Decision Management in the Cloud: Learning from our pal HAL
Timothy Powers 270003F3FN email@example.com | | Tags:  predictive-analytics business-rules-management spss business-rules business-analytics decision-management analytics
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Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Spoiler alert: If you have never seen “2001: A Space Odyssey” forgive me for spoiling the plot, but you’ve had 44 years to see the movie.
In 2001, a space crew was voyaging to Jupiter along with HAL 9000, the spaceship’s computer that was foolproof and incapable making poor decisions. During the journey, HAL began to malfunction, slowly go mad, and refusing to cooperate, turned on his crewmen and methodically “eliminated” them one-by-one.
Ultimately, HAL had to be powered down – against his own will – to keep him from making any further decisions on his own.
And what are those strengths?
Taking information from everywhere (and I do mean everywhere – transactional, social media, call center notes, video, sensors, etc.) with an end goal of providing recommendations for action, such as identifying claims fraud, reducing churn or reducing costs via preventative maintenance.
More specifically, organizations can now employ these systems in the Cloud using all of its proprietary “local” data along with cloud-resident data to tie strategy to execution by means of decision management systems.
By combining predictive models, rules, scoring and optimization techniques to generate recommended actions, decision management systems allow users to automatically deliver high-volume, optimized decisions at the point of impact, such as in a call center, on a website, in a store, etc.
As an additional option for customers, IBM recently launched IBM SPSS Decision Management Software as a Service – one such system that helps organizations make these decisions in the cloud without the administrative overhead and expense of on-site software.
Decision management is an ideal solution for organizations in a range of industries, especially those with high volumes of interactions – such as in retail, banking and financial services, and insurance, as well as government agencies and academic organizations.
For example, some decisions and recommendations will be heavily dependent on rules (e.g., “do not make offer A to customer B…”), while others will be based on predictive analytics (e.g., “… unless the propensity to churn is greater than 90 percent...”). Some decisions and recommendations will be based on internal data (e.g., past purchase patterns and RFM analysis), and others on external sources (e.g., credit score and tweeter feed).
The main point, however, is that all are tied to generating a specific outcome, whether a tactical or strategic decision. And, even before deploying these recommendations into an operational system, multiple simulations and “what if” scenarios can be run to compare the best outcomes.
Let’s get the recommendation right first so the same bad decisions aren’t made over and over again.
If HAL taught us anything it’s that the outcome is king. It’s time to start deploying analytics into operational systems before customers start being methodically eliminated…one-by-one.
Open the pod bay doors HAL…
For more information:
· Watch a demo of IBM SPSS Decision Management
· Read a previous blog post on the “Seven Steps of Decision Management”
· Watch a video of industry analyst James Taylor discussing the benefits of Decision Management