Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: The evolution of BRMS (part 2)
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > The evolution of BRMS (part 2)
Predictive Analytics

The evolution of BRMS (part 2)

Editor SDC
Editor SDC
4 Min Read
SHARE

— Posted by Carole-Ann Thank you for attending the Evolution of BRMS session at Business Rules Forum. It was great meeting some of you there in person. For those of you that could not make it, I wanted to give…

— Posted by Carole-Ann

Img_1913

More Read

Oracle buys Haley
Conditional probability: an easier way
Customer Behavior Analysis in the Telecom Arena
Social Media Data and what analysts can do with it
Automated Valuation Models

Thank you for attending the Evolution of BRMS session at Business Rules Forum.  It was great meeting some of you there in person.

For those of you that could not make it, I wanted to give you the

gist of what I presented.  A previous presentation covered the evolution of

the business rules technology focusing first on the drivers that forced

the market to shift its focus from Business Rules Engines (BRE) to

Business Rules Management Systems (BRMS).  In this second presentation, I explore the evolution that

is taking place as we speak, going from BRMS to Decision Management

(DM). 

In a nutshell, the main

ideas are summarized below. 

Increase Confidence in Strategy Performance

  • Once business rules are verified and validated, they are typically promoted to Production at the right time — How do you check today that those business rules will allow you to achieve your business goals?  How long does it take to realize whether or not the rate of automatic decisions is acceptable?  
  • The next step in decision improvement is to better compare champion / challenger strategies — How do you accurately predict the relative business value of each strategy?  How would external elements such as interest rate change or cost of resources influence the ability to repay or likelihood to accept an offer?
  • The last step (in this scenario; another methodology is to start actually here) would be to generate an optimized strategy out of this decision model and historical data — How does your current strategy map compared to an optimal assignment?  Can you infer an optimal strategy that can be applied consistently to your incoming transactions?

Address more Sophisticated Decisions

  • Business Rules come typically from experts, regulations and/or legacy code — Are they as precise / efficient as those of your competitors?  Would statistics on your historical data help you better identify the good versus bad risk applicants?  What if you could predict which customers are likely to accept which offers?  How competitive would you be if you could accurately price each transaction according to your estimated related expenses?
  • Some decisions may appear sub-optimal and could be improved — Do you offer your customers the best possible deal given your product constraints and your business objectives?  How efficient is your usage of resources given your delivery schedules?

Connect Decisions

  • Decisions made in silos may lead to contradictions, overlaps, inefficiencies — Do you proactively market to customers that have a history of Fraud or Delinquency?  How valuable are the customers you try to retain?

The slide deck is available from the Business Rules Forum website if you are an attendee.  Our marketing guys will also post it on our community site for your convenience.


Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

business recovering from data loss
How Data-Driven Businesses Protect MySQL Databases from Shutdown
Big Data Exclusive
ai driven task management
Reducing “Work About Work” with AI Task Managers
Artificial Intelligence Exclusive
data center uptime
Why Rodent-Resistant Conduits Are Critical for Data Center Uptime
Big Data Data Management Exclusive Risk Management
big data and AI
The Intersection of Big Data and AI in Project Management
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Another Moneyball quote

1 Min Read

Guest Post: Inference for R

5 Min Read

Advocate of Analytics – Economist Paul A. Samuelson (1915-2009)

4 Min Read

Determining Perception Gap Through Twitter [INFOGRAPHIC]

1 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?