalesce 2025, where the dbt-Fivetran merge was officially announced. While this will have long-lasting ramifications in our world, dbt also painted a picture of what’s to come in 2026.

• The modern data stack is entering a phase of maturity and consolidation, with a clear industry shift away from chasing new tools toward building interoperable, cost-efficient foundations that allow technologies to work together.
• Analytics engineering is evolving from building pipelines to enabling AI-driven workflows, where the effectiveness of AI is directly dependent on clean, governed, and semantically rich data foundations provided by platforms like dbt Fusion.
• The landmark Fivetran + dbt Labs merger signals a new era of collaboration in the ecosystem, emphasizing that the future will be built on open standards, streamlined integration, and community-driven innovation.

Small Data, Big Changes

The intimate, practitioner-focused vibe of Small Data SF 2025 revealed a parallel truth: the AI era has hit the industry's reset button, pushing "small data" definitions to 5-10 TBs and demanding a new mindset. The consensus was clear—this is uncharted territory. Success won't come from clinging to old methods but from cultivating curiosity and flexibility. AI will initially create more jobs by amplifying productivity, and its next frontier is moving beyond answering "what" to explaining "why." The future points to AI agents replacing traditional app UIs and AI becoming embedded across all teams, not siloed within them. This signals a profound convergence where data work becomes less about managing scale and more about enabling intelligent, agent-driven action.

What it All Means in 2026

The era of data convergence is here. The artificial walls separating data engineering, analytics, business intelligence, and AI were actively dismantled in 2025. Platforms are no longer content to be a single point in a chain; they are expanding horizontally to become unified environments for the entire data lifecycle. 

Cost Monitoring is King

As data systems and platforms converge into unified data environments, controlling the costs associated with your data will be a paramount task for teams in 2026. When platforms expand their products and services around data pipelines, the cost of leveraging your data will inevitably increase. Finding innovative way to reduce the cost of ownership will set successful teams apart from those falling behind in the next year. Some platforms like Snowflake are already planning for a cost-focused future, with Snowflake introducing cost-monitoring dashboards and queries.

In 2026, data engineering will be less about building isolated pipelines and more about curating intelligent data products within a unified platform. Data analysis will transcend visualization, becoming the direct interface for triggering business processes and automated decisions. The winners will be those who build on interoperable foundations, leverage AI as a native fabric, and empower their communities to activate data in the real world.

Winning data teams need winning partners – and as the data landscape continues to transform, partners who can leverage these new unified platforms will remain leaps and bounds ahead of the competition. As an award-winning partner to Snowflake, dbt, Fivetran, Sigma, and more, Brooklyn Data is ready to help lead you down your path to unified data.