Lessons Learned: Implementing Digital Transformations & Why I Am Now a Field CDO

All massive organizations face challenges and opportunities of varying scales and take customer intelligence and experience very seriously. After leaving the core mission work at the National Security Agency, I worked on solving challenging commercial problems at the intersection of digital and data transformation for companies such as Philips, Concur, Nike, American Eagle, and Zendesk. What quickly fascinated me was that, regardless of industry, I found that each of them experienced “False Starts.”

“False Starts” occur when initial plans result in the need to restart, shift funding, or change strategies. They require rebuilding trust with business partners. Following are a few valuable takeaways from my path to a Field Chief Data Officer (CDO).

Lesson 1: No One Funds Architecture

Those funded to rebuild data and analytics foundations know there is a struggle with competing business priorities and time to launch. Executives need to answer questions such as, “How long will it take to deliver? How much money will it cost? And how fast and often will I be able to provide real value?” Once that funding is granted, it immediately becomes a target — a way to be measured against others’ priorities. I learned early in my career that every platform, capability, and feature being worked on should have direct lines to multiple transformation initiatives and key strategies. “Whys” rarely change, but “whats” and “hows” can, so roadmaps are as much an expression of strategy as they are commitments of deliverables.

At Nike, after defunding a number of platforms, we quickly aligned on key patterns tied to transformation initiatives. The impact went from me inheriting 70+ platforms that were behind schedule and mostly mapped to 1-5 small teams each to fewer than 25 platforms with 15-35 teams prioritized and all tied to transformation initiatives. Shifting focus to patterns became more about delivering faster and negotiating feature tradeoffs with the business versus talking about technologies and restarting work.

Lesson 2: Cost Savings and Efficiencies = Self-Funding and Innovation

As foundations are rebuilt, data leaders need to consider cost savings and other efficiencies. The ability to leverage these savings as internal reinvestment opportunities helps enable expansive delivery opportunities without the need to adjust capacity. The opportunities are valuable to data leaders because they can be traded for headcount (get more done), software (drive speed, trust, scale, automation, etc.), or — my personal favorite — to start hiding capacity for innovation and product adjacent innovation.

At AEO, we built a “Voice of the Customer Platform,” an initiative funded primarily through software license negotiations and efficiencies. This automation enabled us to engage in better prioritization and impact discussions with our business partners. The platform unlocked value internally for multiple teams and drove deeper trust and flexibility. Teams cut discovery down from multiple people over weeks to minutes by a single person. Insights were delivered on the expected impact to LTV to determine the next best action and message by modeling the response against key indicators and segments. And production partners’ new quality and auditability insights were delivered for the first time. Lastly, it opened team capacity to keep rebuilding the underlying foundational architecture for me (See Lesson 1). These teams could self-service over top of trusted data and reporting as well as the capacity given back to the business to drive better experiences and outcomes: Everyone won!

Lesson 3: Get the Most Core Foundation Right

Data democratization has forced a shift in achieving trust and scale. Three non-negotiable capabilities that ensure success are:

• Establishing a contemporary entitlements service.
• Leveraging modern data catalog services.
• Treating data as an asset.

These capabilities ensure access and visibility across your ecosystem. At Nike, for example, I guaranteed that using our entitlements service and pub/subbing all data events into our metadata catalog would successfully govern anything. At Zendesk, operating fully across distributed platforms was hard without security group management and data attribution/tags. This pattern, when executed at scale, always results in over-provisioned data.

A centralized data catalog has a few major components, and each should be prioritized via a maturity map depending on the business priorities and state of maturity:

• Business Terms and Glossary — Establish “facts,” definitions, and ownership.
• Metadata Catalog — Active metadata management, including business and system-level metadata.
• Discovery & Compliance — Active and passive discovery, inference, and tagging.

Treating data as an asset means understanding all the data's attributes, usage, and ownership. Attribute Based Access Control (ABAC) — which NIST defines as providing “greater efficiency, flexibility, scalability, and security than traditional access control methods, without burdening administrators or users” — needs to be the foundational building block, often shifting from RBAC (Role) as the core for FGAC. This enables entitlements services to scale across a distributed data landscape and simplifies policy and group management to achieve Zero Trust. RBAC and PBAC (Policy/Purpose) still play a crucial role, but they dynamically operate with the asset per the data’s attributes.

Lesson 4: Storytelling > Insights > Data

I once heard a CEO state, “I see and understand the data, and your recommendations make sense, but I don’t like the way the data makes me feel. So, I’m not going to listen to it.” At that moment, I learned that the interest is not so much in the actual data but in the insights it delivers. Moreover, insights elicit feelings and should provide recommendations tied to those feelings. How people consume shared information varies and therefore requires variations of storytelling. Business leaders consume information differently than engineering leaders and care more about the recommendations and impacts (“Why” and then “What”). At the same time, engineering leaders often want to get to the details (“What” and “How”) to validate and analyze the opportunity and the impact on the rest of their ecosystem and commitments. These worlds cross best at mission and customer outcomes, but how they consume information to get to that result widely varies based on roles and what those leaders are accountable for.

When I helped rearchitect and onboard teams into a new PaaS/IaaS platform at Phillips, our adoption and enablement took off when I shifted the first 1-2 days of a 4-day engagement to just talking about business goals, friction in speed and delivery, what the teams would work on if they could go faster [and safer], and what it would mean to their business goals. The final two days left massive flexibility as we grounded every debate to business, NOT technology. We all walked out aligned, aware of pivot opportunities or triggers, and fully confident that we would meet the business needs. Teams were excited about their opportunities due to the architectural changes and did not consider it a competing priority.

Business leaders might like technology but prefer not to worry about it. Peer engineering and delivery teams care more about trusting the implementation than tools. They don’t want to be awakened in the middle of the night and would prefer to celebrate with their partners! Making technology invisible allows everyone to focus on the most important things.

No False Starts

A “No False Start” means connecting all work to business value and impact, prioritizing work with shared OKRs across the business, and releasing capabilities in a product model. It means taking savings and efficiencies to reinvest in unlocking capabilities, people, and innovation to drive more value to the business with evolving trust and latitude. It means getting the core right — entitlements, data catalogs, and treating data as an asset with foundational attribution — to enable the massive scale. And it means building a muscle of storytelling with data, accomplishments and a vision, all connected to the enterprise’s highest “why” priorities, mission, and values.

I switched to Field Chief Data Officer because I wanted to help 80-800 enterprises to navigate this journey, help build a more connected tech community as data mesh patterns expand, and drive impact at a larger scale. This role allows me to focus externally and internally so that we, as a community, can share and grow together.

About the Author

Nik Acheson is Field CDO at Okera. Acheson — an experienced data and technology leader – has delivered digital and technology transformations at massive scale at companies such as Nike, Zendesk, AEO, Philips, Concur, and the NSA. He is the Field CDO at Okera, the leader in delivering trust with enterprise data. Acheson’s mission is to enable “No False Starts” by helping Okera’s partners evolve and deliver advanced business transformations through modern technology approaches.