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.
Read articleRelated macro
Analytics, data and decision systems
Turn data, analytics and decision systems into clearer insight, stronger choices and more effective execution.
Discover the macroEditorial overview
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.
Read articleFocus
Who is the customer for your data?
A data product becomes meaningful when its consumers, recurring needs and expected outcomes are clearer than the technology used to deliver it.
Does the model understand how your industry works?
Analytical performance depends on representing the economics, constraints and relationships that actually determine outcomes within the sector.
Strategic challenges
Enterprises produce data faster than they create information value
Large data estates can continue expanding while important users still recreate datasets and struggle to find reliable information.
Complex decisions rarely have one objective
Leaders often optimise several competing outcomes simultaneously, making trade-offs unavoidable even when the underlying analysis is strong.
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.
Sector expertise should change the model, not decorate the presentation
If industry knowledge does not alter variables, assumptions or interpretation, the analysis is still generic.
Strategic impact
Drivers make performance more actionable
Connecting outcomes to the factors behind them allows managers to understand where intervention can influence future results.
Product thinking changes the unit of data investment
Reusable information assets can concentrate ownership and engineering around needs shared across multiple consumers and applications.
What we observe
Complex models often answer simple questions badly
We frequently see analytical sophistication increase while the business question, assumptions and intended decision remain poorly defined.
Centralisation is often mistaken for maturity
We frequently see analytics consolidated into one function even when decision ownership and domain knowledge remain distributed across the business.