Business Data Analytics: Improving Decision Quality
Data analytics is more than producing reports. The right indicators, reliable data sources and analyses linked to management decisions help businesses spot problems early and use their resources more effectively.
To evaluate management practices using concrete evidence, identify improvement opportunities and develop actionable decisions. These recommendations are for general information and should be adapted to each business's sector and scale.
Before analysing: Define the management question
The first step is not choosing a tool, but defining the decision that needs to be made: Which products create value? Where do losses arise in the process? In which customer segments is churn rising? Measurement must address these specific questions.
Data quality and ownership
Data definitions across ERP, CRM, accounting and production systems should be aligned, while missing, duplicate and inconsistent records are addressed. Data ownership, update frequency and responsibility for quality controls should be defined.
Management indicators
Financial indicators (cash conversion and profitability), operational indicators (on-time delivery, scrap and capacity) and customer indicators (repeat purchasing and complaint resolution) should be assessed together. Having more indicators does not, on its own, mean better performance.
Stages of analytics maturity
Descriptive analytics examines what happened, diagnostic analytics why it happened, predictive analytics what may happen, and prescriptive analytics the potential impact of different options. Businesses should start at a level appropriate to their data infrastructure and decision risks.
An example management cycle
Define one critical decision; validate data definitions; measure baseline performance; analyse root causes; pilot a limited improvement; monitor the outcomes and scale the intervention accordingly.
Data analytics and corporate capacity assessment
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