The Paradox of Data Abundance: Why Go-to-Market Teams Struggle
Never before have businesses had access to so much data, with so much potential to transform performance. So why do so many Go-to-Market Teams feel like they are drowning instead of thriving?
1. The Data Paradox
As businesses grow more sophisticated, they both require and generate more data. New technologies are applied to meet those needs — and in turn create even more data and new opportunities to use it. The cycle accelerates.
Today, organizations have and use more data than at any time in history. Yet many disciplines — especially the Go-to-Market (GTM) Teams that engage customers directly (Marketing, Sales, Product Management, and Customer Service) — are struggling to realize its full value. Instead of thriving, many complain of drowning in data.
The paradox is clear: more data should mean more clarity, but in practice it often leads to confusion, fragmentation, and missed opportunity.
In this article, we’ll explore how we got here, why Go-to-Market Teams in particular lag behind, and what it will take to wrangle data into solutions that transform how businesses engage their audiences.
2. From Scarcity to Abundance
Managing data has always been at the core of business. From clay tablets to papyrus, from ledgers to punch cards and magnetic tape, organizations have always sought better ways to capture, store, and access information. Digital technologies were the next evolution, but they still came with restrictions. For much of the modern era, data remained expensive and scarce.
Storage was expensive. Companies had to invest heavily in on-premise infrastructure. The flood of data generated by digital engagement platforms far exceeded what most organizations could store, so much of it was simply discarded because storing it wasn’t feasible.
Moving data was expensive. Data warehouses acted primarily as storage vaults, not fluid environments. Shifting information between systems required costly hardware and long lead times.
Analysis was expensive. Processing vast volumes of data required not only major upfront investment, but ongoing capital as both demand and technology evolved.
The result was a world where data resources had to be rationed. Most use cases were limited to periodic reporting and after-the-fact analysis, rarely in real time.
Then cloud computing changed everything.
Storage scales elastically. Cloud eliminated the need for capital-heavy provisioning; organizations can store essentially unlimited data on demand.
Data movement became easier. Cloud platforms, APIs, and integration tools allow data to flow across environments with far less friction.
Processing power expanded dramatically. Distributed compute brought real-time analytics and advanced modeling within reach of organizations of every size.
Data structures became more flexible. The evolution from rigid flat files to relational databases, and now to data lakes and graph models, further reduced the constraints of scarcity, making it easier to capture and query vast amounts of information.
This transition gave rise to enterprise-grade platforms like Snowflake, SAP HANA, Oracle ERP, Teradata, ServiceNow, Workday, and others — many of them built to meet the structured, transactional needs of Finance and Operations.
Finance and Operations are already making the transition into this new era. Go-to-Market Teams, however, have not kept pace. Why? That brings us to the heart of the issue.
3. Abundance Without Advantage: The Go-to-Market Data Gap
Not all business functions entered the age of data abundance on equal footing.
Finance & Accounting have always been data disciplines. Every transaction is tracked, categorized, audited, and reported with rigor.
Operations & Distribution became data-intensive in the 1990s with ERP and supply-chain optimization — and crucially, they operate as relatively closed systems with repeatable processes. Clear handoffs, standardized workflows, and stable telemetry make tracking and measuring outcomes far easier.
Go-to-Market Teams (Marketing, Sales, Product Management, and Customer Service) have only recently accelerated their use of data, driven by digital channels and rising expectations for personalization.
What this means in practice: Finance and Operations have been preparing for this moment for decades — even centuries — by codifying standards, controls, and repeatable processes. Go-to-Market Teams, by contrast, are only now building the disciplines and infrastructure to keep pace.
Go-to-Market Teams lag behind not because they’re resistant to data, but because they deal with a fundamentally more complex reality.
A. Measuring Go-to-Market Performance is Harder.
Finance is inherently numeric.
Operations is built around closed-loop systems that naturally produce measurable outcomes.
Go-to-Market Teams are different. Success often depends on audience perceptions and feelings — highly variable, difficult to measure objectively, and often influenced by external factors beyond the company’s control.
B. Go-to-Market Engagements are More Complex.
Customers interact with companies across many channels: web, advertising, sales calls, social media, print, events, service touchpoints, and product usage.
They do so for many reasons: awareness, research, purchase, use, service.
Each engagement usually requires coordination across multiple teams.
Collecting, aligning, and making sense of this data is orders of magnitude more complicated than tracking financial transactions or monitoring package deliveries.
C. The Consequence.
Because Go-to-Market Teams operated for decades in an era of data scarcity, they could only collect a fraction of the data available. And what they did collect had to be defined narrowly — in ways that justified the expense of capturing, storing, and analyzing it. In other words, data wasn’t just described for what it was, but for the specific purpose it was collected to serve.
That approach left much of the surrounding context — and often entire categories of potentially valuable data — behind. It also created definitions that were highly specific to the team or project that generated them.
Those practices made sense when data was scarce and expensive. But in today’s era of abundance, they have become liabilities:
Narrow, purpose-driven definitions don’t translate across teams.
The context of what data represents is often lost.
Local curation fragments the enterprise and prevents reuse.
4. Why This Matters Now
The explosion of customer engagement data — combined with enterprise demand for personalization, automation, and AI — makes this gap more urgent than ever. The very techniques that promise to transform Go-to-Market functions all rely on vast volumes of comprehensive, clean, and alignable data:
Capturing and tracking audience activity across the entire customer lifecycle.
Building 360-degree views of the customer.
Designing automated, measurable journeys that span multiple channels.
Delivering real-time insights and powering personalization engines.
Enabling AI-driven solutions that can learn, predict, and optimize at scale.
None of these outcomes are achievable with the curated, siloed, and purpose-bound data practices that Go-to-Market Teams still rely on today. In fact, as organizations rush to capture and store more data than ever before, the problem is getting worse.
When data is inconsistent, narrowly defined, or fragmented across systems, adding more of it doesn’t unlock insight — it compounds confusion. Instead of moving closer to the promise of transformation, most organizations find themselves with a larger, more expensive version of the same problem.
The promise of personalization and AI won’t be unlocked by sheer volume alone. It will be unlocked by data that is clean, comprehensive, and consistently described.
5. The Schema6 Point of View
Closing the Go-to-Market data gap doesn’t mean reinventing the wheel — it means borrowing the best practices from other disciplines and extending them.
From Finance, we take rigor: every activity precisely described, auditable, and consistent.
From Operations, we take granularity: breaking complex work into measurable steps that can be rolled up into outcomes.
But Go-to-Market requires more. Schema6 delivers that “more” through three integrated products:
A universal data schema. The 6Qs framework provides a shared, easy-to-understand way to describe any engagement — regardless of audience, medium, objective, or discipline. It captures context at a granular level and allows definitions to be recombined into patterns for future reuse.
Process design and application. We zero in on the exact points where data is generated and collected, embedding schema definitions into workflows with surgical precision. This minimizes disruption, reduces manual effort and errors, and leverages hierarchical/relational structures to build comprehensiveness as you go. The result: transformative impact with minimal noise.
Consultative services for leadership. We help organizations review and restructure responsibilities to ensure the shared definitions, services, and solutions are effectively governed and delivered. This guidance ensures consistency, scalability, and enterprise-wide adoption of data-driven Go-to-Market practices.
This is what Schema6 was built to deliver. By systematically answering Who, What, Where, When, Why, and How (the 6Qs) at the point of creation — and aligning schema, process, and governance — organizations gain data that is cleaner, more comprehensive, and more reusable. The result is immediate business value today and long-term enterprise advantage tomorrow.
6. Bringing It All Together
The journey from data scarcity to today’s age of abundance has reshaped every business discipline. Finance and Operations adapted early, codifying rigor and granularity into the way they work. Go-to-Market Teams, however, inherited practices from the scarcity era that now hold them back: narrow definitions, fragmented taxonomies, and siloed data that can’t scale.
The path forward is clear: Go-to-Market organizations must combine Finance-level rigor with Operations-level granularity, powered by a universal schema, pragmatic process design, and enterprise governance. This is what Schema6 was built to deliver.
The promise of data abundance can’t be realized by sheer volume alone. It requires data that is clean, comprehensive, and consistently described — captured at the point of creation, embedded into daily processes, and reusable across the enterprise.
👉 The next article in this series: “Simple Isn’t Simple: Why Oversimplified Data Breaks Go-to-Market.”
Interested in exploring how to make your Go-to-Market data cleaner, more comprehensive, and more usable? Let’s start the conversation.
The next generation of customer engagement will be built on how well you manage data. If you’d like to discuss practical ways to get there, let’s connect.
