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
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Decision Management Loops Back to Decision Support Systems
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > Decision Management > Decision Management Loops Back to Decision Support Systems
Decision Management

Decision Management Loops Back to Decision Support Systems

CMatignon
CMatignon
6 Min Read
SHARE

Manual DecisionsJames explained the difference between Decision Managemen

Manual DecisionsJames explained the difference between Decision Management and Decision Support Systems in a recent post.  Although it was a solid picture of manual versus automated decisions, I think it is missing half the story…  Let me elaborate.

Current state

In summary, James makes the point that repeatable decisions, that cannot afford human intervention, should be handled by a Decision Management System.  There is no argument there.  It was the very reason that those systems were created in the first place.

The issue I see in such an abrupt dichotomy between automated and human decisions is that, as long as we keep doing it this way, the 2 systems will not leverage each other.  At best, automated decision services document the identified red flags or opportunities identified by the business rules before deciding to refer the decision to an expert.  From that point on, the manual decision ignores the body of knowledge accumulated in the form of decision logic, and turns to other sources like wikis or simply expertise.  There is hardly any investment happening today in looping back these manual decisions into the repository of decision logic.

Why they should eventually merge

If, alternatively, we considered the world of decisions as a single “decision support system” in the literal sense, we would be able to better connect the dots.

  1. From Automated to Manual: If the case worker could see more than stored rule execution results, and explore the business rules that triggered those flags “How high risk this high-risk applicant?”.  Rules execute or they don’t.  Seeing that the application was borderline may influence further the case worker, and help him or her make the best decision.  Furthermore, in isolation, a manual decision for one customer or one transaction can seem harmless, but assessing how it would impact the business if operationalized can yet again help guide the manual decision.  Business Intelligence tools provide some of this insight of course, when they are well-integrated.  The only provide exploration tools on the distribution of your portfolio though.  It would be much harder to assess how many dollars would be involved if you granted a discount to all applicants with such and such characteristics.  This is where Decision Management shines, allowing the operator to actually run decision logic and simulate the results, including any post-processing calculations or predictions.  In the hands of this expert making manual decisions, it would him/her further.
  2. From Manual to Automated: Once the decision has been made, the case worker moves on to the next decision, the next case.  So what happens to the intellectual effort that was put together for this manual decision?  Gone…  To be fair, it will linger for a little while in the head of that case worker for a while.  Unless that person is extremely diligent and documents every case carefully and then shares them with colleagues, it is unlikely that this knowledge will be propagate beyond that one person.  Manual decisions are delicate decisions, often applied to the high-risk / high-value cases.  That decisioning logic, although it may only be applicable to one exception, should be captured though in ‘decision logic’ format (complete or incomplete) such that It can become a candidate for automation down the road.  Peers would be able to benefit from the insight that “according to how we have been processing similar applications recently, the informal decision logic recommends to approve/decline”.  Management would be able to review how each case worker or how the group has been manually making decisions to operationalize those that seem the most repeatable and the most effective (meaning more revenue, less risk, better customer satisfaction, less complaints or any other set of metrics that are important to your business).

The main point I want to make it that decisions span manual and automated processing, but it is not an either-or situation, there are shades of grey, and an integrated approach to both can deliver huge benefits.

That reminds me of the discussion on Business Rules versus Business Intelligence, the rear view mirror story.  The key benefit is in combining both for better decisions.  Having a dashboard readily available when you are eliciting business rules gives you a tremendous advantage, compared to most organizations that can produce the equivalent report in a matter of weeks.  The more insight, the more effective.

 

 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Business Analytics Error: Learn from Uber’s Mistake During the Sydney Terror Attack

2 Min Read

Black Swan Alert: Low Tech Links Devastate High Tech Supply Chains

4 Min Read

Test, Learn, Adapt: Using Analytics to Improve Public Policy

3 Min Read

The Unintended Consequences of Combining Technology and Speed

4 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.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?