From Chaos to Clarity: How to Fix Your Broken SPM Data

Ineffective data management significantly reduces the effectiveness of your go-to-market strategy and erodes employee trust in the business.   

Sales compensation teams rely on stakeholders across the organization for data yet are ultimately accountable for using that data to pay people. This can become highly frustrating as upstream Sales Performance Management (SPM) data issues prevent sales comp teams from being responsive to their payees and becoming true strategic partners to leadership. 

Our mission is to help organizations leverage their most powerful tool to drive behavior and actualize their go-to-market strategy: sales incentives.’s experts have implemented and supported hundreds of enterprise-level organizations' SPM systems.  

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We've seen the full spectrum of how organizations collect, govern, and automate SPM data and how much damage can be done when improperly constructed. We have also seen the outsized impact on efficiency and performance that effective data management of data can have. 

This article will discuss the common symptoms of broken sales performance management data to help you understand and articulate the root causes and identify the steps you can take to improve your data management. The aim is to equip you with the information needed to make a business case for investment in this area to organizational leaders. 

Common Symptoms of Ineffective SPM Data Management 

Despite what many Incentive Compensation (IC) software vendors may have you believe, sales compensation is not static. While it’s true the core sales incentive program is rarely changed more than once or twice a year, dozens of adjustments and additions are made to it throughout the year in the form of SPIFs, crediting adjustments, product launches, new components, personnel changes, and one-off splits.   

One all-too-common symptom of ineffective SPM data management surfaces when these new plan components, rules, or SPIFs are introduced mid-cycle.  Often under pressure due to suboptimal performance, sales leaders will approve new compensation plan metrics or incentives without the compensation operations team knowing how the underlying data and workflows will support them.  

Consequently, the terms communicated to the sales team when the new incentives are launched don't fully align with how the calculations work in practice, leading to questions, shadow accounting, and frustration from the field. The longer it takes compensation teams to figure out how to source and organize data to support the new components or SPIF, the longer those on the plan go without feedback on their performance, eroding its effectiveness. 

Another common issue is sales compensation data governance. Most organizations use Customer Relationship Management (CRM) software or crowd-sourced data for transactional and object-related data fields. Often, that source data will change after payouts, especially in cases where role changes lead to the reassignment of account owners and opportunities.  

When the data sales reps see in the CRM doesn’t match what they see in other systems, it leads to confusion and degrades trust in those systems. It also leaves you without a complete audit trail — unless your SPM tool is sophisticated enough the maintain a full history of input and output data for each payroll, including a history of changes. 

What Healthy Sales Comp Data Management Looks Like  

Here are some indications of a healthy SPM data management system: 

  1. Metrics or performance numbers match across systems, reports, and dashboards. 
  1. Employees are not surprised when they see their final payout number. 
  1. Sales compensation teams are involved in the planning and testing of all projects that impact sales performance data, such as system migrations or CRM workflow changes. 
  1. Data providers and compensation administrators vet new plans or components before being presented to the field. 

In our experience, few organizations can check all four indicators, although not from a lack of effort. With so many systems and stakeholders involved, maintaining enterprise-level sales data in a meaningful and manageable way takes considerable time and resources. 

While it may seem expensive and time-consuming, optimizing your organization’s SPM data management practices will make life easier for multiple functions, particularly the sales comp and revenue ops teams, freeing them to create further growth and optimization opportunities.  

We recommend addressing the following three pillars of data management to enhance the data management processes in your organization: 

  1. Make sure every data source has an owner 
  1. Automate data transfer and validation 
  1. Unify and centralize data sources 

1. Give each data source a specified owner 

Sales compensation teams have some of the highest standards for data quality and availability within an organization. Any unexpected changes or delays with critical Sales, Human Resources, or Financial datasets quickly become barriers to the sales team getting paid.  

Most companies have 12 to 15 apps plugged into their CRM, all of which use APIs that treat everything like a point-to-point connection. Each of those systems is like a silo, with no awareness of the overall data needs of the business. And that’s just the CRM; Sales Comp teams also rely on data from other Enterprise Resource Planning software, Product, Support, and Finance. 

Building relationships with the owners of those data systems is critical for the long-term success of a sales compensation program. Source systems are prone to frequent changes, so partnering with the owners of those systems to ensure architecture and workflow changes is critical to maintaining data hygiene.  

Sales compensation teams must understand the intricacies of upstream data systems and data-providing partners and be involved in the planning and testing of any projects which impact SPM data, such as system migrations or CRM workflow changes. 

💡Companies spend $260 billion on analytics, yet only 55 percent of their data is available, and only a fraction is suitable for analysis.  

We often get customer requests to update a data source to a new format (for example, from a system migration). Most SPM systems are complex, often using a single column or data field from one dataset in many ways across the comp plans. Consequently, a deep understanding of the data sources and rigorous regression testing are necessary to ensure the calculations do not produce unexpected results or outcomes after the changeover.

If the compensation team is not involved in the data migration project from the start, they cannot take ownership of those tasks. Instead, they must come in after the fact to perform ad-hoc validation with the data project owners, who have likely already set timelines and made key decisions about the data structure.  

At that point, the options are either an un-tested cutover to the new data source, an unexpected delay in the project while the compensation team tests, or a mad scramble to meet existing timelines — none of which are ideal. Continuous collaboration between the sales comp team, other functions, and their data providers will help avoid these scenarios and streamline integrating new or updated data sources. 

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If the comp team doesn't understand the SPM data or how it's changing, they can't answer questions from the field about how specific actions or behaviors will impact their payout. The inability of the compensation team to answer simple questions about why a salesperson did not receive credit quickly erodes confidence in the compensation program and supporting tools.  

Suppose changes to sales performance data systems aren't communicated and tested. In that case, they're inevitably caught by compensation administrators on the eleventh hour during payroll processing or — in the worst cases — when sales reps complain that their pay is wrong. 

For every data source you depend on for payroll, assign a specific owner outside the sales compensation team and make them aware of their role. Engage with them regularly so that you can be proactive about identifying changes that will impact SPM and commission data. Strictly maintain data documents for all sources and include IT and Business Intelligence (BI) teams in any broader data governance resources and documentation.  

Include details on the business purpose of specific fields in the system, with as much detail as possible. For example, the “Account Owner" field could be described as, "This field drives crediting to our Account Manager plans and appears on incentives dashboards as 'Owner.' It is maintained by the sales operations team, who update it in the CRM.” 

Whenever changes to these fields are made, record details about the timing and purpose of the changes and any subsequent changes to corresponding data or workflow logic. This will maintain an audit trail and help employees understand how and why things are built the way they are, preventing them from being changed erroneously or unnecessarily in the future. 

2. Automate data transfer, management, and validation processes 

Virtually everyone agrees that repetitive data manipulation tasks should be automated. Automating data tasks does more than save time. It ensures consistency and scrutability and removes the risks present when only one or two people know how a dataset or workflow is constructed.  

Achieving a high degree of automation is a must for any sales compensation team that wants to become a strategic partner to leadership. Unsurprisingly, automation is the number one reason enterprises invest in new Incentive Compensation Management (ICM) software. But the sad reality is that automation in the SPM world is more of a cruel irony than an actuality; The common “automation” tools rarely deliver the expected benefits, especially once they are older than a year or two.  

Any automation implemented when onboarding to the ICM software eventually breaks due to updates to the plan, its components, or other data sources. The time, effort, knowledge, and resources needed to maintain the previously working automation are often unavailable, so processes gradually become more manual. Sales compensation managers rarely get deep enough into the details to notice these shifts, but over time, they erode the effectiveness of the original automation and the compensation program. 

💡 In a study of 75 enterprises by MIT, only 3 percent of data sets were within an acceptable range, and 50 percent contained critical errors.  

When organizations onboard new sales comp automation solutions, the implementation team will first look to understand what data is provided, when, how, and by who or what. Best-in-class implementation teams will be suspicious of any sources provided via manual Excel upload and seek to "work upstream" to define automated data interfaces with source systems. That same mindset should apply to teams not implementing a new SPM system. 

Do not just "set it and forget it." For any data that flows into an SPM tool, identify and document who or what provides it, when it's delivered, what upstream processes it's dependent on, and what impact it has in compensation calculations. That means, for example, not just receiving a file from HR with new hires and loading it into the system but understanding how it is created and what it does once loaded into the SPM software.  

Without this documentation and understanding, as soon as changes to policy come that impact things like new hire guarantees, you may end up in a spot where the HR data is supplemented by manual adjustments to get calculations right, defeating the purpose of the automation. 

Anytime we hear the word “adjustments” used about a data set indicates that the underlying automation needs to be fixed or rebuilt. If your compensation team spends more time on data adjustments than strategy and analysis, it is probably time to upgrade your automation. 

3. Unify and centralize all SPM data sources 

Centralizing and properly utilizing SPM data systems is critical to the overall health of a compensation program. Siloed or redundant systems that perform similar functions only lead to confusion from sales reps and inefficient processes for the comp team. 

Sales performance data is vital for more than just payouts. Tasks like financial accruals, plan modeling, sales forecasting, and goal setting use the same inputs and business logic that apply to payouts but are often managed by separate teams with separate tools. That leads to multiple sources of truth and redundant systems.  

Centralizing all sales performance data means less time spent wrangling data to model new plans and quotas and more time evaluating account potential, balancing territories, and evaluating plan alternatives — tasks that provide incremental value to the organization. 

Every Sales Performance Management tool advertises "crediting, quota management, data transformation," but few customers get to the stage where they can reliably use those features as part of their go-to-market planning. Despite spending millions on those SPM tools, most organizations still rely on Excel workbooks, monolithic SQL queries, or home-grown solutions to apply crediting rules. Your SPM systems should connect directly to source data and be the source of truth for any logic or filters that apply to compensation processing. 

A significant benefit of centralizing business logic and processes is that once problems are solved or decisions made, they are encoded until they are changed again. When multiple systems are used for the same process, these benefits invert. There's more risk of situations like filters on sales crediting not being considered when targets are set or changes to compensation plans not being reflected in financial models. 

Multiple sources of truth for sales data also lead to inquiries from the sales team. When analytics or BI teams build dashboards without considering the complex logic the sales comp team applies, performance dashboards and commission statements will all have different numbers, leading to uncomfortable and unnecessary discussions after every pay run. 

To avoid this, examine all the processes dependent on sales performance data (crediting, data transformations, territories, accounts, quotas, etc.) and available tools. Consolidate and centralize operations as much as possible. This takes collaboration with systems and BI teams and close partnerships with tool vendors. 

Elevate Sales Comp to a Strategic Partner 

Great sales organizations are creative, strategic, and forward-thinking. These qualities shine through when implementing incentive compensation plans, with new components and policies emerging quickly, often without a clear plan on how data will support them. 

That leads to considerable demands on sales compensation administrators responsible for getting people paid correctly. They are directly accountable to core business functions (sales, finance, HR) but often don’t have the control or resources to own all the data you dependent it.  

Sales compensation teams want to be a partner and not a barrier, but unfortunately, that means short-term, unsustainable solutions when it comes to managing sales performance and compensation data.  

Sales comp teams rarely have the data or systems expertise necessary to support the complexity inherent to sales comp. It takes either a technically minded compensation leader or a close partnership with IT/BI to understand the weaknesses of their current processes and align on which tools are correct for a particular function. Like any change, it will be painful, but getting data right is the foundation for successful sales compensation. 

By understanding your performance data, automating repetitive processes, and centralizing logic, you build the foundation for a sustainable, adaptable, and effective compensation program that delivers the outcomes expected in your go-to-market strategy and elevates the comp team to a more strategic function.  

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