Most organizations today are inundated with data. The challenge is no longer about the availability of data but rather what to do with it and how to prioritize ideas to convert them into tangible value. I’ve seen firsthand how this transformation is not just about technology but also about a strategic shift in mindset. Our customers often struggle with questions like, "Where do we start?" or "How can we make sure to create real value with Data and AI?" These questions highlight the need for a structured approach to turning data into actionable insights. I’d like to share some key insights from a recent interview.
The starting point: defining opportunities
The beginning of any data-driven initiative should fundamentally start with the definition of a specific business problem, not with the definition of a solution and then searching the right problem for it. An “opportunity” in the context of AI & Data Product Management is defined as “a specific business problem or desire that can be addressed through data or AI, without preconceived notions of the technological solution”. The core of defining an opportunity lies in its ability to solve a problem or fulfill a need, and therefore creating value for the business.
GenAI is a good example to apply opportunity thinking to. Many organizations these days are implementing technical prototypes for it. But some forget to start with the question of "What goal do I actually want to reach with this? Do I want to increase customer satisfaction with my customer support? Cut cost in my operational processes? Or provide new services?" We should always start by asking these questions before we jump right into the prototype development.
Opportunity Exploration is the initial phase in our Value Discovery process where these business problems, needs, or desires are identified and documented. Whether the goal is to enhance customer retention, streamline operations, or discover new revenue streams, the focus should remain on the desired business outcomes.
It’s important to engage the right participants in these discussions. Those people experiencing the problems or having the desires data can address should be included, i.e. the business departments. Data teams play a facilitative role, and often a Data Product Manager is appointed, helping to bridge the gap between the business needs and the technological capabilities.
Defining, assessing, and prioritizing opportunities
One effective way to kickstart this process is through a workshop that engages your chosen group of participants from across the organization. By asking each participant to identify their top three data opportunities and then collectively prioritizing these. They can be measured in various ways, such as monetary value, reduction in carbon footprint, or improvements in user or client satisfaction. The key is to rank these Opportunities based on their perceived value to the organization.
This approach not only surfaces the most valuable and feasible opportunities but also fosters a culture of collaboration and innovation, democratizing the decision-making process. It's essential to recognize the inherently subjective nature of this process but the goal is to infuse as much objectivity as possible by involving a diverse group of stakeholders in evaluating and prioritizing opportunities based on their potential value.
After ranking by value, the next consideration is feasibility. This involves a high-level assessment of how technically achievable each opportunity is. This step is crucial and should be done with input from technical experts, but it should remain a rough assessment at this stage. Ultimately, the goal is to identify one or two opportunities that offer significant value and are relatively easy to implement. These opportunities will serve as the starting point for more detailed exploration and development.
Remember that this isn't a one-time activity but a cyclical process that requires regular revisiting and reassessment. Ideally conducted on a quarterly basis, this iterative approach allows organizations to stay agile, adapting to changing business needs and technological advancements.
Real-world impact
While this “opportunity-first” approach may seem straightforward, it represents a significant shift in thinking for many organizations. Traditionally, business units approached their data teams with specific requests – “We want this model, this dataset, this dashboard" – without first defining the underlying business need or value. Our approach shifts this narrative for a clearer understanding of how to leverage data effectively.
Embracing a shift in mindset
The key takeaway from our exploration of opportunities is the necessity of a paradigm shift in how organizations approach data and technology. The focus should always start with the business opportunity, not the technology. This approach ensures that data-driven initiatives are firmly anchored in real business needs, thereby maximizing the chances of delivering tangible value.
As we continue to navigate the opportunity maze, let's remember that the journey is as much about the mindset as it is about methodology. By placing business needs at the heart of our data strategies, we pave the way for truly transformative outcomes.
Download our Step-by-Step Guide Along the AI & Data Product Management Loop for more info on discovering, implementing and adopting opportunities for data & AI or reach out to us directly.
Watch the full interview:
