The Lead-to-Revenue Data Loop

Exploring the inextricable link between data, lead-to-revenue operations and organizational agility

In the modern business landscape, effective lead-to-revenue (LTR) operations and sound data are inextricably linked.  The ability to embrace new business models, speed time to market and create an enhanced customer experience requires a firm but flexible LTR framework that connects the front, middle and back offices.

At the very foundation of this architecture is data, gathered and analyzed from many systems and tools across Sales, Service, Finance and Delivery.  At the same time, generating accurate, relevant and timely data requires the business to unite their disparate systems and tooling in a common framework—a.k.a., a comprehensive and connected lead-to-revenue architecture.  As such, businesses are on something of a loop: The organization needs data to connect the business, but a connected business in order to gather relevant data. This means that businesses cannot create and deploy a data strategy in isolation—nor can they create a lead-to-revenue framework that does not have data at its core.

In this article, we explore the inextricable link between data and lead-to-revenue operations—and how organizations can increase agility, drive growth and enhance the customer experience through an integrated transformation plan.

Overcoming the limitations of legacy systems

Every legacy system has its limitations. And to overcome legacy is to overcome history.

“Over time, organizations have built data sets in certain ways, often working to the limitations of their existing systems,” explains Steve Terry, Navint’s EVP for Lead-to-Revenue Advisory. “There’s a history of that data spanning years or even decades without any real enterprise-wide discipline as to how that data should be gathered, stored, managed and governed.”

A scattered data estate makes it difficult to know precisely where the customer is at any point in the journey—which is of critical importance in a recurring revenue model or any scenario in which the business needs to perform potentially complex or customized tasks such as managing renewals, upgrades and add-ons. It is also an absolute necessity if the business wants to automate sales motions and revenue strategies.

“The business can’t be effective if they don’t connect sales and billing, or traditional front and back office functions, through an enterprise data system. Data in drawers, data in spreadsheets—that doesn’t cut it,” explains Steve.

Unfortunately, a technology gap within the enterprise software market means that an end-to-end solution is not available. No single solution spans the entire lead-to-revenue lifecycle, which means that the data strategy also won’t be singular in nature.

Data must act as the lowest common denominator, uniting these disparate functions and systems, threading the customer experience from lead to revenue.

“The business can’t be effective if they don’t connect sales and billing, or traditional front and back office functions, through an enterprise data system. Data in drawers, data in spreadsheets—that doesn’t cut it.”
– Steve Terry, EVP LTR Advisory

Creating an effective, flexible, scalable enterprise data scheme

Within each transformation project there are two main data objectives:

  1. Ensuring good, healthy data makes it into the system; and
  2. That the data works properly within the new architecture.

Supplementing the documentation phase with a diagnostic element

The sheer volume of information, a complex business architecture and functional silos all contribute to an incomplete, inconsistent, disconnected data estate.

Most data transformation programs begin with an assessment phase wherein the business takes inventory of all existing data. However, organizations should also consider adding a diagnostic element to run concurrently with the documentation and design phases. This assessment will identify which pieces are crucial to the future state during each stage of the transformation journey.

“The data modernization process isn’t just about finding out what data exists now, but what we need to enable the future state. Part of that diagnostic is asking: Where is our data? How does it relate to where we’re trying to go? And what kind of plan do we need to put in place to mitigate any issues and ensure success?” says Robyn Anderson, Navint’s EVP for Advisory, EMEA.

In performing the diagnostic, it is possible to prioritize efforts to maximize impact and speed time to value.

“You can’t remove all data issues in the very first phase,” says Steve. “We can focus on a select few to create a simple, clean model and set aside the complex account structures for a later phase. That’s the secret to speeding time-to-value—creating the foundation for improved processes and analytics in steps.”

Technology and data integration

The other part of the transformation equation is integration of the data within the new architecture. A common misstep is overlooking how a new technology or process will impact other systems that process resulting data.

“Organizations may think: ‘Well it worked before. Why wouldn’t it work again?’” says Robyn. “The short answer is because there wasn’t integration there before or it was not the same integration.”

New technology changes the environment in a material way, which means that all existing integrations need to be reconsidered and revalidated to ensure they still function properly and produce the desired results. In some cases, this will mean putting the data through some form of enrichment or conversion to ensure it works with multiple systems. It could also mean that some integrations will need to be rebuilt or reappointed.

The same principles apply to both ERP and CRM data modernization. For example, ERP modernization isn’t just a matter of identifying missing data or identifying all the data that needs to be transferred. When the organization is shifting from one system to another, the data structures will not match. The new system could have slightly different field lengths or there could be an extra field. The business needs to come up with a plan that addresses those discrepancies and disconnects with the least amount of cost and in the fastest period of time.

During this process it’s important to keep in mind that data conversion is not a one-time task. In fact, data may need to go through several stages of cleansing or enrichment in order to draw the right information to guide the system.

Organizations must design, execute and operationalize a process that will enable data sourcing, as well as data cleansing, enrichment and validation. Functional testing is not just ensuring that the system operates, but that it meets all those requirements, across all functions of the future-state as outlined during the project planning.

Data is iterative—that’s a lesson learned in the trenches. You have to convert data many times throughout a transformation in order to achieve the ideal future state.
– Robyn Anderson, EVP LTR Advisory, EMEA

Beyond technology: Driving success through data access, ownership and stewardship

Like all transformation programs, success lies not just in the technology and its implementation but also on the change management program that wraps around it.

“There’s this misconception that enterprise data transformation is enabled exclusively by data engineering resources,” says Steve. “In fact, there is an important human intelligence component which is required to understand how the existing data strategy needs to evolve to help the organization meet their future-state vision.”

A successful program is one that gives people across the organization access to clean, healthy, timely data—and empowers them and inspires them to use it.

“If some business units are still relying on spreadsheets to track data after the implementation, then the program—no matter how perfect it may be in theory—was a failure in practice because people refused to use it. The technology may operate flawlessly, the data might be near-perfect, but adoption requires people to understand how to use the system and the value in doing so,” says Robyn.

This is why it is so crucial to bring together all the different stakeholders, including Sales, Finance and Customer Success, at the outset of a data program. The processes you settle on, the technology that’s wrapped around it and the systems that feed the tooling all need to satisfy the needs of the entire business. Outputs can be tailored to suit each function on an individual level, but at its core, the system must support the entire business and customer lifecycle.

Engaging a partner, ensuring success

For many organizations, a data transformation program requires expertise, often from a partner. This transformation partner will help the organization make decisions around all aspects of the project, as well as understand the implications of those decisions. Even though data is ultimately owned by the client, the business cannot and should not underestimate the value of having a trusted, knowledgeable voice at the table to guide those conversations.

Navint: A market-leading Lead-to-Revenue transformation partner

Navint is an advisory and technology services firm that enables enterprise organizations to drive growth and operational efficiency throughout the lead-to-revenue lifecycle. Our trusted team of cross-functional LTR experts recognizes the interdependency between data and lead-to-revenue operations. We help businesses take a data-first approach to every program and create an integrated transformation plan that will increase agility, drive growth and enhance the customer experience.

What makes Navint a good data partner:

  • Our true lead-to-revenue expertise and ability to connect all aspects of the business, including data, across Sales, Service, Finance and Delivery.
  • Our ability to incorporate the data modernization program within the broader transformation agenda
  • Our deep understanding of data as a strategic asset that will drive the organization’s future-state & GTM agility
  • Our strategic planning and program management capabilities, which help us effectively align the organization and all stakeholders on program goals, priorities and processes
  • Our technical expertise as it relates to data, as well as the lead-to-revenue lifecycle systems, platforms and solution providers

Navint’s data-first approach to LTR transformation

1. Define how data drives strategic and operational priorities

  • Assess current business needs and differentiators
  • Define how key aspects of data drive revenue growth, sales expansions and effective renewals
  • Define how data quality can drive value through speed, accuracy and efficiency
  • Connect data to future-state technology and operations
  • Develop a compelling business case to ensure stakeholder support and adoption

2. Identify data capabilities

  • Assess existing data capabilities, overlaps, gaps and silos
  • Identify target architecture and capability framework
  • Align operating practices with data capabilities, as well as technology design and planning
  • Connect data improvements to investments across solutions (Sales, CPQ, Billing, ERP, Service)

3. Mobilize for implementation

  • Prioritize investments across low-medium-high impact
  • Prepare change management activity to ensure successful adoption and business results
  • Generate meaningful reporting and analytics
  • Enable continuous tracking of benefit realization

4. Unify the lead-to-revenue process

  • Ensure data, technology and process optimization across functions for effective change
  • Provide strategic counsel to effectively manage complexity and ensure necessary collaboration
  • Prepare the organization to collaborate for optimal results
  • Measure results and create continuous improvement with business stakeholders


Integrating the data strategy within the organization’s lead-to-revenue framework is a potentially complex process—but it is an absolute necessity within the modern business landscape. With it comes the ability to understand exactly where each customer is on their journey, as well as enable the capabilities within the business to effectively serve on an individual level. In uniting data with the lead-to-revenue architecture, organizations may find that they are indeed running on a loop—one in which the customer keeps coming back.

For more information about how your organization can effectively leverage the power of data within the customer lifecycle, please reach out to our team of experts to schedule a consultation.



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