Agile Analytics

Analytics is a method of logical analysis. It’s a method where a whole is divided into component parts.

On a macro level, component parts of the business whole include resources consumed, activities performed, products and services provided, customers and markets served, supplier networks, and the competitive environment.

For most of us in the performance management community, the analytic applications we’re most interested in focus on profitability, process, and performance.

Business examples include product and service cost, profitability analysis (products, services, customers. brands, and channels), operational modeling, process improvement, and performance measurement.

For those in larger companies many of the analytic applications we need are met with large scale Enterprise Analytics.

These Enterprise-class analytic luxury liners from SAS, SAP, Oracle, and others, are large scale, repeatable, IT intensive, integrated, and transaction oriented.

Agile Analytics is more of a speed boat, often project based with attributes like fast (speed to results), flexible (applies to many different situations), simple and on point, relevant, operations focused, actionable (results lead to change) and adaptable to a changing environment.

Together Enterprise and Agile are the Yin and Yang solutions to business analytics.

It would not be unusual to use Agile Analytics as a bridge to Enterprise Analytics, a proof of concept before large commitments are made. Nor would it be unusual for an agile solution to draw data and information from Enterprise Analytic systems and data depositories.

Agile analytics rapidly and efficiently answers the questions that Enterprise Analytics either cannot answer, or that take a significant time and resource to answer.

Best practice for Agile Analytics starts with short and aggressive timelines, expecting answers not progress reports. 2-6 months, never more than a year.

Another best practice is clarity and singleness of purpose. Short aggressive time lines require clear definition of the problem to be solved and/or specific questions to get answered.

Best practice also includes simplicity and maximizing the amount of work NOT done.

When the objective is accurate but not precise, reasonable but not absolute, good but not perfect, a lot of unnecessary work can be avoided.

Always consider Yin and Yang solutions to your business issues…

John A. Miller

June 2010

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