The problem with current dashboards
73% of executives surveyed by McKinsey in 2024 say their reporting tools do not help them make better decisions. They have access to more data than ever, but most of it is retrospective, decontextualised and arrives late. A dashboard that tells you what happened last week is not business intelligence — it is a diary with visualisations.
The underlying problem is that most BI implementations start from the capabilities of the tool, not from the questions the business needs to answer. They build what is technically possible, not what is strategically necessary. The result is control panels with 40 metrics where nobody knows which one really matters.
A useful executive dashboard has no more than 5-7 main metrics, each with context (trend, benchmark, target), and must allow the user to move from 'what is happening?' to 'why is it happening?' in fewer than three clicks. That is not a tool problem — it is a design problem.
Actionable KPIs: the difference between measuring and managing
A KPI is actionable when it meets three conditions: someone in the organisation has the ability to influence it, there is a clear threshold that triggers a specific action, and the update frequency is consistent with the speed at which action can be taken. If your customer satisfaction KPI is updated monthly but you can intervene in real time, the indicator is designed to report, not to manage.
In retail projects, we have seen how gross margin by category as a level-1 KPI leads to correct pricing decisions. But when that same KPI is mixed with 30 operations metrics on the same dashboard, product managers stop paying attention because the visual noise exceeds the signal. Information architecture matters as much as the data.
KPI selection must start by mapping the value levers of the business model, not by inventorying what the ERP can export. For a professional services operator, the levers are utilisation, margin per project and renewal rate. Everything else is secondary context. Start from strategy and work down to data, not the other way round.
Data as a strategic asset: beyond the data lake
Many organisations have invested millions in data lakes that are, in practice, data swamps: repositories where data arrives but from which no structured value emerges. The problem is not storage — it is governance, quality and, above all, the lack of a semantic model that makes data from different systems speak the same language.
Treating data as a strategic asset means understanding its complete lifecycle: capture, quality, lineage, access and deprecation. It also means assigning real ownership — not just technical but business — to each data domain. The data mesh concept, with its decentralised domains and data products with explicit SLAs, is the most coherent evolution of this idea for organisations of a certain size.
The most reliable indicator of data maturity is not the size of the data warehouse — it is how many relevant business decisions were made last week based on own data. If the honest answer is 'few or none', the problem is not technical. It is cultural and organisational, and requires a different intervention.
From data to competitive advantage: when insights change the game
The real competitive advantage of data does not come from having the same dashboards as the market — it comes from detecting patterns that competitors do not see because they do not have the right data or do not know what to look for. A retailer that correlates local weather data with demand patterns by store can optimise stock in ways that competitors, working only with sales history, cannot replicate.
The highest-impact use cases we have implemented share one characteristic: they combine data sources that normally live in different silos. Churn prediction incorporating support interaction data has 34% greater accuracy than one working solely with transactional data. Profitability analysis per customer that includes the real cost of service (not just gross margin) completely changes the sales strategy.
The question that should guide any data initiative is not 'what can we measure?' but 'what decision do we want to improve and how much is that improvement worth to the business?' With that orientation, projects have clear ROI from the design stage and avoid the most common mistake: building analytical capability without a concrete problem to solve.
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