Decision Quality in Product Management and Digital Transformation

In the ever-evolving realm of product development and digital transformation, decision quality emerges as a pivotal, yet often overshadowed force. While it's easy for many to dismiss it as an abstract ideal, confident in their organization's mastery over it, the truth is far more nuanced. Decision quality, in its tangible essence, stands parallel to any product we manifest, just in a uniquely different dimension.

The real fuel for analytics is knowledge management. It underpins:

1. Methodologies – Determining the approach for decision-making.
2. Data and Information Scope – What is the breadth of data that employees, or "knowledge workers," access for decision-making?
3. Tools Reliance – The tools and technologies that aid in extracting and applying this knowledge.

'bout to go nuts

Imagine an enterprise on the cusp of a digital transformation, aiming to launch new in-house products. Resources are limited. The challenge? Prioritizing which features to allocate these resources to.

On the surface, product managers may evangelize certain frameworks to guide these decisions. And while these may work seamlessly at the product level, scaling them to an enterprise level – especially when deciding on program priorities (across a portfolio of products) – can be murky.

This is the juncture where conventional prioritization techniques wane in their reliability. Decision quality at this scale requires a more holistic approach, integrating not just analytics but a robust knowledge management system.

A Simplified Explanation of dilemma

Imagine you're in a massive warehouse, filled with countless switches, dials, and buttons. Each one represents a decision you have to make. You're not merely choosing between pressing a button ("yes") or not pressing it ("no"). Instead, you first need to discern which of these numerous options you should even consider.

As you scale up, making decisions for larger programs, you're handed a set of metrics, like a cheat sheet. These metrics, derived from knowledge management, offer a guide. Yet, even with this guide in hand, you're not entirely confident about which button to push.

So, you add more metrics to your cheat sheet: KPIs, OKRs, and the like. But here's the catch – with every new metric you add, your decision becomes exponentially more complex. A simple yes/no decision might be at level 1. But when options come into play, starting at difficulty level 2, things get tricky. Add another factor (like wanting more profit) and you're at level 4. Introduce another dimension (like needing to launch faster) and the difficulty jumps to level 8, then 16, and so on.

At the program management level, it's like juggling several intricate puzzles at once. Each decision you make for one puzzle impacts the others. As things keep shifting, the challenge becomes overwhelming. Many managers, understandably, get frazzled. Losing sight of their original objective, they might resort to gut feelings or delegate decisions to trusted team members, hoping for the best.

So, what's the solution?

The key is understanding that good decision quality isn't something you merely declare—it's something you achieve. This is where knowledge management shines. It provides a way (though not a guarantee) to record the outcomes of these complex situations and manage them. Equipped with the right tools, you can then retrieve and apply this stored knowledge.

By systematically capturing past decisions and transforming them into actionable knowledge, organizations can gauge and refine their decision quality over time. It's like having a constantly updating guidebook, giving you a clearer view of which buttons to press in that vast warehouse of choices.