Joining Data Across Siloed Systems Without an Engineering Ticket

TL;DR

• Data silos still get in the way of enterprise analytics teams trying to deliver timely insights.
• Even when data is already in the platform, analysts lose hours to extra transformation, validation, and back-and-forth across teams.
• AI-powered self-service lets analytics teams build cross-system joins in their own workflows instead of filing tickets to data engineering.
• Prophecy supports workflow creation on a drag-and-drop visual canvas, sped up by multiple AI agents.
• Built-in governance scales better than the ungoverned spreadsheet workarounds analysts fall back on, and workflows can run on cloud data platforms.

It's Wednesday afternoon, and the analysis is due Friday. You need customer data from Salesforce joined with financial actuals, plus campaign spend from your marketing platform. The data is already sitting in your cloud data platform. Data engineering ingested and modeled it, but you still need to join it, shape it, and explore it for this specific question. So you file a ticket. One week. Maybe three. By the time the data finally lands, the business has moved on, or someone made the call from a spreadsheet stitched together.

AI-powered self-service changes that pattern. This article focuses on analytics workflows, meaning the transformations, joins, and ad hoc work that sit on top of governed data and feed into analysis, rather than the ETL pipelines that data engineers own and that remain the primary way data lands in cloud data platforms like Databricks, Snowflake, or BigQuery.

The silo problem keeps getting worse

Even when data engineering does its part well, analytics teams still hit silos the moment they try to combine data sources for a specific question. The costs show up in a few familiar places:

• Integration overhead: Analytics teams spend time stitching together data from multiple sources before any real analysis can start.
• Quality issues: Inconsistent definitions across sources chip away at trust in downstream reports and create friction for analytics work that depends on data standardization.
• Time lost: For analysts, the cost that stings most is the time spent waiting instead of analyzing.

Analytics teams then turn that data into insights through their own workflows, additional analytics transformations, ad hoc queries, and analysis. From there, BI tools take over for dashboards and reporting; they're powerful for visualization and exploration, but they depend on well-prepared data sets.

Where your hours actually go

Data discovery, extra transformation, and access requests eat into the hours analysts could be spending on actual insights. The pattern usually looks something like this:

• Transformation for analytics: Even after ETL, analysts still need to reshape, join, and validate data for the specific business question or reporting need.
• Tool sprawl: Jumping between disconnected platforms creates friction and adds to analytics engineer burnout.
• Coordination tax: Aligning with data engineering, data owners, and stakeholders takes hours that never make it into the final deliverable.

When access is too hard or too slow, teams sometimes skip the data altogether and just make the call.

The engineering queue isn't going to clear itself

In ticket-based workflows, analysts file a request and wait. Dashboard and custom-report requests can sit in the queue for weeks, and the backlog doesn't clear on its own:

• Maintenance crowds out new work: Data engineering capacity gets absorbed by keeping existing ETL pipelines running, leaving little room for analytics requests on top.
• Headcount doesn't fix it: Backlogs tend to grow no matter how many engineers you add, because demand expands along with capacity.
• Wait time compounds: Every handoff adds delay, and the business decision often happens before the data ever lands.

When analytics teams can do the join and transformation work themselves on top of already-governed data, the wait time drops. But self-service only works when AI makes it fast and governance keeps it safe.

How the spreadsheet workaround makes everything worse

When analysts can't get joined data through official channels, they build it themselves. They turn to spreadsheets, legacy desktop tools, or whatever unsanctioned option gets the job done fastest.

Ad hoc analyst joins can weaken governed self-service practices in four ways:

• Access control visibility disappears: Analysts can't follow the company's data governance standards when working on legacy desktop tools, and auditing becomes more difficult.
• Data lineage breaks: The documented trail from source through transformation to consumption doesn't exist for spreadsheet-based integrations.
• No data contracts exist: Engineering-managed ETL pipelines carry defined schemas, quality expectations, and freshness guarantees; ad hoc integrations carry none of that.
• Compliance violations occur invisibly: GDPR Article 83(5) penalties can reach up to 4% of total global annual turnover.

Stale upstream data also makes downstream models, metrics, and dashboards unreliable, and once trust starts slipping, every output that follows feels it.

How Prophecy removes the ticket from the analytics workflow

Prophecy enables AI-powered self-service analytics workflows. Once data is in a cloud data platform like Databricks, Snowflake, or BigQuery, analytics teams can prepare it, join it across systems, and transform it confidently without filing tickets. ETL stays the primary way data lands in the platform, and Prophecy picks up from there. Multiple AI agents handle data preparation, while human review, standardization, and Git versioning keep every workflow auditable end-to-end.

Visual joins on a drag-and-drop canvas

Prophecy's interface lets analysts build visual workflows by connecting building blocks called Gems on a drag-and-drop canvas. The Join Gem supports the join types available in the SQL warehouse.

Two expression modes are available:

• Visual expression mode: Point-and-click column matching lets you map join keys without writing code.
• Code expression mode: Direct SQL input is there for analysts who prefer to write joins by hand.

Common use cases include adding customer details to order records, combining user activity with account information, enriching data sets with lookup tables, and matching records between systems. For instance, a revenue operations analyst building a churn-risk view can drop a Join Gem onto the canvas, connect Salesforce account records to Zendesk support tickets on account ID, and layer in a filter Gem to scope the result to enterprise customers with open severity-one issues, all without writing a line of SQL. Once the resulting data set lands back in the cloud data platform, BI tools can pick it up for dashboards and reporting.

Agentic AI for analytics workflow generation

Prophecy v4 introduced agentic AI features that bring multiple specialized agents into a three-step flow:

• Preparation: You describe a business goal in natural language, and a preparation agent reads the Knowledge Graph to draft a visual workflow with joins, filters, and other transformations.
• Review: A review agent surfaces the draft as a visual workflow you can inspect and refine step-by-step.
• Analysis: An analysis agent works alongside you as you pull insights from the prepared data set.

Picture a marketing analyst typing, "Show me last quarter's paid campaign spend joined with pipeline generated by source, broken out by region." The preparation agent reads the Knowledge Graph, identifies the right tables across the marketing platform and CRM, infers the join keys, and drafts a visual workflow that the analyst can review and tweak before running, no ticket required.

Agents infer join keys and transformation logic, and you can always specify them by hand when needed.

Governed self-service that the platform team controls

Every workflow Prophecy generates produces code that goes into Git, and the workflows analysts build are the same artifacts that run in production. Compute, governance, and security all stay in your stack. Data engineering keeps full ownership of ingestion, ETL, and data management, while analytics teams stop waiting in line for the joins and extra transformation they need on top. Prophecy fits alongside the rest of your data tooling, including BI platforms, catalogs, and orchestrators, rather than trying to replace any of it.

Here is how the built-in governance covers the controls enterprise teams expect:

• Role-based access control (RBAC): Granular permissions decide who can view, edit, and deploy workflows.
• Single sign-on (SSO): Identity is managed through your existing provider.
• Audit logging: Every change is captured for review and compliance reporting.
• Compliance certifications: Prophecy meets industry-standard requirements out of the box.

For analytics leaders building a business case, Prophecy takes the engineering handoff off the analytics critical path while keeping governance controls in place.

Migrating off legacy desktop tools

If you have workflows you're trying to pull into a cloud data platform, Prophecy's transpiler makes migrations from tools like Alteryx more straightforward. Teams often use it to consolidate legacy desktop workflows into a governed, cloud-native environment without retraining their entire team or undertaking a full rip-and-replace project. Analysts keep working in a familiar visual canvas while the workflows themselves move into the warehouse you already run on.

Deploy where your data already lives

The free Starter Edition runs on Prophecy's infrastructure, so analysts can get started without provisioning anything. Teams that need to run workflows on their own cloud data platform can move to the Enterprise Edition, which supports native deployment to Databricks, Snowflake, and BigQuery for SQL workflows.

The analyst role is shifting, and the tools should too

Enterprise applications now ship with task-specific AI agents, and agentic AI is reshaping analytics workflows. Analysts who can join and transform data across systems on their own, with AI acceleration and inside governed guardrails, will deliver faster and with fewer dependencies. Analysts stuck in engineering queues will keep watching deadlines slip past.

Joining data across systems with Prophecy

Analysts need joined and transformed data fast, data engineering queues are full of higher-priority ETL work, and spreadsheet workarounds break governance. Prophecy resolves that trade-off as an AI data prep and analysis platform, letting analytics teams build cross-system joins on a governed, visual canvas while multiple AI agents turn natural-language prompts into production-grade workflows. Speed, independence, and control stop being mutually exclusive.

Here's how Prophecy supports that shift:

• Agentic AI features: Multiple AI agents handle preparation, review, and analysis, generating visual workflows with join keys and transformation logic inferred from the underlying Knowledge Graph.
• Visual interface and code: A drag-and-drop canvas with reusable Gems lets analysts build joins and transformations without writing SQL by hand, while the code underneath stays open for review.
• Workflow automation: RBAC, SSO, audit logging, and Git-backed version control keep every workflow auditable and ready to run on a schedule.
• Cloud-native deployment: Native support for Databricks, Snowflake, and BigQuery means Prophecy runs where your data already lives.

With Prophecy, your team can build production-ready workflows faster, without trading speed for governance or pushing every cross-system join into the engineering queue. Book a Demo to see Prophecy's AI agents build your first cross-system join in minutes.

FAQs

Why do engineering tickets slow down analytics work?

Data engineering teams juggle limited capacity across ETL maintenance, ingestion, governance, and ad hoc requests. When analytics requests land in the same queue, they often wait weeks behind higher-priority work, which delays decisions and pushes analysts toward unsanctioned workarounds.

How does Prophecy keep self-service governed?

Every workflow Prophecy generates is stored as code in Git and inherits role-based access control, single sign-on, and audit logging. Analytics teams work inside the same guardrails data engineering already enforces, so self-service doesn't trade speed for control.

Can Prophecy help migrate workflows from Alteryx?

Yes. Prophecy's transpiler converts existing Alteryx workflows into governed, cloud-native workflows that run on Databricks, Snowflake, or BigQuery, so teams don't have to rebuild from scratch or retrain analysts to make the move.

Why not just use a general-purpose AI coding tool?

General-purpose AI can write code quickly, but every analyst prompts it a little differently, so the output drifts in naming, structure, and quality. Prophecy pairs agentic AI features with human review, standardization, and Git versioning, so workflows stay consistent and auditable across the team.

Which cloud data platforms does Prophecy support?

The Enterprise Edition deploys natively to Databricks, Snowflake, and BigQuery, with support for SQL workflows. Capability details vary by platform, so teams should confirm specific feature support with Prophecy directly.