Automating Formula Building Using a Centralized Data Model
In the world of Incentive Compensation Management (ICM), data models play a pivotal role in converting real-world concepts into digital systems. These systems, functioning as large-scale calculators, rely on structured input and precise operations to generate outputs that impact real-life individuals and entities.
However, the decentralized nature of data models used in traditional ICM systems presents a challenge. Each organization tends to have its unique model, resulting in a lack of standardization and compatibility. This requires professionals working with ICM tools to start from scratch when switching organizations, relearn concepts and their relationships within the system, and rebuild functionality they’ve already seen.
A centralized compensation data model is essential to address this problem and improve compensation administration. This blog explores the benefits and potential of a centralized data model in the future of incentive compensation management, including automated formula building, enhanced industry-wide standardization, and the ability for sales organizations to confidently adapt their plan structures.
What is a Data Model?
A data model is an abstract model that structures data elements and establishes standardized connections between them. It acts as a representation of reality by documenting real-life individuals, places, objects, and their interactions.
Challenges of Diverse Data Models
Incentive compensation systems are built on data models. When you create a “Payee” or “Component” record in the system, you’re aligning real-world concepts with the data model. This is necessary because ICM systems function as large-scale calculators, depending on structured input and precise operations. The data model serves as the link between these ICM “calculators” and real-life individuals and entities, ensuring accurate outputs. For instance, when paying 200 individuals, the system must differentiate the 200 distinct records linked to each person.
Given that data models help us encode the real-world into the digital system, another question should arise to those familiar with incentive compensation:
“If there are so many data models for incentive compensation, which one(s) should I use?”.
This is a deeply rooted and underestimated issue. Traditional ICM systems serve as tools for building formulas. While they may offer pre-built tables for common elements like “plans” and “territories”, the way these objects interact is left to the discretion of the system builder.
As a result, numerous decentralized data models emerge, each unique to the respective organization. Even if you have extensive experience with a specific ICM tool, transitioning to another organization’s environment often requires starting over and relearning the meaning and connections of these concepts within the system. What is considered a “metric” in one system may be labelled as a “component” or a “KPI” in another. Additionally, one system may only allow employees to have a single “plan” at a time, while another may require multiple “plans” assigned to a single individual to accommodate different payout mechanisms. This needs to change.
The Benefits of a Centralized Data Model
A centralized data model is essential for improving incentive compensation administration.
Compensation plans are built on intuitive concepts widely understood, but which have complex technical aspects known by only a few. Translating these concepts into formulas and code is a challenging, time-intensive, and often thankless task. However, doing so in legacy ICM tools often leads to the creation of yet another brittle data model that lacks sustainability and accumulates technical debt. Instead of addressing problems on an organizational level, a centralized incentive compensation data model can solve them on an industry-wide scale.
If you have experienced the complexities of managing multiple generations of incentive plans for different companies, you might be skeptical. Countries have unique labor laws, companies have time-tested cultures, and industries undergo frequent disruptions, all while compensation recipients demand transparency and accuracy while shopping around for better jobs. Consolidating all these nuances into a single model may seem daunting, unrealistic, or doomed to break.
However, there is precedence for standardizing and centralizing improving outcomes and not limiting flexibility. Centrally maintained standards have historically enhanced various fields without limiting flexibility: TCP/IP for internet communication, MIDI for musical instruments, and FHIR for healthcare record exchange are just a few instances.
It’s a bit counterintuitive though — how does standardizing across organizations benefit individual organizations? More tangibly, how does a centralized data model help a company handle complexity and change their compensation plans to gain a competitive advantage?
Consider that, in a world where a centralized compensation data model automates formula building:
- Plan administrators will spend less time building formulas and more time optimizing the use of the sales compensation budget.
- Sales managers will spend less time teaching sales reps how to understand unique aspects of the compensation plan, and more time helping them to fit the right products to the right customers.
- Human resources and compliances leaders will rest assured that plans follow an established standard shared by many organizations.
In short, your best people will acquire more time to deliver true value to the organization.
Revolutionizing Incentive Compensation Systems
Apologies in advance to Apple loyalists, but for a moment consider the Android operating system. If you’re like the author of this blog, you have likely customized your phone extensively to your preferences and workflows. When a particular layout, productivity app, or even the phone itself no longer meets your needs, you can easily change it without an in-depth technical understanding of its inner workings. That’s because Android (and other operating systems) act as an intermediary between your desired user experience and the underlying complex technologies. The engineers behind the operating system have thoroughly tested all possible scenarios you could throw at it on a variety of devices. A centralized data model can create a similar environment for incentive compensation, where decisions such as “transitioning from monthly to quarterly quotas with a 100% cap until Q4” can be made without considering all the technical details. You simply make the decision, inform the system, and it seamlessly adapts.
This is the future of incentive compensation systems — a system that speaks the language of the sales organization with the necessary technical foundation to handle the most complex scenarios.
The widespread adoption of consumer AI tools, such as chatbots, demonstrates how digital systems that leverage extensive knowledge and natural language can accelerate change and improve productivity. However, in sales compensation, where precise accuracy is critical, utilizing unstructured data from diverse and potentially unreliable systems as a basis for training is an inadequate foundation.
It is only through a centralized data model, wherein challenges related to plan concepts like proration, draws, guarantees, and caps have been permanently resolved, that incentive compensation systems can attain the level of intelligence required to provide the agility that complex organizations demand while maintaining 100% accuracy of payout.
Despite the considerable effort invested by skilled analysts, administrators, plan designers, and system architects, we continually encounter the same challenges, resulting in ineffective systems that never fulfill their promise of flexibility. Automating formula building using a centralized data model will remove barriers from tailoring compensation plans to immediate needs of the business and yield massive improvements in efficiency for teams that spend too much time programming for technical edge cases that have already been resolved. The promise of flexibility is delivered.
Talk to one of our experts to learn more about how Forma.ai drives better, data-driven decision-making around your incentive compensation plan.