RevOps leaders in enterprise organizations often face the same paradox: more data, less clarity. Every system has an owner, but no one is clearly accountable for alignment across the revenue engine. Aligning CRM, ERP, billing, and compensation systems isn’t just a technical integration task; it’s a governance mandate.
In many cases, the problem doesn't stem from data quality issues. Instead, it's often fragmentation caused by complex CRM, ERP, and billing environments that define revenue differently.
Examples include activity in HubSpot, the compensation history in your legacy system, territory definitions in a spreadsheet someone created two years ago, and product data in your enterprise resource planning (ERP) system.
Even with integrations in place, these systems rarely align cleanly, and your team can still spend days or weeks reconciling definitions, hierarchies, and mappings by hand.
Often, manual data processes don't just slow you down. Sometimes, they can limit your ability to act when you need to adjust quotas, rebalance territories, or test new compensation models. This is often because each change triggers a fresh round of extracts, mappings, and validations before leaders can approve the update.
Fortunately, new automation capabilities are enabling data governance that wasn't possible even a few years ago. You no longer have to accept manual reconciliation, inconsistent definitions, or delayed reporting as the cost of scale.
In this article, we'll explore the data problems enterprise RevOps teams face, how automation can help solve them, and what makes Varicent sales planning software different, particularly the ELT Assistant and AI tools, which empower admins and reduce dependence on IT resources.
The Data Challenge in Modern Revenue Operations
RevOps teams may find themselves working with data that's fragmented, inconsistent, or difficult to access, whether due to a large number of business units, multiple currencies, or reliance on many different calendars.
The Data Fragmentation Problem
RevOps data often lives in multiple systems and formats, each owned and optimized by a different function—Finance for compliance, Sales for speed, HR for policy. Essentially, different teams can own different pieces, sometimes even within the same department.
Even if consolidation efforts were made at some point, those efforts frequently break down at enterprise scale. Misaligned hierarchies, differing fiscal calendars, and regional variations in compensation logic all prevent systems from reconciling cleanly.
Another challenge for large enterprises is the actionability gap. For example, analytics teams may surface performance metrics in Tableau or Looker, but those dashboards are typically organized around high-level views—such as region or product—rather than the compensation plans, territories, or roles used in planning models. When reporting and planning structures don’t match, RevOps teams can be forced to rebuild the data before they can apply it manually. This limits their ability to adjust quotas, rebalance territories, or model incentives with confidence.
Data sitting in disconnected systems is not always easily accessible, creating blind spots. Sometimes you can't quickly see which territories are underperforming, or whether comp plans are working.
To complicate things even more, different datasets use different definitions. "Lead," "prospect," or "territory" can mean different things across different systems, making reconciliation difficult and creating reporting inconsistencies.
Many organizations need dedicated analysts just to prepare data for use. Not many tools handle ingestion, transformation, and reconciliation natively within a single platform.
Additionally, when territory, comp, and product data aren’t reconciled, teams can often debate numbers rather than act—forecasts slip, payouts lag, and plan cycles stretch by weeks.
Data Silos: The Hidden Revenue Killer
RevOps leaders who deal with data silos across Salesforce, Workday, HubSpot, and other enterprise systems may struggle to get a holistic view of revenue performance, seller capacity, and customer health.
Quotas that appear balanced at the regional level may mask rep-level productivity insights because they're based on the invoice date in the ERP rather than the close date in the CRM.
Total available market (TAM) models can be built without considering how your teams actually sell. You might staff the wrong reps on the wrong products because you can't easily see who's succeeding where.
Portions of your team could be set up to fail before the quarter even starts. Payout errors can happen when comp data doesn't sync with actuals. Different leaders may look at different numbers and make conflicting decisions. Revenue can leak out through missed opportunities and misaligned execution.
Automating data ingestion and normalization with an extract, load, transform (ELT) solution like Varicent can help break down these silos and give you a unified view from which to plan.
Manual Data Processes: Scaling Challenges
Manual data processes often depend on IT resources and custom queries for each request. They can also struggle to keep pace with regional data refresh schedules, multiple time zones, and varied currencies. As the business evolves, relationships between datasets can shift, so analysts must continuously pull and clean the data.
A typical plan cycle might require:
- Extracting opportunities and activity data from the CRM.
- Capturing a roster snapshot from the HRIS.
- Pulling bookings by product from the ERP.
- Incorporating compensation calculation outputs.
- Harmonizing IDs, time grains, and currencies across all sources.
At scale, the cost of managing data manually can start to outweigh its value. Some RevOps teams find themselves operating a full data function just to support basic reporting and forecasting.
Instead of using data to inform decisions, teams spend time chasing numbers and filling gaps. When insights aren’t available quickly, it becomes harder to pivot as conditions change.
Market dynamics shift, competitors adjust pricing, or a product gains traction in an unexpected segment, but the organization is still waiting on last month’s analysis. Delays like these can limit responsiveness.
However, AI-driven automation can help by giving admins direct access to insights in natural language, reducing routine IT requests. Teams can query and test data conversationally, lowering the risk of missing errors and freeing time for higher-value analysis.
How Can Data Automation Help RevOps Teams?
Your go-to-market (GTM) strategy, territory design, quota setting, comp plan modeling, and performance analysis all depend on having reliable information. At the enterprise level, the real advantage isn’t just getting data faster, it’s operating leverage, using automation to shrink the feedback loop between performance signals and the plan adjustments you need to make.
RevOps data automation matters because it allows leaders to replace slow, manual, IT-dependent processes with fast, AI-driven insights. The advantage can extend beyond operational efficiency (saving time, lowering costs, reducing reliance on IT) to enable a stronger GTM strategy.
AI can make it possible to:
- Automate processes such as data pulls, reconciliations, and field-level validations
- Ingest inputs directly from your CRM, HRP, HRIS, and other platforms
- Eliminate steps like refreshing opportunity snapshots or aligning fiscal calendars
- Define core entities (such as accounts, products, territories, and crediting rules).
- Standardize data grains across critical metrics.
- Perform complex scenario testing.
- Adapt parameters automatically when market conditions change.
- Trust your numbers as accurate and final.
RevOps automation can improve profitability, increase predictability with cleaner and timelier data, and accelerate speed to market with quicker plan adjustments.
Automation helps address the root causes behind common RevOps problems by unifying data and eliminating silos.
Core Components of Revenue Operations Data Automation
RevOps data automation typically involves four key capabilities:
- Data ingestion: Connecting multiple data sources like customer relationship management (CRM), ERP, human resources information system (HRIS), and spreadsheets, so you're always working from current information without manual exports.
- Data normalization: Standardizing formats, cleaning errors, and centralizing definitions owned by RevOps so you can trust your numbers. (Hint: Manual normalization is often where teams spend the most time and where the process becomes particularly arduous.)
- Analysis: Using AI and algorithms to highlight key areas for improvement, such as territory potential or account scoring, and turning raw data into actionable insights.
- Workflow automation: Automation connects systems such as CRM, ERP, HRIS, and compensation platforms to streamline every stage of the RevOps data lifecycle, from ingestion and normalization to entity resolution, governance, and analysis. It standardizes data to maintain consistent definitions, reconciles accounts, products, and territories into governed entities with clear lineage. Automation can also establish a trusted foundation for planning and performance management at enterprise scale.
- Data governance and ownership: Defining clear ownership for core entities and metrics across RevOps, Sales, Finance, and HR, along with cross-functional processes for requesting, approving, and deploying changes, so rollouts stay aligned and controlled at enterprise scale.
The Enterprise RevOps Data Automation Framework
Here's how leaders can move from messy, siloed data to revenue operations automation.
Phase 1: Data Assessment and Strategy
Start the process by mapping where your data currently lives and identifying gaps in access or quality. This will give you clarity on what's needed for accurate sales forecasting and territory and quota design.
- Set goals: Define the business questions you need answered and the level of granularity required (e.g., quota fairness by territory, rep productivity by activity type).
- Identify data: Identify what information you need to answer key business questions.
- Audit: Review the data you already have and determine where it lives (CRM, ERP, HRIS, spreadsheets).
- Identify gaps: Determine which data you need by identifying the questions you need answered. Then, you can compare this with the data you already have to identify gaps. Filling those gaps often requires selecting and onboarding new tools over several quarters. Hence, leaders typically pair that long-term rollout with a short-term data map and interim processes to manage dependencies during the transition.
- Map it: Produce a map of what data you have, where it's collected, who owns it, and how reliable and clean it is.
Phase 2: How to Select a RevOps Platform
Once you know what data you need and where it lives, you're ready to set out criteria for a RevOps platform that suits your needs.
Ideally, you'll look for a platform that can connect to CRM, ERP, HRIS, finance systems, spreadsheets, and enrichment tools without requiring custom development for each source. Many vendors promise complete ingestion and normalization, but few actually deliver at enterprise scale.
It's also essential to consider admin usability. RevOps leaders shouldn't need IT or technical teams to query or reconcile data once the platform is live. A certain ease-of-use is expected.
Varicent ELT, for example, offers scalability, AI-driven insights, and the ability to handle ingestion, normalization, and analytics within a single system. The ELT Assistant lets admins query data in natural language, so you can get answers without writing SQL or waiting on technical resources.
Phase 3: Implementation, Testing, Acceptance, and Iteration
Implement the chosen platform, then test and refine. The more sources you pull from, the longer it takes to ensure data is clean, trustworthy, and correctly aggregated. Even with automation, getting things right takes time.
Keep in mind that AI is not always a turnkey solution. You may still need to define the questions you're asking and determine which data to analyze. Where AI can help is guiding which data sources will best answer specific questions and how to structure that data for analysis. Once configured, AI can deliver answers quickly.
Once you're satisfied with the testing, verify that your chosen platform meets business requirements before you start iterating. Iterative testing builds trust in your data and ensures accuracy, so your RevOps team can act more confidently.
Phase 4: Adapt to Internal and Market Changes
Data automation shouldn’t be a one-and-done process. As internal structures change, as org design and sales motions, or market conditions shift, use AI to reconfigure sources, adjust definitions, and update outputs.
Keeping your automation current can help maintain relevant insights, preserve agility, and support profitability, predictability, and speed to market as circumstances evolve.
Why Spreadsheets and Generic Data Warehouses Fall Short (and How Varicent Is Different)
As we've mentioned, many teams rely on spreadsheets or generic data warehouses to manage their data. But these tools don't always provide the sales performance management context your RevOps team needs.
Traditional tools store information and run calculations effectively. Still, they aren’t designed for SPM-specific logic—like quota attainment curves, territory balance, or pay mix optimization—so teams often have to build that context manually.
SPM features can also satisfy many organizational governance and risk requirements by enabling data owners, audit logs, and controlled access permissions.
Varicent takes a different approach with three key capabilities:
- ELT Assistant: Admins query data in natural language to connect, cleanse, and analyze with minimal IT dependency. Questions that might take days to answer through traditional channels can often be resolved in hours.
- Advanced algorithm library: Varicent simulations and predictive insights like account scoring and territory potential, giving leaders foresight into performance before plans go live.
- Prebuilt connectors: The platform can integrate with CRM, ERP, and HRIS systems without custom development, reducing setup time and implementation risk.
Start Automating Today With Varicent's AI-Driven Sales Planning Software
RevOps data automation transforms disorganized, siloed data into a unified foundation you can actually use. Instead of spending weeks chasing numbers across systems, you get the insights you need to plan territories, set quotas, and model compensation.
Varicent's AI and ELT capabilities can cut turnaround times from weeks to hours. For example, AFL, a global telecommunications company, faced unsustainable, unscalable manual processes for managing its data. With Varicent ELT, they saw a return on their investment in less than 6 months, finally trusted their data, and eliminated other costly software systems.
“ELT allowed us to transform our data in a way we couldn’t do before,” said Greg Keller, Customer Experience Operations Manager at AFL. Watch their success story to see how Varicent ELT simplified and automated their data preparation and processes, with accuracy and efficiency that led to cost savings within the first six months.
Admins can work independently without waiting on IT, which may lower costs and improve efficiency. Advanced algorithms can enable more accurate forecasts, better territory balance, and fairer quota distribution.
These improvements can also directly affect your bottom line. RevOps data automation can mean fewer revenue leaks, more predictable performance, and faster response when you need to adjust plans mid-quarter.
Book a demo to find out how Varicent helps RevOps leaders automate data management and drive more predictable revenue.
