Data Enablement: The Human Work Behind Successful AI
Every company says it wants to be “data-driven” and “AI-ready,” but most are trying to get there with the same playbook: buy more tools, add more dashboards, call for clean data, and hope the problem sorts itself out. My earlier articles point to how companies can proactively address the problem efficiently and effectively—see The 10 processes you must lock down and Fix the Metadata, Fix the Machine
The challenge is that every team is impacted by bad customer engagement data, but no team owns enough of the upstream processes—or enough of the downstream context—to fix it for everyone else. What’s missing is a formally owned role accountable for turning disparate, disconnected customer engagement data into a governed, reusable dataset that helps tools like AI deliver on their efficiency and effectiveness promises—Data Enablement.
What is Data Enablement?
Data Enablement designs and governs the shared language, patterns, and guardrails that allow teams to collect, access, and reuse customer engagement data safely and independently—by default. It does this by defining a shared semantic framework, embedding it into the points where data is created, attaching usage rules to governed metadata, and providing self-service access patterns that let teams use data correctly without needing constant oversight.
It overlaps and partners with existing functions like Data Governance, Data Operations, and Data Science but is differentiated in two ways.
How it’s different: Scope
Its scope is specifically customer engagement data (marketing, sales, service, product engagement signals).
a. Historically, customer engagement data is under-governed because it wasn’t treated as business-critical and the volume and variety of creation points made data difficult to collect, align, and analyze.
b. Until the past 5–10 years, utilizing customer engagement data as a scalable business driver hasn’t been a priority. The advent of AI and the push for companies to be more “data driven” has changed this.
How it’s different: What it optimizes
It focuses on how companies collect data and apply standardized, governed metadata to describe customer engagement data so that any team needing to use data for any purpose has—and can use—the data they need.
a. Data Governance traditionally focuses on policing data accuracy and use of a smaller set of business-critical data (like financial data or product performance).
b. Data Operations traditionally focuses on how to move and use marketing lead generation, sales funnel, and digital retail data.
c. Data Science traditionally focuses more on applying advanced data solutions like AI. Traditionally, they have a larger data remit than Data Governance and Data Operations and address data quality issues by spending a good amount of their time cleaning and aligning data before they can use it—efforts the Data Enablement function would address upstream.
Data Enablement is:
• An enterprise solution. One set of standards and solutions can be applied and modified across functions, departments, and teams. See the list of 10 processes you must lock down here:
• A service-oriented operating model charged with making it easier for individual teams to collect, reuse, and leverage customer engagement data.
• Designed to scale with self-service solutions, easy access to data definitions, and impactful, reusable applications that drive immediate business value.
Data Enablement is not:
• A late-stage approval gate that slows work down.
• A permanent ticket queue of requests that never get addressed.
• One team that magically understands every bespoke business context.
• A “boil the ocean” solution. Pre-determined standards mean small, localized use cases can be implemented independently and later scale and merge into one cohesive ecosystem.
Why now?
Most businesses are already paying the price of “dirty data” many times over in the form of rework, delays, duplicated forms and logic, manual interventions, and compliance fire drills. This makes work more expensive than it needs to be—and it limits the business’ ability to implement more robust, enterprise-wide data-driven solutions that can unlock efficiencies and effectiveness at scale.
Data Enablement converts these hidden recurring costs into a reusable capability: invest once in creating, collecting, and describing data, and stop paying repeatedly for cleanup and exceptions.
What does Data Enablement do?
A) Make data collection easy—wherever data enters the business
Data Enablement standardizes how engagement data is described at every entry point—not just CRM or marketing forms, but also service schemas, data purchases, media plans, work tickets, strategy planning, and playbook design. Whether Data Enablement builds the form or simply provides standards and approval, the responsibility is the same: all engagement-data inputs follow the same rules, so teams don’t have to invent their own.
B) Ensure data is usable, clean, and available
At a high level, Data Enablement is accountable for data hygiene and availability—making sure engagement data is complete, discoverable, and ready for reuse. The focus isn’t perfection but making trusted data easier to use than untrusted data.
C) Anchor everything to a shared framework (the 6Qs)
All engagement data is mapped to the same six variables: WHO, WHAT, WHERE, WHEN, WHY, and HOW. This shared semantic layer is what allows data collected for one purpose to be reused safely across teams and tools.
D) No open text fields, no “other”
Every classification choice uses governed metadata. Unstructured content (like transcripts, creative, or documents) is fine. Unstructured meaning is not.
E) Use primitives and composites to balance stability and flexibility
Primitive classifications are the baseline, immutable, self-apparent facts whose meaning never changes once defined. Composites are built from combinations of primitives and are free to vary by business need or use case. Because meaning lives in primitives, the need to manage “master” composites that all teams must use is eliminated, making the entire system easier to manage and use.
F) Capture guardrails upstream and let them inherit everywhere
Instead of repeatedly hard-coding data usage rules inside individual platforms, Data Enablement attaches restrictions as a primitive category, inherited by composites and delivered with metadata that travels with the data. Downstream systems can then enforce the same rule set based on those restriction categories—without reinventing the logic in every tool.
G) Enable self-service access by design—not by tickets
Because primitives and composites are standardized and easy to understand, teams can request and use data without being subject matter experts in the company’s overall data structure. While the Data Enablement team can step in and write queries (typically when requests are unusually complex or capabilities are still immature), they don’t have to.
H) Measure, learn, and improve continuously
The Data Enablement team uses the data it collects to model how to measure and optimize a shared business service. The team monitors metrics like metadata completeness, number of teams self-serving, number of escalations and remediations, and overall data use to track performance and be held accountable for implementing continuous improvement solutions.
Implementing Data Enablement
While every business is unique, successful implementation of Data Enablement follows the same pattern:
Choose a standard framework (like the 6Qs)
• The Data Enablement team owns enterprise standards (6Qs, primitives, patterns).
• Practitioners and subject-matter experts propose additional primitives and own local composite assembly and usage with Data Enablement support.
• The Data Enablement team partners with IT to establish data platform solutions.
Identify processes to pilot
• Processes that are already well defined but could benefit from clean data
• Process owners interested in efficiency improvement and automation
• Processes that can be replicated and reused with minimal rework
Identify and optimize adjacent processes
• Processes where composites from one process are referenced and used in another
• Processes where clean data upstream unlocks automation and other solutions downstream
• Processes that, when aligned, collectively drive greater overall efficiency
Continue expanding until Data Enablement solutions become “how we do business.”
Conclusion
Data Enablement is not about adding process for process’s sake. It’s about making the way a company already works clearer, safer, and more scalable by design. By standardizing how customer engagement data is described, governed, and reused—upstream, where it’s created—businesses can move faster with less risk, unlock real self-service, and finally turn data into a compounding asset instead of a recurring problem. You don’t need to replace your tools or boil the ocean to get started. You need to agree on how work gets described, build guardrails once, and let teams build on top of them.
The quickest win is not a new tool—it’s a documented operating model.
