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: Predictive Analytics: 8 Things to Keep in Mind (Part 1)
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Predictive Analytics: 8 Things to Keep in Mind (Part 1)
Business IntelligenceData MiningPredictive Analytics

Predictive Analytics: 8 Things to Keep in Mind (Part 1)

Editor SDC
Editor SDC
6 Min Read
SHARE

I, along with two of my colleagues (Anand Rao & Dick Findlay), recently conducted a workshop at the World Research Group’s Predictive Modeling conference at Orlando. As part of the workshop, I spoke about a list of 8 things that organizations should keep in mind as they consider investing in predictive analytics.

In this post, I will list the 8 points and discuss the first one. Subsequent posts will explore the rest of the themes.

  1. Understand the cost of a wrong decision
  2. Strategic and operational decisions need different predictive modeling tools and analysis approaches
  3. Integration of multiple data sources, especially third-party data, provides better predictions
  4. Statistical techniques and tools are mature and by itself not likely to provide significant competitive advantage
  5. Good data visualization leads to smarter decisions
  6. Delivering the prediction at the point of decision is critical
  7. Prototype, Pilot, Scale
  8. Create a predictive modeling process & architecture

Theme 1: Understand the Cost of a Wrong Decision

More Read

The 4 Es of Social Media Strategy
Understanding the Importance of AI in 3D Printing Applications
Location Intelligence: Top 12 industries
A powerful computing tool that allows scientists to extract…
Analytics is Not a Dirty Word

Is it even worth investing the resources on developing…


I, along with two of my colleagues (Anand Rao & Dick Findlay), recently conducted a workshop at the World Research Group’s Predictive Modeling conference at Orlando. As part of the workshop, I spoke about a list of 8 things that organizations should keep in mind as they consider investing in predictive analytics.

In this post, I will list the 8 points and discuss the first one. Subsequent posts will explore the rest of the themes.

  1. Understand the cost of a wrong decision
  2. Strategic and operational decisions need different predictive modeling tools and analysis approaches
  3. Integration of multiple data sources, especially third-party data, provides better predictions
  4. Statistical techniques and tools are mature and by itself not likely to provide significant competitive advantage
  5. Good data visualization leads to smarter decisions
  6. Delivering the prediction at the point of decision is critical
  7. Prototype, Pilot, Scale
  8. Create a predictive modeling process & architecture

Theme 1: Understand the Cost of a Wrong Decision

Is it even worth investing the resources on developing a predictive analytics solution for a problem? That is the first question which should be answered. The best way to answer it is to understand the cost of the wrong decision. I define a decision as ‘wrong’ if the outcome is not a desired event. For example, if the direct mail sent to a customer does not lead the desired call to the 800 number listed, then it was a ‘wrong’ decision to send the mail to that customer.

A few months ago my colleague Bill told a story which illustrates the point.

Each year Bill takes his family to Cleveland to visit his mom. They stay in an old Cleveland hotel downtown. The hotel is pretty nice with all the trappings  that you would expect of an old and reputable establishment. Last time they decided to have breakfast at the hotel across the street at the Ritz. After the breakfast when Bill and his family were in the lobby, the property manager spotted him and the kids and walked over to talk. He chatted for a few minutes and probably surmised that Bill was a reasonably seasoned traveler and told the kids to wait for him.  He walked away and came back with a wagon full of toys.  He let each kid pick a toy out of the wagon.  Think about it. They were not even guests at the Ritz, all they did was have breakfast at the Ritz! The kids loved the manager and Bill remembered the gesture. Fast forward to this holiday season, and sure enough Bill and his family booked a suite at the Ritz for six days. For the price of a few nice toys, the manager converted a stay that generated a few thousand dollars in room charges, meals, and parking.

Now suppose Bill did not go back to the hotel, which was the desired outcome by the hotel manager. What would have been the cost of manager’s ‘wrong’ decision?  The cost of a few toys. The cost compared to the potential upside is negligible. Does it make sense for the hotel to build a predictive model to decide which restaurant diners to offer toys so that they come back and stay? I don’t think so.

Understanding the cost of wrong decision upfront saves one from making low value investments in predictive analytics.

Link to original post

TAGGED:integrationpredictive analytics
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

Enterprise Integration
Cloud ComputingComputingData Management

Why Custom Enterprise Integration Demands Cloud Services

4 Min Read
small forex traders
Big DataExclusive

Big Data Eases the Burden of Small Forex Traders

6 Min Read
Image
AnalyticsMarketing

4 Biggest Predictive Analytics Mistakes with Marketing Automation

6 Min Read

Talk Analytics with Executives: 4 Things You Must Understand

8 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 and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

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?