Planning for uncertainty with simulation and optimization
How companies can move beyond point forecasts by combining scenarios, predictive models and optimization to improve decisions under volatile conditions.
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Analytics, data and decision systems
Turn data, analytics and decision systems into clearer insight, stronger choices and more effective execution.
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Articles
Data as a reusable enterprise product
How stronger data foundations, governance and product thinking can turn fragmented information into a scalable source of decision advantage.
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Is your data ready for the ambition placed on it?
Readiness depends on whether critical information can support the actual decisions, analytics and AI workloads the enterprise intends to pursue.
Where does your data pipeline actually break?
Reliability depends on the complete path from source to consumption, including transformations and dependencies hidden between systems.
Strategic challenges
Data complexity compounds quietly
Every new source, transformation and point-to-point integration can increase dependencies faster than the architecture can absorb them.
Analytics demand can overwhelm analytics strategy
A growing backlog of requests can turn specialist teams into internal service desks without clear priorities or differentiated business impact.
POV
Alternative data should have to earn its place
If an external signal does not materially improve understanding or prediction, its novelty is irrelevant and its complexity is a cost.
An analytics team should not own every analytical decision
Business leaders must retain responsibility for decisions; analytics should strengthen the evidence and capability surrounding them.
Strategic impact
Drivers make performance more actionable
Connecting outcomes to the factors behind them allows managers to understand where intervention can influence future results.
Strategy makes data investment more selective
Connecting foundations to future workloads helps distinguish critical transformation from modernisation that offers little strategic value.
What we observe
Novel data is often mistaken for useful data
We frequently see unusual datasets valued for originality before anyone tests whether they improve explanation, prediction or decisions.
Renaming a dataset does not make it a product
We frequently see existing tables relabelled as data products without defined users, service expectations, ownership or lifecycle management.