Scaling Enterprise Systems: Data Management Best Practices with HubSpot

At Mole Street, we believe that the value of your data is directly tied to the quality of your systems architecture. 

For many enterprise organizations, outdated systems architecture can become a significant barrier, holding them back and eliminating competitive advantages. When data is siloed or systems lack integration, staying ahead in a fast-paced market becomes challenging.

After all, enterprise data management isn’t just about having a good tech stack. It’s about enabling innovation, creating a foundation for best practices, and aligning your business processes effectively. 

By focusing on building and refining systems architecture, at Mole Street we help organizations unlock the true potential of their data, empowering every aspect of their business from decision-making to strategic growth.

Key Insights

1. Quality systems architecture unlocks data’s potential: outdated or fragmented architectures limit the ability to leverage data effectively, while a well-designed structure enables data-driven innovation and growth.
2. Decision-driven analytics leads to business success: shifting focus from merely collecting data to supporting specific business decisions with that data helps avoid analysis paralysis and empowers informed, impactful action. 
3. Siloed systems reduce efficiency and innovation: disconnected and poorly integrated systems create silos, limiting data accessibility, hindering decision-making, and reducing overall efficiency. Instead, a single and integrated source of truth provides a unified customer view and better aligns processes across departments.

What Makes Data Valuable?

Data is only as valuable as an organization chooses to make it. 

While your organization collects data simply in the course of your day-to-day operations, there’s a high opportunity cost if you fail to leverage that data. It’s a real problem – many enterprises we work with suffer from outdated systems architecture, leading to key data being siloed or used inconsistently across different departments. 

Instead, focus on what that data can actually do for your organization.

If you start by understanding what data can tell you, it becomes a point of reference that helps you understand the more qualitative aspects of what's around you. Once businesses become more data-driven, it provides a much more objective point of view to make informed decisions.

The real value of data isn’t simply in having it—it's in what decisions it enables a business to make. 

You need data as a barometer to gauge performance. It’s not enough to observe your team and hope for improvement; the objectivity that data brings is critical for moving forward.

Reasons Why Businesses Want to Be Data-Driven

Businesses today aim to make informed decisions using data analysis and insights, and for good reason. 

"There’s the old adage 'you can’t manage what you don’t measure," says Michele Herzog, Director of Technology at Mole Street. 

Data-driven strategies empower organizations to optimize marketing spend, sales strategies, and service operations, ultimately improving ROI. By measuring performance at every stage, companies can manage and improve operations with data as a guiding light.

On the sales strategy side, you can think about data at a very basic level in terms of sales activities. For example, if a sales rep is making hundreds of calls a week but closing fewer deals than expected, the data gives you the insights to identify and address these gaps. At a macro level, if your time to close between stages is longer than expected, you can use data to start asking critical questions—why is it taking so long, and how can we fix it?

Hannah Goldberg, Solutions Architect at HubSpot, provides a structured framework to help businesses effectively align their data-driven approaches. 

During technical discovery calls, she focuses on understanding the full scope of the systems in place and how data flows through them. Goldberg explains, "Always start high level—at the widest part of the funnel—and then get to the diagram. For example, I begin by asking prospects to tell me about the systems they’re using and what business goals they achieve."

Goldberg’s process involves several key steps:

1. Big Picture: Start with an open-ended conversation about the systems and tools in place to understand their purpose and business impact.
2. Identify Pain Points: She adds, "Tell me you're using this tool, but why did you get it in the first place? What was the point of it?" This helps uncover friction points that need attention.
3. Mapping Data Architecture: Goldberg dives deeper into specific tools and their roles, such as asking about email marketing or reporting. Understanding the tech stack helps identify areas that are underutilized or overly complex.
4. Define Desired State: Finally, Goldberg asks, "What direction is the data flowing here, and would you ideally be doing it this way?" This helps in setting up an ideal state for data management.

Incorporating this systematic approach ensures that businesses can maximize the impact of their data-driven strategies by understanding what is currently in place and designing systems optimized for growth and efficiency.

Beyond sales and system structure, being data-driven helps organizations navigate uncertainty by forming informed hypotheses based on past performance. 

If you have a lot of customer service tickets from a particular client focusing on the same issue, it signals a need for educational support. These insights allow you to react proactively, not just from a support standpoint but by bringing in the right resources—whether it’s an onboarding specialist or an account manager.

Leveraging this value requires a consistent effort from leadership. Herzog emphasizes, "When leaders take those metrics and reports for granted, they fall to the wayside. You need to consider—what am I measuring? Is this reliable? Not thinking about it is the biggest risk."

Data and the Illusion of Certainty

Data, even when plentiful, can be misleading if it is not adequately cleaned, integrated, and analyzed. Without the right processes, data can quickly become a source of confusion rather than clarity, leading to poor decision-making and negative business outcomes.

A robust system architecture is crucial for ensuring data accuracy and reliability. Organizations with a well-designed architecture can maintain data integrity and provide the context to derive meaningful insights, ultimately supporting better business decisions.

As consultants, we don’t add technology for its own sake. Every project needs to have a specific purpose and business outcomes tied to it. 

"We're not in a place anymore where we can just come in and say hey, we're going to implement Marketing Hub, and that's going to work for you’," explains Chris DiPietro, Director, Solutions Architecture at Mole Street. "If we're going to implement Marketing Hub, we have to tie it together with the rest of your technology to make sure that we're creating a solution that has a really strong end-to-end process so that the business can achieve the goals that they have."

Data must be correctly handled at every stage—from collection to analysis to derive meaningful insights. Without ensuring its accuracy and alignment with business processes, organizations risk basing decisions on an illusion of certainty rather than solid evidence. 

Alternative Framework: Decision-Driven Analytics

To truly leverage data effectively, shift your focus from simply collecting information to answering specific business questions. 

This means figuring out which key decisions need data support and then setting up data processes that help make those decisions easier and more effective. Parts of your data stack that don’t help improve business performance should be removed. Herzog explains: "We're going to clean up all these things that you don't need, and you don't use and are not accurate, reliable, testable." This way, we ensure that data analytics will provide meaningful and actionable insights.

Take all the tools out of the picture. What does your business care about? What do you want to see in a year or two? Once you have your business goal, consider the metrics that contribute to it.

For example, someone might say: "We need marketing qualified leads, deals created, a good close rate, and an understanding of referral rates—how often do existing customers bring us new business?" Focusing on these core metrics, our consultants help clients identify what to measure and how to build the necessary data processes in HubSpot.

You need to ensure that all of the work done by your data team fits in with the needs of the overall business. One way to operationalize it is a simple framework introduced by Bart De Langhe and Stefano Puntoni in their recent book, “Decision-Driven Analytics":

• Decision: Establish the decisions to be made and desired outcomes.
• Question: Define precise business questions to answer with data.
• Data: Collect and analyze relevant analytics and measurements.
• Answer: Review analysis results and recognize complex interpretations.
  
  

By focusing on the key decisions and defining clear business questions, your organization can avoid the trap of analysis paralysis and instead generate insights that drive real action.

Untapped Potential of Enterprise Data

As organizations grow, they often accumulate a variety of systems and processes that don’t always communicate well with each other. This lack of integration can lead to inefficiencies and missed opportunities. 

Chris DiPietro highlights this challenge: "As we've gone upmarket and started working with bigger, more complex clients, it's not just as simple as improving workflows for a single tool or solution.'” 

The real value for enterprises is in tying every part of the technology stack together.

Opportunity Cost of Siloed Systems

Siloed systems limit data visibility and hinder cross-functional collaboration, making it difficult to achieve a holistic view of the customer journey.

"We're working with companies that have complex technology internally, multiple different systems, enterprise data warehouses, ERP systems, practice management systems, project management systems," says DiPietro. 

With so many tools at play, issues can easily arise. Manual data entry and reconciliation processes are time-consuming and prone to errors, and data fragmentation across various systems makes it challenging to fully leverage insights that could drive better decision-making and efficiency.

Risks of Data Ignorance

A lack of understanding of where data lives and how it’s managed can create significant business challenges.

Herzog explains, "I think people live in the dark” about the data they have within their organization. “It comes up a lot when we go to do a migration. We go to merge data and put it into HubSpot. But where the data lives and how you capture it can be unclear—like, 'Oh, you have to go to that team to get that data.' We don’t know if that data is up to date with this other system. Disorganization and lack of alignment can come to light quickly."

Without clear processes, teams rely on outdated information, struggle with alignment, and miss out on key opportunities. 

Proper governance ensures data is clean, accessible, and ready to drive confident, informed decisions that move the business forward.

Limited Perception of Data Accessibility

Many businesses underestimate the challenges of accessing and integrating data from various sources. Poor data discoverability can hinder analysis and slow decision-making, limiting an organization’s ability to act quickly on opportunities.

Departments within your organization need to understand how their data systems interact. Without this understanding, you can miss out on fully leveraging existing data assets.

Michele Herzog emphasizes: "Keeping your data easy to access and easy to manage is key. It's not just about moving things around or merging them—it's about being able to quickly get the information you need and having confidence that it's accurate."

When data is easy to find and trust, teams can move more quickly and make decisions with greater confidence, ensuring that data is a real asset rather than a burden.

Building a Solid Foundation: The Principles of Effective System Architecture

Building an effective system architecture is key to unlocking the full potential of enterprise data.

As Chris DiPietro points out, "Without that, we're just slapping tools on in different places, and it limits their ability to grow versus creating a solution that's deeply integrated into the rest of the tools that they have so that they have a true end-to-end process that has automation and things like that."

A solid architecture means not only integrating systems but also ensuring that each tool serves a clear purpose in the broader business ecosystem. This approach helps to create a foundation that supports growth and efficiency.

Establishing a Single Source of Truth

One of the key principles of effective system architecture is creating a single source of truth. 

For example, we’ve seen enterprises with multiple instances of Marketo and Salesforce Pardot all pulling in different data and playing disparate functions within that organization’s Go-To-Market (GTM) program. Those marketing programs are typically scattered, much like their data flows, making it impossible to track performance or run streamlined campaigns accurately. 

A unified system can change everything. When designing an effective architecture, you need explicit purposes for each tool. Enterprises often have about 20 different technologies, and understanding why each system exists can reveal operational gaps and opportunities to consolidate and streamline.

Establishing a single source of truth—where all critical data flows and is managed—creates a clear view of customer experiences and helps align all parts of the business.

At Mole Street, we believe that HubSpot offers the perfect platform to be this source of truth. It connects marketing, sales, and service processes in one place. You get the data you need for analytics, automation that simplifies backend procedures, and clarity about whether additional tools are truly needed.

Having a single source of truth means you’re not just using tools—you’re building a cohesive system that ensures data consistency, reduces manual effort, and drives better decision-making across all teams.

Aligning Solutions with Business Needs

Each part of your tech stack should be directly aligned with business needs. 

Hannah Goldberg, Solutions Architect at HubSpot, often starts her discovery calls by asking, "Tell me about the systems you're using today and what business goal they achieve." This question is crucial—if a tool isn’t helping achieve a defined goal, it might add unnecessary complexity.

What are you trying to achieve? You need to align technology with your specific business objectives. By understanding this alignment, you can see if tools are adding real value or just creating silos.

Regularly reassessing your systems architecture helps you keep data focused on supporting the strategic goals of the business.

Prioritizing Data Quality and Governance

Data quality and governance are at the heart of any successful data strategy. 

Herzog states, "Keeping your data easy to access and manage is key. It's not just about moving things around; it's about being able to get the information you need quickly and being confident in its accuracy."

To maintain high data quality, your organization should implement regular checks, establish clear ownership, and continuously cleanse and deduplicate data. This commitment to data quality ensures that decision-makers rely on accurate, up-to-date information supporting effective strategy.

Ensuring Security of Sensitive Information

Data security can’t be overlooked, especially in today’s environment, where data privacy regulations are critical. 

Herzog explains, "I have the power to enroll everyone who fills out this form in our marketing emails without them giving explicit consent." This kind of oversight can lead to serious compliance issues.

HubSpot's evolution to support HIPAA and PII compliance shows how the landscape has changed. "There's a legal compliance side to GDPR, consent, and now HIPAA," Herzog adds. 

However, security issues can plague any business, not just organizations that operate in highly regulated industries. For example, Herzog shares, "Sometimes, there can be too much data access—like when everyone is added as a secret admin to the HubSpot portal. It creates chaos and compromises data integrity." Ensuring access control and appropriate permissions is vital to maintaining data quality.

Protecting data isn’t just about encrypting it—it’s about understanding regulations, implementing proper access controls, and making sure that everyone on your team is aware of the compliance requirements to keep your sensitive data secure.

Common Issues with Enterprise Marketing Data

One of the biggest challenges enterprises face with their marketing data is the basic misuse of data structures and features. These issues often arise due to misunderstandings about the functionality of certain tools and how they should be effectively applied.

Basic Misuse of Data Structure and Features

In HubSpot, a common example of misuse involves confusion between custom objects and custom events. 

Herzog shares her experience: “I've seen a lot of custom objects that are actually custom events. Tell me more. In HubSpot, you can have a custom event that is, like, this happened on the record.”

To clarify the difference:

• Custom Objects:
  • Data structures that categorize a record.
  • Describe attributes that make up a particular record.
  • Allow users to add custom fields or properties to their HubSpot CRM.
  • Store additional information about contacts, companies, deals, and tickets.
  • Represented as tables with individual columns called custom properties.
• Custom Events:
  • Specific interactions and behaviors related to records.
  • Show up as specific properties that can be reported on.
  • Track and analyze those specific interactions or behaviors on websites, emails, or other assets.
  • Configured to store this information within properties that can be used across HubSpot's tools.
    
    

Misunderstanding these distinctions can lead to improper implementation, which limits the ability to fully leverage HubSpot’s capabilities, causing inefficiencies and missed opportunities for optimization.

What a Typical Enterprise Tech Stack Looks Like

Enterprise organizations often face challenges due to the sheer complexity of their tech stacks. 

Your enterprise likely deals with a series of client-facing and back-office software products that help run your operations. A typical setup might include multiple CRMs, marketing automation platforms, ERPs, analytics tools, and other specialized software. This mixture of legacy systems and newer, cloud-based solutions frequently lacks cohesion and integration.

DiPietro shares an example of this challenge: "We have another client who owns 70 private golf courses, and each one has its own CRM. They are all run on the same software, but as completely separate systems. Which means they functionally have 70 CRMs and 70 point of sale software instances."

This kind of setup—where each business unit or location uses a separate instance of the same software—creates a highly fragmented system. Although each team may be comfortable using its own CRM, the lack of integration prevents the organization from seeing the bigger picture. There’s no unified view of customer data, making cross-functional collaboration and strategic decision-making difficult.

Many enterprise tech stacks are also made up of a patchwork of both newer cloud solutions and older legacy systems. This combination often results in limited data sharing, manual reconciliation processes, and increased reliance on human effort to make sense of data. 

Without a unified approach, businesses miss out on the full potential of their data, limiting their ability to understand customer journeys, improve marketing efforts, and optimize internal processes.

How Common Tools Get Misused and Underutilized

Many powerful tools often get misused or underutilized within enterprise environments. 

For example, Salesforce is frequently implemented as a sales-centric tool with little integration into marketing or post-sale efforts. DiPietro explains, "In some cases, much of this information feeds into a data warehouse like Snowflake." This means that Salesforce is often isolated, resulting in data silos and management challenges.

Salesforce can also become overly complex, making it difficult to maintain and requiring specialized expertise to ensure data quality. Instead of being a seamless part of the marketing and sales ecosystem, it becomes a tool that only a few know how to use effectively.

Beyond Salesforce, enterprises often misuse their CRMs by using them merely as contact repositories rather than leveraging their full potential for segmentation, automation, and analytics. This underutilization limits the insights and efficiency gains that could otherwise be achieved. There's also an over-reliance on manual data entry, which increases errors and adds unnecessary workload for employees, instead of utilizing available integrations and APIs to streamline these processes.

In many cases, businesses implement complex workarounds instead of seeking expert guidance and best practices, which leads to inefficient systems that are difficult to update or change. Addressing these issues requires a strategic assessment of how tools are being used and a shift towards leveraging them in ways that align with business goals and maximize their potential.

Technical Debt with Marketing Data

Technical debt refers to the accumulation of outdated systems, inconsistent data structures, and manual processes that make managing data increasingly challenging. Over time, enterprises build layers of workarounds, quick fixes, and disconnected tools that hinder growth and efficiency.

Goldberg emphasizes the importance of addressing these issues upfront: "We have to do a data architecture diagram for me not to make mistakes, to set you all up for success. So you don't implement a system in a way that will cause more pain than value." Poor initial implementation decisions can lead to significant difficulties later, especially as the business scales and evolves.

Technical debt makes it harder to implement new technologies and adapt to changing market conditions and requires a significant investment of resources to resolve.

HubSpot: A Platform for Data-Driven Growth

HubSpot’s commitment to data security and analytics has significantly evolved, transforming it into a robust enterprise solution. 

Over the past few years, HubSpot has shifted its focus from being primarily a solution centered on SMBs to becoming an effective mid-market and enterprise-level solution. Herzog explains, "I think HubSpot has evolved significantly in the past couple of years... they've invested a lot in features like their own data migration tools to help make moving data easier."

This evolution has been accompanied by introducing tools within the Operations Hub, which have greatly improved data management capabilities. These tools include:

• Programmable automation and API
• Data sync
• Data formatting and quality tools
• Datasets
• Custom dashboards and reporting
• Snowflake data share integration.

Herzog highlights these advancements: "Not just running custom code and being able to format data in workflows, but also creating data sets within HubSpot to have new ways of creating reports and understanding your data." These innovations make it easier for businesses to migrate, manage, and gain actionable insights from their data, enhancing HubSpot's appeal to larger, more complex enterprises.

With the introduction of Datasets—now available in Operations Hub Professional as of the recent INBOUND conference—HubSpot is even further equipping organizations with the ability to create more customized reports and a deeper understanding of their data.

Consolidating Your Tech Stack with HubSpot

For many businesses, consolidating their tech stack can lead to more efficient data management. DiPietro explains a real-world scenario: “In one client’s case, everything feeds into Snowflake, but we worked with them to strategize which information goes into Snowflake, and then they feed that into HubSpot, and HubSpot becomes the center of the marketing stack for them."

This way, HubSpot acts as a central hub for managing marketing activities, replacing multiple point solutions and minimizing integration complexities. 

By centralizing data management within HubSpot, organizations can gain a unified view of the customer journey. This reduces the friction caused by having multiple disconnected tools and allows for streamlined workflows, automation, and more efficient use of resources.

Leveraging HubSpot for Data-Driven Insights

HubSpot's reporting and analytics capabilities empower businesses to take actionable steps based on insights that truly matter. With customizable dashboards and reporting features, HubSpot provides tailored views of key performance indicators (KPIs) that help organizations track and assess their marketing, sales, and service activities.

For more advanced insights, HubSpot integrates with external business intelligence (BI) tools, enabling deeper analysis. DiPietro points out, "Once you get into complex companies that are taking data from all these different sources, you, at that point, should be using a BI tool." 

This flexibility allows HubSpot users to maintain a balance between built-in analytics and external tools when dealing with particularly complex data environments.

Maintaining Data Integrity Within HubSpot

Data integrity is crucial for ensuring that organizations make decisions based on reliable information. Herzog says, "In the case of multiple pipelines, you could shove everything into one pipeline and then report on it based on filters. But understanding how different processes—like upsells versus new business—compare becomes much more challenging."

Keeping data organized and segmented correctly within HubSpot helps organizations draw meaningful comparisons between processes. 

To maintain data integrity, it's essential to set clear data entry standards and ensure proper data routing across different tools and portals. Herzog notes the pitfalls of mismanagement, “when you have separate portals with similar names or verbiage, you could end up sending the same person multiple emails from different portals, creating confusion." 

Proper governance avoids these issues and helps maintain a high-quality customer experience.

Mole Street’s Process for Improving Enterprise Data Quality

Improving enterprise data quality is crucial for creating a foundation that supports business growth and strategic decision-making. 

At Mole Street, we take a structured approach to help clients maximize their data’s value and ensure it is ready for use in all aspects of their operations.

Step 1. Identify Business Needs

The foundation of improving data quality starts with understanding the big picture. 

DiPietro explains, "We want to understand what the end-to-end process is and truly grasp all the different pieces and technology in place." Conducting thorough stakeholder interviews is an integral part of this step, allowing us to gain insights into business objectives and data-related challenges.

We work with clients to define key performance indicators (KPIs) and metrics that will be used to measure success. Then, we document the existing data processes, which helps identify areas where improvement is needed. 

Step 2. Assess Existing Data Stack

The next step is to assess the current data architecture and stack. We focus on understanding how data flows between tools, systems, and departments to identify inefficiencies and opportunities for improvement.

This approach fits in with how HubSpot’s own team conducts technical discovery conversations: Hannah Goldberg, Solutions Architect at HubSpot, explains, "Tell me about the systems you're using today and what business goal they achieve?" Starting with broad questions like this helps uncover areas where data may be siloed and tools underutilized.

During this step, we identify all currently used data sources and systems, including CRMs, marketing automation platforms, and analytics tools. Evaluating integration between these systems is crucial in identifying potential data silos. 

We help organizations envision a more streamlined setup by asking questions about ideal data flow.

Assessing data quality, completeness, and consistency is another key focus. Herzog says, "People kind of live in the dark about it. It comes up a lot when we do migrations." Lack of data ownership often slows down processes and leads to delays.

Through this assessment, we create a clear picture of the current state of data architecture and establish a foundation for improvements that align with strategic business goals.

Step 3. Map Out Future States for Data Architecture

Once we have a comprehensive understanding of the current state, we define a future state that addresses the identified business needs and challenges. This involves designing a data architecture that is flexible, scalable, and built to support desired outcomes.

DiPietro says, "So we might map out a more integrated process where HubSpot is central." At this stage, it's essential to determine whether HubSpot will serve as the primary platform or as part of a broader integrated ecosystem.

We also develop a roadmap for implementation, which includes data migration, integration, and governance processes to ensure a seamless transition. DiPietro emphasizes, "Client relationship management is much more than a sales tool. It ties into marketing, sales, customer service, client experience, and account management. It's more than just a place to track contacts and deals."

Every company is unique, and tailoring the system architecture to industry-specific nuances is key.

DiPietro adds, "When we do tech stack alignment, there are nuances for different industries that are important to consider and factors that need to be thought through." By understanding these nuances, we can build a data architecture that aligns closely with the specific needs of the business.

Improve the Value of Your Systems Architecture

Enterprise data management is often daunting, filled with complex systems and technical challenges. 

Yes, enterprise data management or system architecture, whatever you call it, sounds complicated—but if you concentrate on the fundamentals, it becomes very straightforward.

By establishing precise business needs, assessing your current data architecture, and mapping out a future state tailored to your specific challenges, you can turn what seems like an overwhelming task into a manageable and rewarding process. 

This systematic approach enhances data quality and provides a solid foundation for informed decision-making, growth, and improved team performance. Remember, it's not about making data simple—it's about making it clear and actionable, allowing your organization to thrive.

If you’d like to learn more about how we ensure great data management, read our deep dive about our best practices for HubSpot migrations.