Anne Milley from SAS, one of the sponsors of the show, spoke on the art and science of better. Data is often messy and the enterprise is not a lab. Nevertheless, she says, we can still bring science to bear. We can observe, define, measure, experiment, learn and ACT. Anne had a number of observations:

• We must begin with observation. Semmelweis' study on hand washing in 1847 observed that hand washing saved lives but without the understanding of these observations nothing could be done.
  
• Defining the right problem is essential. For instance, in CRM the results are very different if time is considered (e.g. with survival methods) than if it is not.
  
• While there is a cost of running experiments to see what you can learn, there is a cost of ignorance too. Collecting more data through experiments may cost money but not knowing can be much more expensive.
  
• Creating a culture of experimentation and continuous learning is both essential and difficult.
  
• There is an essential step of acting on the modelling or analysis. As Deming said "The object of taking data is to provide a basis for action". This often requires more interpretation and discussion than might be expected and tools like visualization can really help explain what a model is saying, thus increasing the likelihood of action.
• Challenging business as usual is a great way to use analytics and this can be supported by developing an Analytic Center of Excellence (though I would say a Decision Center of Excellence would be better) to see what is being done best across the company, close the loop and drive new behavior elsewhere.
  

More posts and a white paper on predictive analytics and decision management at decisionmanagementsolutions.com/paw