Mystery solved: The case for operationalizing analytics

Mystery solved: The case for operationalizing analytics

Operationalizing analytics saves valuable insights

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 story worth telling – with a notable protagonist to boot.

Here’s what we know: Numerous challenges – our suspects – lurk in the shadows, preventing the best analytical models and information from reaching decision makers. The most common culprits are bad data, poor governance, time-consuming manual processes, and lack of collaboration between data sciences and IT.

Operationalizing analytics saves valuable insights

Automated analytical models create transformative insights for credit card transactions, fraud risk, loan approvals, product quality control and anywhere data plays a role.

The steps from data to discovery to deployment are a continuous loop in the analytics life cycle, driven by teams of business analysts, data scientists and development operations experts.

With time-to-value always in mind, one goal is to intelligently automate and speed up processing wherever possible. By taking advantage of massively parallel compute power, you can run hundreds of models in the same time it used to take just one. You can also use automated machine learning and SAS® Model Manager to manage all of your organization’s models.

Automating repetitive steps saves critical time. If you can register and deploy analytical models with one click for instant value, you can avoid months of recoding for operational environments.

No villains, all value.

Participating in our mystery challenge?

If you're participating in our mystery challenge, the correct answer is Data de Vil, Father Time and Pat Freespirt. In this instance, they worked in cahoots to sabotage analytical insights. Peggy Powercord and Marty Maintenance were just in the wrong place at the wrong time, and their names should be forever cleared!

Enjoy the mystery? Help us spread the word on how others can get more smart models deployed -- learn more about operationalizing analytics.