Why McKinsey's Vision for Data Products is Already a Reality in Delight

When I first read McKinsey’s recent article, The missing data link: Five practical lessons to scale your data products, honestly, at first glance, I jokingly wondered if we’d had a data breach.

Why?

Because their findings align so closely with what we’ve been building in Mindfuel Delight.

Over the last few years, three persistent challenges have repeatedly surfaced in our conversations with 100+ data leaders. McKinsey’s article echoes these exact hurdles, validating our approach.

I'd like to share my perspective on these challenges and illustrate how Delight directly addresses each one, helping data leaders escape the cycle of stalled initiatives and truly scale their impact.

Let’s take a closer look.

Challenge #1: Lack of use case overview and data value contribution

Most companies have a mess when it comes to understanding their use cases and the real value they deliver. Different teams build their own data products independently, without any central view of what's happening. It's chaotic and inefficient.

The lack of a unified view of their use cases causes misalignment and lost opportunities.

McKinsey gets straight to the point: data product programs should start and end with value. Not with pipelines, not with dashboards, but with the impact each use case can generate.

"No data product program should begin until leadership has a firm grasp of the value that each use case can generate and prioritized the biggest opportunities." – McKinsey

Most of the time data initiatives are approached as isolated projects – because someone had a good idea, not because they’re part of a strategic plan. Teams don’t have context around how their individual initiatives fit into broader organizational goals, creating confusion, duplication, and misalignment. Without strategic clarity, data initiatives risk becoming low-impact, redundant and costly exercises with minimal tangible results. And now with GenAI moving at full speed, we’re seeing the same trajectory: disconnected experiments without clear business alignment, making it even harder to scale real impact.

Also read: Mind the Gap: Aligning AI Investments with Strategic Goals

McKinsey’s suggestion for this is to create a map of use cases and their corresponding data products. This means that decision makers can identify and prioritize high-value use cases that rely on the same data products – and therefore identify the most valuable data products.

Now this is where it gets interesting.

In Delight, our Business Lineage feature shows exactly that. It gives teams and their business stakeholders a shared view of which data products power which use cases and how those use cases contribute to business value to the organization.

It also means that dependencies can be tracked, synergies can be identified, and ensures that every initiative is strategically aligned with broader business objectives. Instead of prioritizing based on whoever shouts the loudest – or the classic HIPPO (highest paid person’s opinion) – teams can use actual data to guide their decisions. The most valuable initiatives can be prioritized based on the opportunity cost. Should we work on A or B? Delight makes it easier to answer that.

The difference is that McKinsey has mapped them left-to-right and we chose a top-to-bottom approach, but you get the point: visibility into value drives better decisions.

Challenge #2: No data product reusability and value-driven prioritization

Building similar data solutions over and over again drains valuable resources, frustrates teams, and slows down overall progress. And it happens more oftenthan you’d think. Many data teams reinvent solutions, leading to inflated build and maintenance costs, delayed timelines, and reduced efficiency – because they just aren’t aware ofwhat already exists.

Dataproducts should also be prioritized based on how reusable they are. The shift should move from building more to building fewer, but higher-quality, data products.

Also read: Less is More: Avoiding the Data Product Death Trap

McKinsey explains it well: organizations often fail to recognize the critical importance of reusability. They underestimate the significant incremental costs associated with building unique solutions from scratch, missing opportunities to rapidly scale value across multiple use cases.

With intentional reuse, organizations can achieve economies of scale, which lowers the additional costs associated with each new use case. The flywheel effect then comes into play. Value creation is accelerated and costs are reduced the more data products are reused across use cases.

"The value of a data product comes from the steady reduction in incremental costs achieved from reusing it and the acceleration in capturing the value of each additional use case." – McKinsey

In Delight, we’ve introduced the “Reusability Score”. Each data product is assigned a reusability score (as a percentage) based on which parts of a data product are already built and how much of them can be reused (with or without any modification) for a new use case.

A higher score generally indicates less new work is needed.

If you have three use cases with a similar estimated value, Delight helps you choose the one with the highest reusability score – because it requires the least additional investment to realize that value. For example, a use case with a 90% reusability score might only need to build the remaining 10% to unlock its value.

Teams can therefore prioritize efforts not only by potential business impact, but by how cost-effective and scalable the effort is, based on reuse potential.

Try our Cost Savings Calculator to uncover hidden cost savings in your data and AI portfolio.

Challenge #3: Insufficient and scattered tooling, and no AI to accelerate

Data teams are often stuck relying on outdated tools and manual processes, which hold them back from being effective, agile, and scalable with their limited resources.

They lose so much time navigating multiple platforms, manually handling repetitive tasks, and fighting with data quality issues. This kind of inefficiency really stops them from scaling their data products and realizing meaningful results.

As McKinsey points out, GenAI is dramatically accelerating the development of data products, yet many teams aren’t applying AI to their own workflows.

"Gen AI tools and capabilities are having a profound effect in data product development, accelerating the process by as much as three times over traditional methods." – McKinsey

But here’s the kicker: the people advocating for AI are the ones still running their own operations in PowerPoint and Excel.

McKinsey outlines practical areas where GenAI can boost efficiency, including identifying use cases, and defining data products.

We’re seeing it too: the future of data product development is augmented by AI. Delight is already integrating GenAI so data teams can scale their product portfolio without scaling complexity.

Referring to the above illustration by McKinsey, Delight tackles the “exploration” and part of the “preparation” stages. Its value lies in speeding up those early-stage, resource-heavy tasks with an embedded AI agent, such as generating templatized use cases, building business cases, identifying reusable data products, and proposing architectures based on existing demand and context.

This empowers data product managers (DPMs) to focus strategically, dramatically accelerating decision-making processes and speeding up repetitive tasks.

But it’s also important to note that Delight is designed to augment the work of DPMs (not eliminate it).

Today, a team might need 10 DPMs to manage 10 data products. But as the demand grows and the portfolio scales to 50 or more, those same 10 DPMs should be able to handle the load – without burning out or compromising on quality. That’s exactly where Delight helps: by streamlining workflows and embedding AI where it counts, teams can scale their impact without needing to scale headcount at the same pace. And in a world where skilled DPMs are scarce and hard to hire, that’s a real advantage.

‍ Realizing the promise of scale

The core message from McKinsey’s article is that the biggest obstacle to scaling data products isn’t technical – it’s strategic and operational. Misaligned incentives, lack of reuse, and poor visibility into value all conspire to make data efforts harder than they need to be.

But with the right structure, the right workflows, and the right tools, that complexity becomes manageable.

That’s where Delight comes in. We’ve taken those problems and built the solution. What McKinsey recommends in theory,we’ve built in practice.

Is your data team ready to streamline operations, work more efficiently, and scale with confidence?

The future of scaling data products doesn’t have to be aspirational.

It can be operational. Today.

Interested in seeing Delight in action, book a demo or start your 14-day trial today.