Tag: operationalizing analytics
Most model assessment metrics, such as Lift, AUC, KS, ASE, require the presence of the target/label to be in the data. This is always the case at the time of model training. But how can I ensure that the developed model can be applied to new data for prediction?
Everyone is talking about artificial intelligence (AI) and how it affects our lives -- there are even AI toothbrushes! But how do businesses use AI to help them compete in the market? According to Gartner research, only half of all AI projects are deployed and 90% take more than three
A business glossary improves data quality – one of the top five ways it makes analytics better.
Getting value from analytics is becoming top of mind for businesses. Organizations have invested millions of dollars in data, people and technology and are looking for a return on their investment. That requires operationalizing analytics so that it can be used for strategic decision making -- often referred to as
The final phase in analytical model deployment is the perfect unsolved mystery. Why are 50% of analytics models never deployed? And why does it take three months or more to complete 90% of deployed models? What happened or didn’t happen to allow analytical insights to reach their potential? It’s a
How do you deploy your model so that business processes can make use of it? This post explores how SAS Viya applications can directly add models to a model repository, and specifically focuses on how to deploy them with SAS Model Manager to Hadoop.
This is the fourth post in my series of 10 machine learning best practices. It’s common to build models on historical training data and then apply the model to new data to make decisions. This process is called model deployment or scoring. I often hear data scientists say, “It took
The increasing use of predictive analytics in mission-critical business decisions and operations brings new challenges to the forefront for many of our customers. Throughout the last year I spoke to many customers about their use of predictive analytics and where they see areas of improvement to achieve even more success
In part 1 of my thoughts about analytics maturity, I deferred talking about issues related to the actual assessment of your organization’s level. Today I intend to detail some of the ways my peers and I are thinking about analytical maturity, comment on scales in use today, and address some