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: Data Mining Book Review: Decision Management 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 > Data Mining Book Review: Decision Management Systems
Book ReviewDecision ManagementPredictive Analytics

Data Mining Book Review: Decision Management Systems

SandroSaitta
SandroSaitta
3 Min Read
SHARE

DMSI recently read the last book from James Taylor, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. As a data miner, I was interested by the subtitle of the book.

DMSI recently read the last book from James Taylor, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. As a data miner, I was interested by the subtitle of the book. Although, the book is really well written, I’m a bit disapointed regarding the content for someone in analytics. I was expecting real methodologies and examples to move from analytics to actions in the company. How to successfully apply predictive analytics in the industry. The books only partly answer this question and gives mainly examples of business rules and how they are applied in companies.

If you come from the business side (e.g. C-level), the book may be interesting but the explanations about predictive analytics are quite light and you won’t see all benefits of these techniques in the company. I know that the main focus of the book is not about teaching analytics. It seems also not to be about filling the gap between analytics and action. I’m thus a bit confused about the real objective of the book. It is also explaining concepts at a very high level of abstraction. It is thus not directly usable in practice.

The book is divided in three parts. In the first part, James explains what are DMS and why they are useful for the company. The second part focuses on building these DMS. The third part is about the enablers (people, processes and technology), i.e. the aspects that will allow such DMS to be a successful initiative. Personally, I found the book very interesting starting from chapter 6 (Design and Implement Decision Services). The topic of fraud detection and prevention is very well studied throughout the book.

More Read

Travel funding available for UseR! and DSC 2009
How Text Mining Can Help Your Business Dig For Gold
Ben Goertzel’s Report on AGI-09: The Second Conference on…
Data is the differentiator
New Podcast About Decision Management Systems

A very strange choice has been made to repeat in full text the expression Decision Management Systems hundreds of times. It thus make the reading sometimes a bit tiring. The simple use of the abbreviation DMS would have solved this issue. To conclude, I found the book interesting and well written. However, keep in mind that it is written with a very high level of abstraction. You will thus have a clear understanding of the domain, but no practical advices.

Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

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
data migration risk prevention
Best Approach to Risk Management for Data Migration in Data-Driven Businesses
Big Data Data Management Exclusive Risk Management

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

business intelligence software for companies
AnalyticsBig DataBusiness IntelligenceCollaborative DataDecision ManagementExclusiveFeaturedKnowledge Management

4 Ways to Use Business Intelligence in Your Business

6 Min Read

The evolution of BRMS (part 1)

4 Min Read

Last call for papers for Business Rules Forum/EDM Summit 2009

5 Min Read

Where Did the ‘Data Explosion’ Come From?

6 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
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

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?