By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Target variables matter but so do decisions.
Share
Notification Show More
Latest News
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > Decision Management > Target variables matter but so do decisions.
Decision ManagementPredictive Analytics

Target variables matter but so do decisions.

JamesTaylor
Last updated: 2012/04/10 at 1:01 PM
JamesTaylor
7 Min Read
SHARE

Dean Abbot wrote a great post recently “Why Defining the Target Variable in Predictive Analytics is Critical” in which he referenced the CRISP-DM approach to building predictive analytic models and talked about the importance of target variable selection in building an effective model. The thrust of Dean’s post was the crucial point that because

Dean Abbot wrote a great post recently “Why Defining the Target Variable in Predictive Analytics is Critical” in which he referenced the CRISP-DM approach to building predictive analytic models and talked about the importance of target variable selection in building an effective model. The thrust of Dean’s post was the crucial point that because

The target variable carries with it all the information that summarizes the outcome we would like to predict

More Read

predictive analytics in dropshipping

Predictive Analytics Helps New Dropshipping Businesses Thrive

Promising Benefits of Predictive Analytics in Asset Management
Albanian Bitcoin Investors Tap the Power of Predictive Analytics
Predictive Analytics Improves Trading Decisions as Euro Rebounds
Can Predictive Analytics Help Traders Navigate Bitcoin’s Volatility?

we must be careful how we select it and we must recognize that the target variable we select will have a material impact on our predictive analytic modeling. But how do we ensure the right context to ask this question? What must we do first, before we can pick the right target variable?

Decision Management Solutions is working with a number of analytic modeling teams using CRISP-DM (or something very similar) to manage their analytic process. The first step in this process is Business Understanding in which the analytic team is supposed to develop some understanding of the business problem to guide their effort. To Dean’s point, this business understanding had better result in a good target variable selection or our modeling will not go well. But what can we use to build business understanding? Most teams and companies have no real standard for this and so we have been working on some ideas here at Decision Management Solutions and come up with something that looks like this:

  • A statement of the analytic insight that is to be developed
    In Dean’s example, Claim Fraud Likelihood for instance.
  • An assessment of the information available to develop that insight
    Claims databases, customer databases, convictions and litigation information, case management files etc.
  • An explicit list of the business decisions that will be made differently because of that analytic insight
    This is critical. Understanding the various business decisions that should be improved will drive the selection of target variables and much more. In this case, for instance, we might develop very different models if our objective was to improve the initial determination of fraud decision, the case manager assignment decision or the litigate decision. These decisions should use the same information we are using to build the model so that we can be sure that the data we need to power the model will be available when we make these decisions.
  • A set of business objectives or measures that will be met or improved by changing these decisions
    Any decision has an impact on one of more business metrics or objectives. Any change  to decision-making will likely change this impact. An analytic effort must understand which objectives/measures are being targeted for improvement, which are being held steady and which are potentially being traded off for improvement elsewhere. The set of decisions determines which objectives and measures should be considered.
  • An impact analysis showing the systems and business processes that rely on or implement all those decisions impacted by the analytic insight
    Decisions are implemented in systems such as the Claims Processing System or Case Management System and support business processes such as Process Claims. Understanding this helps ensure that the model developed will be one that works in the real-world environment of decision-making. If the system is COBOL and batch then this may prevent real-time scoring of a model or execution of more complex scoring logic for instance.
  • The set of organizational units that own the definition of how these decisions should be made, the set of organizations that must make these decisions day-to-day, as well as those that are impacted by any change in the decision making.
    This determines who needs to buy into the  results of the model, who needs to understand it and approve it and who needs to be able to access it and when.

Taken together this information will focus an analytic project effectively and help select the right target variable, right approach to modeling and ensure an effective deployment plan later. Building this information requires:

  • An approach to decomposing the design of the decision-making being targeted so that these decisions are understood in some detail.
  • A mapping of this decomposition to the  objectives, metrics, processes and systems of the business.
  • A collaborative software environment to allow business, IT and analytics teams to work together and ensure these models become part of an ongoing model of the business, linking projects and reducing effort in subsequent projects.

I wrote about such a process in my recent book and we are now using it in our consulting projects. Results look good so far, with some great quick wins where analytic teams changed their approach once they understood the decision-making they were trying to influence.

Interested in our approach? Drop me a line  – jtaylor@decisionmanagementsolutions.com. Got a different approach, post a comment as I am always keen to learn what works.


Copyright © 2012 http://jtonedm.com James Taylor

JamesTaylor April 10, 2012
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
analyst,women,looking,at,kpi,data,on,computer,screen
Predictive Analytics

Promising Benefits of Predictive Analytics in Asset Management

11 Min Read
predictive analytics helps Albanian bitcoin investors
Blockchain

Albanian Bitcoin Investors Tap the Power of Predictive Analytics

9 Min Read
benefits of data analytics for financial management
Predictive Analytics

Predictive Analytics Improves Trading Decisions as Euro Rebounds

10 Min Read

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

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?