Closing the Data Gap with AI.
Introduction
Today's world is all about data. Organizations increasingly depend on data to make informed decisions, improve efficiencies, and drive innovation. But there lies a hard truth that many of us have encountered: managing data can feel like trying to corral a herd of wild horses. If you are not careful, the data starts managing you. That is where we find ourselves faced with a critical choice: Do we manage data, or does data manage us?
At the heart of this problem lies the data gap—the difference between the data we need and the data we have. Whether incomplete, inaccurate, or inconsistently managed, the data gap creates inefficiencies and erodes trust. This problem is not limited to general data management, but extends to Strategic Portfolio Management, including Project Management, where decisions are highly dependent on timely and accurate data. As we continue to wrestle with these challenges, it is becoming increasingly clear that trust can only be earned by focusing on three critical pillars:
- Data quality,
- Data granularity and
- Minimizing administrative work.
Building Trust
Why are data quality, granularity, and minimizing admin work critical to data management success?
- Data quality is fundamental. It is only possible to make reliable decisions if your data is consistent or full of errors.
- Data granularity refers to the level of detail in the data. More granular data provides better insights and enables more accurate predictions, but it requires thoughtful management to prevent overwhelming users.
- Administrative work is the silent productivity killer. We realized something crucial early on—no matter how many roles, skills, or tools you have in place, if you ask project managers to spend 50% of their time on administrative tasks, your processes are broken. And no one will volunteer to spend that much time on administration.
At the end of the day, systems are only as good as those managing them and the information being entered into them. Without a dedicated team to effectively manage the data and processes, your organization will be held back by inefficiencies, bottlenecks, and an ever-widening data gap.
The Past: The Wooden Era
To understand where we are today, let us rewind 15 years to the Wooden Era of Resource Management.
In 2008, I started my career at a multinational company and was tasked with helping to implement a Project Portfolio Management (PPM) solution. The goal was to create transparency in resource capacity/demand, particularly in IT, which was becoming a bottleneck as the company scaled.
At first, things went smoothly. We assigned project managers and allocated resources, and everything seemed to be under control. But then we hit our first roadblock: each project manager wanted the best resources. This resulted in 20% of our IT staff being overworked while others were idle—so much for transparency!
To fix this, we decided to assign roles rather than specific people. This allowed resource managers to allocate work more evenly. It worked—for a while. However, we soon discovered that project managers did not update tasks when roles turned into named resources. So, we introduced timesheets to track actual versus planned work, but convincing people to use them was difficult.
We introduced skill-driven resource allocation instead of roles, adding another layer of complexity. However, this only created more administrative tasks, and we soon found ourselves overwhelmed. This highlighted a fundamental truth: managing data is more than just implementing tools or processes; it requires dedicated resources to ensure data quality and maintain trust.
The Present: The Silicon Era
Fast-forward to today, and we have entered what I call the Silicon Era. While we have progressed significantly, we are still dealing with some of the same challenges—just on a larger scale and with more advanced tools.
Many organizations use powerful platforms like ServiceNow to manage resources and streamline processes. The goal is to eliminate redundant solutions and consolidate everything onto a single platform. By integrating planning and delivery, organizations aim to create a single source of truth—a critical step toward closing the data gap.
After the agile transformations, we achieved better organizational structures. However, most organizations still operate in siloed teams with top-down hierarchies and more bureaucratic processes. Achieving cross-functional collaboration and quick adaptability remains an area for improvement.
AI is starting to play a more significant role in helping organizations manage their data more effectively, but we are not quite there yet. The challenge is that AI can only work if the data it relies on is trustworthy. AI systems can only deliver on their promise with accurate, granular, and up-to-date data. This highlighted another crucial moment; we should do some reverse thinking and focus on filling that data gap with AI rather than how we will automate our Resource Management or processes.
The Future: The Digital Circuit Era
Looking ahead to the Digital Circuit Era, AI will fundamentally change how we approach Resource Management. Instead of teams chasing after tasks, AI could match tasks to the right people based on their skills, availability, workload, and interests. This will radically change how we work as AI becomes the central manager of resources and workflows.
However, this future can only be realized if we solve today's data gap problem. Without trustworthy data, AI will only amplify the issues we already face.
To ensure the success of AI-driven Resource Management systems, we need to focus on data quality and granularity and minimize administrative work now.
Conclusion: Trust, Data, and AI
Ultimately, the question remains: are we managing data, or is it managing us? The answer lies in our ability to close the data gap by focusing on the three critical pillars. If we can achieve this, a future where AI takes over resource management is not only possible but inevitable.
Until then, we must recognize that trust is earned, not given—and that trust starts with effectively managing data today, not just waiting for AI to solve our problems tomorrow.
Are you curious about how this works in practice?