How Aware Super Operationalized Critical Data Elements: A Practical Blueprint

When two major Australian superannuation funds merged to form Aware Super, the newly combined organization faced a challenge familiar to many enterprises: strong governance policies gathering dust while disparate systems and definitions created daily friction across the business.

Managing over AUD $200 billion for more than one million members, with investments spanning equities, property, infrastructure, and private markets, Aware Super couldn't afford data governance to remain theoretical. As a regulated entity, they needed a practical path from framework to execution—and they found it through Critical Data Elements (CDEs).

As a regulated entity operating under the Australian Prudential Regulation Authority (APRA), Aware Super needed more than governance policies—it needed provable control over its most critical data. That journey began with Critical Data Elements (CDEs): the small subset of data elements most essential to the organization’s operations, decision-making, and compliance.

“CDEs are the most important data assets your organization relies on to operate, make decisions, and stay compliant,” explained Natalie Hogan, Senior Manager of Data Quality and Enablement at Aware Super. “They’ve got to be business critical, they’ve got to be high impact … very select—the most sensitive, most important bits of data.”

By zeroing in on these “must-have” data elements, Aware Super found a way to prioritize what matters most, satisfying regulators while enabling business agility and analytics maturity.

In a webinar, Hogan shared the organization's journey to operationalize data governance through CDEs. Her insights offer a pragmatic playbook for any organization struggling to translate governance ambitions into business reality.

Starting point: Framework without execution

Aware Super began with strong governance fundamentals—comprehensive policies, standards, and frameworks—but lacked a way to bring them to life at scale.

"We had a really good framework. We had lots of really good policies and standards," Hogan explained. "So what we needed to do was that next step in the process—how do we actually operationalize this to all of our domains in the business?"

Post-merger, the challenge was clear: different systems, inconsistent definitions, and rising regulatory expectations. The governance team needed to move beyond documentation and embed data accountability into daily operations across multiple business domains.

Hybrid model: Central standards, distributed ownership

Rather than choosing between centralized control or fully federated governance, Aware Super adopted a hybrid approach that balanced both.

"We ended up with a hybrid model of a federated and centralized operating model," Hogan said. "Domains were very much accountable for their data quality and the data they owned within their domains.

Then we had the centralized functions—our data services team, data governance team, and data quality team—we established the guardrails. We helped to uplift the organization in data literacy, and we do ongoing mandatory training to keep uplifting all of those data stewards and owners."

The team started by mapping the organization, conducting extensive meetings to understand the domain structure. They aligned domains functionally with divisional structures, ensuring that reporting could flow up to the executive level in a way leadership could understand and act upon.

The five-step framework: Defining domains as “done”

"One of the issues we found early on was understanding our definition of done—how we could understand when a domain was considered under governance," Hogan noted. 

To avoid endless initiatives without clear outcomes, Aware Super established a five-step framework to bring each domain under governance:

1. Accountability: Identify data owners and stewards for each domain

2. Discoverability: Document CDEs and create a complete map of what's produced and consumed

3. Risk identification: Map risks and controls throughout the data lifecycle

4. Lineage: Trace data flows from source to consumption

5. Data quality: Implement monitoring rules and metrics

This framework gave teams clarity and allowed leadership to track progress across domains with consistent metrics.

The "100 CDEs" problem: Getting real about criticality

One of the most common challenges emerged early: domain owners who insisted everything was critical.

"Sometimes they would come back with a few CDEs. Some of them came back with 100 because they felt that everything was very highly critical," Hogan recalled.

The team developed two tactics to address this. First, they reframed the question: "If you were to get 10 to 20% of this particular data wrong, would it cause a severe or major impact to the organization—the whole organization, not just your team?"

Second, they made the implications clear: "We would just casually mention that any of these we get in through our governance process, you then have to monitor: ‘You will be looking after these and reviewing them on a year-to-year basis.’ That usually helped them understand what would be critical because they didn't want to spend all day, every day just looking after data quality."

Through this process—including deep analysis of lineage and risk—the team often declassified candidate CDEs. For example, while a data element might be highly regulated, if it only impacted 20 members, the likelihood of severe organizational impact decreased significantly.

Prioritization: The risk-based approach

Not all domains were treated equally. Working with their risk function, Aware Super used risk appetite statements to identify which domains had zero tolerance for error.

"We looked at which of our domains were actually more highly critical," Hogan explained. "We worked with risk to determine which data we have no risk tolerance for getting wrong and which ones we have a little bit of wiggle room. That helped us decide which domains we were going to start with first."

They divided domains into four tranches based on criticality, starting with the highest-risk areas, such as member data.

Tooling and documentation: Making stewardship manageable

Aware Super implemented Alation as its data catalog, customizing it extensively to support its CDE program. They created a standard CDE template within Alation's glossary function, capturing key attributes like rationale, regulatory reporting requirements, PII classification, and ownership.

Rather than overwhelming stewards with administrative burden, the governance team took a practical approach. "The bulk upload tool has been really helpful for us," Hogan said. "Once we've got through the candidate CDE process and gotten risk reviewed and signed off by our data governance council, we could bulk upload them for the data stewards. They were just being introduced to the tool—we were in it a lot more."

This allowed stewards to enter a system where their CDEs were already documented, which they could then refine as composers, rather than starting from scratch.

The team also built a library of data risks and controls directly into the catalog. Each risk—like "incomplete data"—includes a complete definition, examples, and suggested controls. "We wanted to have these data risks and controls documented, but we didn't want it to be a tick-box exercise," Hogan emphasized. "We wanted it to be useful so domain owners and stewards really understood the flow of the data."

Building relationships and capability

Technology alone didn't drive adoption—relationships did.

"An important part of this whole process is developing those relationships with the business," Hogan said. "We have a really great relationship with all of our data stewards now. We can jump in and help them whenever they need help, and if I say we want to have a bit of a push towards this by the end of next week, they'll say 'yep, cool, I can help you out with that one.'"

The team ran a "Data Catalog Awareness Day" with demonstrations from different perspectives—stewards, data engineers—and created a data community in Microsoft Teams with dedicated channels for stewards and catalog support.

Measuring and reporting progress

The five-step framework enabled consistent progress reporting to governance councils and executives.

"We could easily break it up into a percentage-based completion," Hogan explained. "Each of the execs could see their particular area, and we could report to them anytime they asked. This is our high criticality domains, this is how far along the process of being under governance we are, this is our next step."

These visible metrics proved essential for maintaining executive sponsorship and demonstrating value.

Organic adoption: The unexpected win

Perhaps the strongest validation came from unexpected sources. Teams not yet in scope began proactively reaching out.

"After there is that understanding of what the data catalog can do and the documentation within there, we've had other areas of the business come to us and say 'how do I do this? How can I almost govern myself? I want to bring forward my governance,'" Hogan shared.

One example: the emissions reporting team, facing upcoming regulatory requirements, used the catalog to establish consistent data definitions and create a "contract" for data providers—ensuring quarter-over-quarter consistency and audit readiness.

Key takeaways: Lessons for other organizations

When asked for her top advice on operationalizing data governance, Hogan's guidance was clear:

Don't wait for perfect. The team didn't have everything figured out before rolling out to the first domain. They started, learned, improved, and even went back to refine their initial domain work based on lessons learned.

Be flexible and chunk it down. "Understand your definition of done and chunk it down. You learn as you go. Bring those learnings back into the process as you're developing it out. It's going to be different for everybody because all of their data is different."

Win over your stewards. Make their lives easier, not harder. Automate administrative tasks. Build libraries and templates. Provide clear guidance. "As composers, they could come in and make changes as they learned more about it or had more information to add in."

Get executive buy-in early. "We started with the execs and the data owners and highlighted the importance and the responsibilities of domains in actually looking after the data and being responsible for data quality. Then they helped us assign somebody and help to make way for that resourcing."

The bottom line

Aware Super's journey demonstrates that operationalizing data governance doesn't require perfect plans or unlimited resources—it requires pragmatic prioritization, clear milestones, relationship building, and iterative learning.

By focusing ruthlessly on what truly matters with CDEs, defining concrete steps to completion, and building capability alongside technology, they transformed governance from policies on paper to practices embedded in daily business operations.

As Hogan summarized: "It's just understanding that we're learning as we go. Every domain is different. Just keep learning and bring the stewards along for the journey as well. They're a part of it."

Explore CDE Manager to see how it can simplify and streamline your governance program.

Watch the full webinar to hear more details about Aware Super's CDE journey and see their implementation in action.