TL;DR
- More data does not equal better decisions; it often just creates a dashboard graveyard where trust goes to die, and analysts are reduced to weary report-builders.
- Insights become rich when data is laced with human experience and front-line sentiment, requiring analysts to feel the space they are measuring.
- Decision makers are not a monolith, owning the outcomes of analysis means delivering prescriptive nudges for some and raw depth for others, moving teams from linking to reports to owning the outcome.
The Dashboard Trap
The message is mixed on both sides of the fence. Organizations hire analytics leaders to produce actionable insights, only to receive a flood of “self-service” reports. Conversely, analytics leaders push for real insight but are bogged down by an overpowering demand for more ways to slice and dice information.
We want predictive power, but we can’t articulate how it will drive action. We want time to build deep insights, but we rarely show the business what that value looks like.
In our Decision Ridge work, we’ve found that the myth of more is an enemy of clarity. When we flood decision makers with data without understanding their cognitive capacity, we don’t empower them, we erode their trust. We turn great thinkers into report-checkers and great analysts into order. And our organizations, teams, and customers are worse off for it.
Navigating the Path of Decision Makers
Analytics professionals should own their outcomes, the value they create. And, while decision makers can help lead great analysis we will focus on the power that great analytics teams can gain across a few opportunities.
1. The Dance of Customization
Analytics teams often build for a general user. This can be due to limited resources, lack of vision or influences by the loudest voices. The reality, like so much of the world is less clear as a full spectrum of personas exists.
- The Enthusiasts: They need only a nudge, a prescriptive, pointed insight delivered to their pocket via “random acts of email.” They already understand the motivation and are ready to act.
- The Deep-Divers: They need to feel the raw texture of the data to gain confidence. They want to guide their own direction.
- The Missing Middle: The 50% who fluctuate based on their confidence in the team and the stakes of the decision.
When intelligence teams adopt a Product mindset, they realize that a front-line manager in a call center has a different perspective of time and needs than a CFO. One needs to win the hour; the other needs to win the decade. They realize that building trust through co-creation with stakeholders is the greatest way to build value beyond the dashboards.
2. Always Be Selling
The most effective tools are those used [well] consistently. Yet too often the analytics team stops engaging and lobs the results over the fence to the requestors. (And then we complain that we have become order takers…).
Analytics teams and their development dollars focus on AI, data structures, coding, visualization and any number of other tools are available. But, what about the human side of the work of analytics?
- How much time are you spending in the education, consulting, and (yes) sales side of your role?
- Are you measured on how many requests you filled or how well and often your results are used?
- And, maybe more importantly, what decisions would you make differently if those incentives were aligned?
Taking a product sales approach to your analytics can help. This perspective leads us to avoid the fracturing and then cannibalization of users across multiple dashboards and technologies. An experience where a dashboard (likely following a different link they have to set in their favorites) for finance and another for HR, and likely a few different links even within each area where customer sentiment and sales are in different locations all leading to increased frustration and lack of value in the product. Selling this setup to anyone seems completely illogical, yet this is what we feed our internal decision makers.
3. Anti-Bias Structures
Lies, damned lies, and statistics. From leading questions to tortured data and poorly drawn conclusions there are more ways to use data to defend an existing premise (confirmation bias) than there are to get real information. When intelligence teams or individuals are not properly supported, measured and rewarded for unbiased truthful results then it is a quick fall to data as a wasted effort.
Assuming the analyst in the room full of executives will naturally stand their ground goes against our very nature. This is where the Behavioral View is vital. If we don’t co-create insights in a high-trust environment, we miss the anti-patterns, the uncomfortable truths that actually hold the key to the next breakthrough view. Empowerment is about giving analysts the cultural cover to be the owner of the truth, even when it’s unpopular.
Borrowing from The Octopus Organization, we see the ideal data structure as one where the tentacles (decentralized analysts) are embedded deeply with customers, while still being connected to the central brain (the center of excellence) to ensure mathematical and ethical standards.
Stronger Decisions Matter
Organizations need accurate, timely, and relevant insights from the boardroom to the front-line to improve the millions of decisions made every day. When teams responsible for the feeding of great intelligence take an outside-in product view their approaches shift. They start with their decision makers and their unique personas to build more integrated insights that move with their customers as they already work, building habits on well-worn paths and habits.
My Perspective
In most cases, none of this fracturing is done with ill intention, quite the opposite. Analysts say “yes” out of a desire to help and create value; decision makers use leading questions about their projects not to create false support but because they fully believe in the efforts.
I’ve sat in both seats, the leader begging for a clear answer and the architect trying to build a centralized truth. The reality is, the best report is the one that gets used. Period.
Success is less about structure then culture and happens when data people learn to sense the space of their customers. Sometimes that means having the resilience and support to say no to more data and instead focusing on the three supporting measures that actually drive the outcome. As intelligence teams build trust, they must move from being librarians of reports to owners of prescriptive action.
About the research
This article navigates the Decision Ridge and the Path of Decision Maker Personas, applying the Behavioral and Systemic Views of the Ridgeline Synthesis. It was influenced by The Octopus Organization (Le-Brun and Werner) regarding the balance of decentralized intelligence, The Ridgeline Research Graph synthesis of decision-making capacity and cognitive load and Product Management Principles as applied to internal data delivery.
