Google pays publishers for AI answers
Benedikt Langer
4 min read Google's “AI contribution pilot” pays around 100 digital publishers for the extent to ...
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TL;DR
- 91% of executives say they make data-driven decisions – but only 29% link analytics to strategic actions.
- The problem: too many dashboards, too few actionable recommendations.
- Three archetypes of failure: the Dashboard Graveyard, Analysis Paralysis, and HiPPO Override.
- Decision Intelligence integrates data science, cognitive psychology, and decision theory.
- Successful data-driven companies stand out not for better tools – but for superior decision-making processes.
A DAX-listed company’s executive board receives a 60-page analytics report every Monday. When asked which three decisions that report has changed over the past six months, silence follows. The data is there. The dashboards are polished. Yet a wide gap remains between insight and action.
“Data-driven” is management’s most misused buzzword – not because data is missing, but because no one has defined the process that turns data into better decisions.
The Data-to-Decision Gap
NewVantage Partners: 91% of organizations are increasing their analytics investments – but only 29% report measurable influence on strategic decision-making.
Misplaced abstraction: C-suite executives need three actionable scenarios – with clear recommendations – not 40 charts.
Lack of causality: Dashboards display correlations, not the underlying reasons why something happened.
No institutionalized decision-making processes: Which decisions are made, when, and based on which data?
Three Archetypes of Failure
Dashboard Graveyard: Hundreds of dashboards that no one views. Solution: Radically consolidate to just ten core dashboards.
Analysis Paralysis: Every decision triggers yet another analysis. Solution: Introduce explicit decision gates – 80% confidence is sufficient.
HiPPO Override: The highest-paid person in the room overrides data-driven insights. Solution: Enforce transparency – anyone overriding data must document their rationale.
Decision Intelligence
Rather than asking “What do the data show?”, Decision Intelligence (DI) asks: “Which decision needs to be made – and what data do we need to make it?”
Decision Mapping: Prioritising analytics investments by identifying and ranking upcoming decisions.
Causal AI: Establishing cause-and-effect relationships – e.g., “Revenue is falling BECAUSE delivery times have increased” – not just correlations.
Decision Review: Systematic post-decision analysis, feeding insights back into future decision-making.
Five Steps
1. Decision Audit: Identify the ten most critical recurring decisions.
2. Data-Decision Mapping: Which data does each decision require?
3. Decision Dashboards: One dedicated dashboard per strategic decision – instead of generic BI tools.
4. Decision Cadence: Regular decision-making meetings with a defined format.
5. Decision Review: Quarterly assessment of decision quality.
Frequently Asked Questions
Do I need a data science team?
No – not to get started. Standard BI tools are sufficient for most strategic decisions. Data science becomes relevant for predictive analytics and machine learning.
Which BI tool should I choose?
Power BI for Microsoft-centric environments, Tableau for best-in-class visualisation, and Metabase as an open-source alternative.
How do I overcome the HiPPO effect?
Distribute data before meetings, solicit individual input on positions, document decisions that contradict the data – and evaluate those decisions against outcomes after six months.
Source of cover image: Unsplash / Stephen Dawson
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Translated from the German original using artificial intelligence. The German version is authoritative.