The Evolution Journey of Data Products
Field Notes - From Lab to Factory Through the BLAC Lens
I stumbled upon two fascinating articles by Michael Skok from the early 2010s about startup value creation. His BLAC (Blatant, Latent, Aspirational, Critical) framework, originally designed for evaluating startup opportunities, sparked an intriguing thought: Could this lens help us better understand data product evolution? Building on my previous explorations of measuring outcomes over outputs and the twin engines of lab and factory patterns, this framework offers fresh insights into how data products naturally evolve within organizations. When combined with modern thinking around platform architecture, self-service infrastructure, and computational governance, it reveals compelling patterns in data product lifecycles that I hadn't previously considered
Understanding the Evolution Stages
The journey of a data product isn't linear - it's a maturation process that moves through distinct stages, each requiring different approaches to management, measurement, and value creation.
This evolution journey, even in its earliest stages, is profoundly influenced by an organization's platform capabilities. Modern infrastructure and governance don't just support later-stage products - they create the foundation for safe experimentation and rapid learning from the very beginning, allowing teams to focus on discovering value rather than managing technical overhead.
The Lab (Aspirational Stage)
In this initial stage, data products exist primarily as possibilities. Teams explore how to combine and transform data in ways that could create business value. They might experiment with connecting different data sources, testing analytical approaches, or exploring new ways to present insights. Like any lab experiment, many initiatives here will fail - and that's exactly as it should be. The key is to fail fast and learn continuously about how data can serve business needs.
What's interesting about this stage is that traditional data metrics like quality scores or system performance matter less than learning metrics:
What business insights are we gaining from the data?
Which assumptions about data value are we validating or invalidating?
How is our understanding of the business problem evolving through data exploration?
Finding Product-Market Fit (Latent Stage)
As concepts prove viable, data products enter a stage where their value becomes increasingly apparent but isn't yet fully recognized across the organization. This is where the real work of refining the data product happens - ensuring it delivers consistent, reliable insights or capabilities that solve specific business problems.
This stage requires a shift in focus from pure experimentation to value validation. We start measuring not just data quality but actual business impact:
How are early adopters using the data product to make decisions?
What unexpected use cases are emerging for the data?
Which business problems are being solved in ways that weren't possible before?
Scaling in the Factory (Blatant Stage)
When a data product's value becomes clear and demand grows, it enters the factory stage. Here, the focus shifts to scalability, reliability, and consistent delivery of data-driven value. The product's impact becomes increasingly visible and measurable in concrete business terms.
This is where the measurement framework becomes crucial. We're no longer just tracking data usage metrics but measuring concrete business outcomes:
How much faster are decisions being made?
What costs are being reduced through data-driven automation?
How are customer experiences improving through better data utilization?
Core Business Asset (Critical Stage)
The final evolution stage is when a data product becomes integral to business operations. It's no longer just a source of insights but a crucial component of how the organization functions. The measurements here focus on business dependency and value preservation:
How would operations be impacted if this data product wasn't available?
What competitive advantages does it enable through unique data capabilities?
Building Teams for Evolution
The journey through these stages demands more than just the right infrastructure - it requires the right people with the right combination of capabilities. Successful data product teams exhibit three core capabilities:
They bring the attitude necessary for experimentation and learning, especially crucial in early stages.
They possess the aptitude to adapt as products evolve and mature.
They demonstrate the ability to execute at both technical and business levels.
Beyond these foundational traits, the most effective teams also display three advanced characteristics.
They remain deeply aware of both technical possibilities and business realities.
They maintain authenticity in their interactions across organizational boundaries.
Perhaps most importantly, they show the mental agility of athletes, able to pivot between experimental and operational modes as needed.
These characteristics flourish especially when teams are supported by infrastructure that lets them focus on value creation rather than technical complexity.
Evaluating Evolution Opportunities
When considering how to evolve data products through these stages, organizations must evaluate opportunities through multiple lenses. First, they must understand whether the problem being solved is truly unworkable within current systems, unavoidable due to regulatory or business requirements, urgent enough to command attention, and underserved by existing solutions.
Success also requires breakthrough value creation. This means delivering discontinuous innovation that transforms how work is done, building defensible advantages that create lasting value, and enabling disruptive business models that change how value is captured.
Most critically, data products must deliver significantly more value than the effort required to adopt them. This "gain/pain ratio" becomes increasingly important as products move toward critical status. Products that can't achieve at least a tenfold improvement in value compared to adoption effort often remain stuck in earlier stages.
Natural Rhythms in the Evolution Journey
The progression through these stages isn't just about maturity - it's about finding the natural rhythm that matches each domain's value creation pattern. Different domains naturally gravitate toward different operational patterns, directly influencing how quickly and in what way their data products move through these stages.
Some domains, focused on rapid innovation, might cycle quickly through Aspirational and Latent stages before either graduating to Critical or being retired. Others, focused on core business operations, might move more deliberately but with higher likelihood of reaching the Critical stage. The key is recognizing and respecting these natural rhythms rather than forcing all products through the same development pattern.
Infrastructure and Support Evolution
As data products mature through these stages, their infrastructure and support needs evolve naturally. Rather than requiring completely different environments for each stage, modern approaches enable teams to work within a consistent but adaptive framework that grows with their products' maturity. This creates a more seamless evolution from early experimentation through to critical operation.
The Platform Advantage
Organizations that implement domain-oriented platforms with computational governance and self-service infrastructure can significantly smooth this evolution journey. Instead of managing separate tools and processes for each stage, a unified platform approach offers several advantages:
Consistent Governance: Automated guardrails flex naturally with each product's stage, enabling safe experimentation while ensuring appropriate controls
Seamless Transitions: Teams can move between lab and factory patterns without switching platforms or rebuilding infrastructure
Accelerated Evolution: Self-service capabilities mean teams can focus on value creation rather than infrastructure management
Domain Alignment: Each domain can move at its natural pace while maintaining platform compatibility
This modern platform approach doesn't eliminate the need for different support models at different stages, but it does make the transitions more natural and less disruptive. Teams can focus on creating value while the platform handles the complexities of governance and infrastructure scaling.
Evolution, Transformation, and Sunset
The journey through BLAC quadrants isn't always forward-moving. Just as data products evolve toward Critical status, they can also transform or sunset through reverse movement through the quadrants. Understanding these patterns is crucial for effective portfolio management.
When data products begin their sunset journey, they often move backwards through the quadrants - from Critical to Blatant, then to Latent, and finally to Aspirational. But unlike their forward evolution, this reverse journey focuses on carefully managing decreasing dependencies and ensuring smooth transitions for affected business processes.
This natural evolution cycle creates space for new Aspirational products to emerge. Resources freed from sunsetting products can fuel new innovations, creating a healthy renewal cycle within the data product portfolio.
Looking Forward
As organizations continue to mature their data product capabilities, understanding this evolutionary journey becomes crucial. It helps us avoid common traps:
applying factory-style management to lab experiments,
treating critical business assets as experimental projects,
or maintaining products past their natural lifecycle.
The key lies in recognizing that this isn't just about maturity - it's about matching the evolution pattern to each domain's natural value creation cycle. Modern platforms with embedded governance enable this fluid evolution, allowing teams to focus on value creation while maintaining appropriate controls at each stage.
Success requires more than just understanding these patterns - it demands building teams with the right combination of capabilities, evaluating opportunities through multiple lenses, and creating infrastructure that supports natural evolution. Organizations that master this balance can create sustainable value through their data products, whether those products are in early exploration or serving as critical business assets.
What's your experience with data product evolution? Have you seen these patterns in your organization, or does your journey look different? How do you balance the need for experimentation with the demands of running critical data products?