What Is Data Orchestration?
Data orchestration means acquiring, cleaning, matching, enriching, and activating data across your tech stack so revenue teams can act on accurate, unified information. It captures data from multiple sources, transforms it into standardized formats, and routes it to the systems where sales and marketing teams need it most.
The orchestration layer sits between your data sources and the systems that use that data. It coordinates data movement, transformation, and activation across your entire tech stack. Rather than relying on individual tools to manage their own data flows, orchestration pulls from multiple sources, centralizes processing, and activates data for downstream use in CRM, marketing automation, and sales engagement platforms.
At its core, data orchestration performs three essential functions:
Collect data from disparate sources across your tech stack
Transform it into usable, standardized formats
Activate it across GTM systems where teams need it
How Data Orchestration Works
Maintaining actionable data is a symbiotic process with several steps. Here's how orchestration coordinates data flows from collection through activation:
Step 1: Organize and Collect Data
Orchestration starts by connecting to and inventorying data across your systems. This includes identifying what data exists, where it lives, and cataloging it for processing. The collection phase pulls records from multiple sources into a centralized repository where transformation can begin.
Common data sources include:
CRM platforms (Salesforce, HubSpot)
Marketing automation tools (Marketo, Pardot)
Event and webinar platforms
Third-party data providers
Step 2: Transform, Validate, and Enrich
Once data is collected, orchestration transforms it into a usable state. This is where data gets cleaned, deduplicated, normalized, matched to accounts, and enriched with additional fields. The transformation phase ensures data meets quality standards before activation.
Key transformation operations include:
Cleanse: Increasing the number of usable records in your database depends on starting with a clean data set. Clean data means it's formatted correctly, complete, and up to date. This step resolves inconsistencies using normalization tools.
Dedupe: Prevents duplicate data from disrupting sales and marketing teams. Whether it's data uploaded from trade shows, list buys, or webinar leads, record merging and duplicate prevention logic form an important part of the orchestration process.
Standardize: Also referred to as normalizing, data standardization uses rules, templates and field mapping to refine data into standard taxonomies. This ensures that the formatting of any incoming data complies with your systems. For example, you can run a workflow to standardize phone numbers in batches to ensure auto-dialers return a higher contact rate. Standardization makes lead routing more efficient, resulting in fewer lost opportunities.
Match: Link contacts to accounts so new leads connect to existing account records for proper assignment and context.
Enrich: Multi-vendor, real-time enrichment triggers give teams additional information on prospects. As you import a list, any critical data points that are missing (like phone number, region, or job title) can be populated automatically. The enrichment process of data orchestration results in better lead-to-account matching.
Step 3: Activate and Deliver to Downstream Systems
Once data is transformed, it flows to the systems and people who need it. Data gets segmented into audiences, routed to reps, and protected by rules that prevent future data issues. Activation ensures the right data reaches the right place at the right time.
Activation operations include:
Segment: Segmentation tools increase the ability to execute multi-channel go-to-market strategies by categorizing and filtering data into clear buyer personas, sales territories, scores, job role, industry classification. This empowers teams with precision targeting capabilities to identify new opportunities and run hyper-targeted campaigns.
Route: Once your data is cleaned, deduped, enriched, and segmented it has to be sent somewhere. How effectively it's routed to the correct people or queues depends on orchestrating the right routing rules. For example, leads for specific industries can be automatically sent to the reps that specialize in their needs. Or if an enterprise-level account comes in, it can be sent to the best rep to handle a customer of that size.
Prevent: Data orchestration prevents future data inaccuracies from entering the system by using customizable workflows to automatically identify and resolve duplicates, normalize incoming data, and enrich fields with specific sources.
Data Orchestration vs. Automation: What's the Difference?
Automation and orchestration often get confused, but they serve different purposes in your data operations.
Automation handles single, repetitive tasks. It executes a specific action when triggered: auto-send an email, update a field, create a task. Automation is powerful for eliminating manual work, but it operates in isolation.
Orchestration coordinates multiple automated tasks and data flows across systems. It's the coordination layer that manages when, where, and how data moves through your tech stack. Orchestration includes automation as a component, but it also manages dependencies, sequences, and system-to-system handoffs.
Here's how they compare:
Aspect | Automation | Orchestration |
|---|---|---|
Scope | Single task | Multiple tasks and systems |
Focus | Execution | Coordination |
Example | Auto-enrich new lead | Enrich, dedupe, score, route, sync to CRM |
Think of automation as individual musicians playing their parts. Orchestration is the conductor making sure everyone plays together in the right sequence.
Data Orchestration vs. ETL: Key Differences
Data integration is the process of automatically centralizing data to provide one single source of information. This makes data more accessible to the teams and systems that need it, including a data orchestration system.
ETL (Extract, Transform, Load) is a specific data integration pattern focused on moving and transforming data from source systems to a destination, typically a data warehouse. ETL extracts data, transforms it into a structured format, and loads it into storage.
Data orchestration is broader. It includes ETL as one component but manages the entire data lifecycle across all systems. Orchestration involves the coordinated, automated process of cleaning, enriching, and routing data based on customizable rules and workflows. It prepares and organizes data in order to extract value. By ensuring data is error-free, formatted properly, and mapped to the right fields in each platform, data orchestration is the multi-step process that gets data ready to fuel more effective GTM execution.
ETL gets data into your warehouse. Orchestration gets it into the hands of your revenue teams.
Common Data Orchestration Challenges
The process of merging, cleaning, and updating a database is typically a time-consuming activity for IT and data analyst teams. Even with extensive time investments, their efforts could still result in significant inaccuracies without a system to automate and manage it all.
With proper data orchestration techniques, organizations are creating end-to-end data management processes that help teams quickly access and use information to fuel their job. Here are the most common challenges teams face when attempting to orchestrate data:
Integration Complexity
Connecting multiple systems is the first hurdle. CRM, marketing automation, data providers, and engagement tools each have different schemas, APIs, and update frequencies. When integrations break down or fail to sync properly, data problems multiply.
Common data inconsistencies include:
Dirty data
Inconsistent data
Too much data
Duplicate data
Incomplete data
Inaccurate data
Decaying data
Insecure data
Data orchestration resolves these inconsistencies and delivers engagement-ready data for go-to-market motions.
Data Quality and Decay
Organizations often face fragmented data sets sprawled across their tech stack. This siloed data blocks accurate buyer prioritization and engagement. Without orchestration to unify and maintain records, quality degrades continuously.
Common decay factors include:
Job changes (contacts move to new companies)
Company changes (mergers, acquisitions, closures)
Contact information going stale (phone numbers, email addresses)
Records falling out of sync across systems
Breaking down data silos and transforming data into usable intelligence requires a unified system of record. The ideal data orchestration system will solve this lack of interconnectivity within your data ecosystem by creating a centralized approach to acquiring, processing, and activating data.
Governance and Compliance
Maintaining data governance across systems creates significant risk. Without orchestration, opt-outs may not sync between platforms, audit trails become fragmented, and compliance risk increases.
Key governance challenges include:
Opt-out syncing across all connected systems
Audit trail management for data changes
Privacy regulation alignment (GDPR, CCPA)
The risk of hefty fines is reduced because all connected systems can be configured to conform to data and privacy regulations, ensuring that not just an individual's specific email address is opted out, but also their entire profile, across all platforms or instances.
ZoomInfo: Accelerate Your PipelineData-driven solutions that connect your business to buyers at every stage.Get a Demo
Benefits of Data Orchestration for GTM Teams
The benefits of data orchestration touch almost every part of a data-driven business. Government and industry compliance, sales and marketing alignment, forecasting, and cost savings are only a few. Here are the most impactful benefits for go-to-market teams:
Eliminate Data Silos Across Sales and Marketing
Unified data means sales and marketing work from the same view. Clean, consistent records enable accurate segmentation and eliminate conflicting information. Your CRM becomes a system of insight, not a burden.
Key benefits include:
Unified customer view: No more conflicting records or missed accounts across GTM systems
Accurate segmentation: Target campaigns using any criteria unique to your business, including vertical, persona, and company news
Shared market visibility: Total addressable market data accessible to all teams
Improve Data Quality and Freshness
Clean, current data drives better targeting, higher reply rates, and accurate attribution. It improves inbound lead generation, ensures more qualified meetings, and moves prospects through the buyer journey faster.
Quality improvements translate to:
Better targeting: Accurate firmographics for precise audience selection
Higher contact rates: Verified phone and email data that connects
Accurate attribution: Campaign performance tied directly to revenue outcomes
Accelerate Time-to-Insight
When data is orchestrated and accessible, teams act faster. No waiting for IT to pull reports or manually stitch data together. Every team member has the data they need to make informed decisions.
Speed advantages include:
Faster lead follow-up: Automated routing eliminates manual handoffs
Real-time campaign adjustments: Act on performance data immediately
Immediate signal access: Buyer intent data triggers outreach instantly
Enable Scalable Workflows
Orchestration replaces manual processes and multiple point solutions. As data volume grows, workflows scale without adding headcount. Compliance stays consistent across systems.
Scalability benefits include:
Reduce point solution costs: Consolidate tools through unified orchestration
Automate manual work: Eliminate data tasks that don't scale
Maintain compliance at scale: Consistent governance across all systems
Data Orchestration Use Cases for GTM Operations
The way you orchestrate data can look different depending on what you want to do. Here are some real-life examples of how teams use data orchestration in action to achieve their goals:
CRM Enrichment and Record Hygiene
You run a data quality report and find that 46% of your records have blank fields. Orchestration tools like Enrich Premium complete your database automatically by mapping fields to the most relevant third-party data sources.
CRM enrichment isn't a one-time fix. Orchestration runs continuous enrichment workflows to maintain data quality as contacts change jobs, companies get acquired, and phone numbers go stale.
Lead-to-Account Matching and Routing
For most go-to-market teams, speed-to-lead is the name of the game. Qualifying and routing leads to the right rep quickly is a key element for success.
When a new visitor comes to your website, data orchestration provides an automatic, rules-based process for identifying, qualifying, and connecting them with the next best step. When a contact from a target account fills out a form, orchestration matches them to the account record and routes to the assigned rep, not a random SDR.
"When you get back to a lead that filled out a form on your website in the first few minutes, you have a 10 times better chance of converting that lead into an opportunity than if you waited an hour or longer," says Henry Schuck, CEO and founder of ZoomInfo.
Intent Signal Activation
Orchestration enables teams to act on intent and trigger signals. When accounts show research intent, receive funding, install new technology, or hit other buying signals, orchestration routes those signals to reps or triggers automated outreach.
Examples of intent signal activation:
Account showing research intent gets flagged for SDR outreach
Funding announcement triggers account tier upgrade and AE assignment
Tech install signal updates CRM record and adds to relevant campaign
Audience Sync to Marketing Platforms
You've conducted a webinar and need an automated way to clean, normalize, and segment your attendee list for immediate follow-up. Using data orchestration to upload and standardize your list accelerates your MQL-to-conversion process.
Common audience sync workflows include:
Event attendee processing: Automatically clean and segment webinar or trade show lists for fast campaign activation
Form enrichment: Deploy real-time matching and data enrichment to supplement form submissions with phone numbers, industry classification, company size, and revenue data
Web activity audiences: Build audiences from site behavior and push them to marketing platforms for targeted campaigns
What to Look for in a Data Orchestration Approach
Not all data orchestration approaches are created equal. Here are the capabilities buyers should prioritize when evaluating tools and platforms:
Data Quality and Freshness
The foundation of any orchestration approach is data quality. Look for continuous verification, regular refresh cycles, and multiple data sources to triangulate accuracy.
Questions to ask:
Does the data refresh regularly or is it static?
Are there multiple sources for verification?
Is there transparency into data sourcing and collection methods?
Workflow Automation Capabilities
Evaluate whether the approach supports automated, rules-based workflows, not just manual data pulls. Look for triggers, conditional logic, and scheduled operations that run without manual intervention.
Questions to ask:
Can you set rules for automatic enrichment?
Does it support trigger-based actions?
Can workflows run on schedules?
Governance and Auditability
Consider how the approach handles compliance, opt-outs, and audit trails. Data operations need to be traceable and compliant with privacy regulations.
Questions to ask:
Are opt-outs synced across systems?
Is there an audit trail for data changes?
Does it support privacy regulation requirements (GDPR, CCPA)?
GTM System Integrations
Prioritize approaches with deep integrations into core GTM systems. Native connectors reduce implementation friction and improve data flow reliability.
Key integration categories:
CRM (Salesforce, HubSpot)
Marketing Automation (Marketo, Pardot)
Sales Engagement (Outreach, Salesloft)
Build a Data-Driven GTM Engine
Data orchestration helps sales and marketing ops teams make better decisions and drive more business opportunities. A successful strategy includes deduplication, normalization, enrichment, segmentation, and scoring to manage data quality across your systems.
When orchestration works, your GTM engine runs on clean, current, actionable data. Teams spend less time hunting for information and more time engaging buyers. Workflows scale without adding headcount.
Ready to see how data orchestration can improve your GTM operations? Talk to our team to learn more.