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
    predictive analytics risk management
    How Predictive Analytics Is Redefining Risk Management Across Industries
    7 Min Read
    data analytics and gold trading
    Data Analytics and the New Era of Gold Trading
    9 Min Read
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Decision Management and software development II – Model Driven Engineering
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 > CRM > Decision Management and software development II – Model Driven Engineering
Business IntelligenceCRMData MiningPredictive Analytics

Decision Management and software development II – Model Driven Engineering

JamesTaylor
JamesTaylor
5 Min Read
SHARE

Continuing this weeks posts on using decision management to improve development,  I thought I would post on how decision management should be part of model-driven development (model-driven engineering, a model-driven architecture or whatever).

The recent, and premature, discussion of the death of SOA led Johan den Haan to post SOA is dead; long live Model-Driven SOA in which he said:

That’s why we should talk more about the problem domain. We have to capture today’s business with formal models.

I have posted before about using decision management with MDE and answered some questions from a reader on MDE but I thought his comment was particularly pertinent to the issue of using business rules and decision management in MDE.

The key challenge, as he notes, is the problem domain and capturing today’s business. I would go further and say that we want to make capturing the problem domain as close to the solution domain as we can (so that the business users who understand what they need to do can make their software actually do it) and that we need not only…

More Read

Looking upstream for warranty cost savings
Are Traders at Risk From being Overtaken by AI?
Smart’ fridges that run on renewable electricity and are capable…
Irony and WordPress.com advertising
How to Begin Analyzing Social Media


Copyright © 2009 James Taylor. Visit the original article at Decision Management and software development II – Model Driven Engineering.

Continuing this weeks posts on using decision management to improve development,  I thought I would post on how decision management should be part of model-driven development (model-driven engineering, a model-driven architecture or whatever).

The recent, and premature, discussion of the death of SOA led Johan den Haan to post SOA is dead; long live Model-Driven SOA in which he said:

That’s why we should talk more about the problem domain. We have to capture today’s business with formal models.

I have posted before about using decision management with MDE and answered some questions from a reader on MDE but I thought his comment was particularly pertinent to the issue of using business rules and decision management in MDE.

The key challenge, as he notes, is the problem domain and capturing today’s business. I would go further and say that we want to make capturing the problem domain as close to the solution domain as we can (so that the business users who understand what they need to do can make their software actually do it) and that we need not only to handle today’s business but create an environment in which we can easily capture tomorrow’s also. We must handle change. In an era where most of the cost and time spent on a system will be spent in modifications not initial development, this last is crucial.

Modern systems must act – they must respond to events, keep processes moving to enable straight through processing, support busy people with no spare time and high throughput demands. Any model of such a system should model the decisions within it as first class objects to enable this. Further, these decisions – these decision services – must be easy to change and controlled by those who understand the business. After all any move by a competitor, any new regulation or policy, any new marketing initiative, any new contract will require the decision making in the system to change. If the business users who understand these drivers cannot make the changes for themselves then the result will be delay, confusion, inaccuracy, lost business and fines. Automate these decisions with business rules and all this can be addressed.

So, model-driven is certainly the future but those models should model decisions and do so explicitly and the decisions should be described using business rules so they can be managed and evolved.


Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

street address database
Why Data-Driven Companies Rely on Accurate Street Address Databases
Big Data Exclusive
predictive analytics risk management
How Predictive Analytics Is Redefining Risk Management Across Industries
Analytics Exclusive Predictive Analytics
data analytics and gold trading
Data Analytics and the New Era of Gold Trading
Analytics Big Data Exclusive
student learning AI
Advanced Degrees Still Matter in an AI-Driven Job Market
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Factual Logic: Why Numbers Count

7 Min Read

Moving Beyond Smart Part Numbers

4 Min Read
Image
Big DataBusiness IntelligenceData MiningData WarehousingHadoopITMapReduceOpen SourceSoftwareWorkforce Data

How Big Data Can Improve Manufacturing Quality

4 Min Read

Back to the basics

3 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 in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
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?