We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
    cybersecurity efforts
    How Behavioral Analytics and AI Are Redefining Cybersecurity for Boca Raton Businesses
    14 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Data is still defensible
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 > Data is still defensible
Business Intelligence

Data is still defensible

JamesTaylor
JamesTaylor
4 Min Read
Data is still defensible
Illustration generated with Qwen Image.
SHARE

In Is Data a new defensibility? Abhishek Tiwari argues that even data is not defensible any more. He argues that data integration and the use of new sources of data are key skills but that companies cannot use unique data to differentiate because there is too much data and too much of it is available publicly.

He has a point, of course. Lots of data is available publicly and using it won’t necessarily give you much of a competitive advantage. What I think he misses is the value of data that you have about your customers and their behavior. You know, or at least could know, which of your customers bought what and when. You can track what they look at on your website and map that to your products and offerings. You can track who calls and what they call about. And you can use this data to segment them, make predictions about them and assess them. To a large extent, your competitors cannot do this. This gives you some critical, defensible, advantages:

  • You should be able to make retention offers that are more compelling than the acquisition offers your competitors make when…

Copyright © 2009 James Taylor. Visit the original article at Data is still defensible.

Syndicated from BeyeNetwork

More Read

Finding the Right Sponsor for Your Big Data Project
Finding the Right Sponsor for Your Big Data Project
3 Big Data Myths for Enterprises
SAP’s New Fraud Management Analytical Application
Big Data’s Athletic Moment: Turning Sporting Arenas into Preferred Business Venues
A Revised “Promised Land” of BI

In Is Data a new defensibility? Abhishek Tiwari argues that even data is not defensible any more. He argues that data integration and the use of new sources of data are key skills but that companies cannot use unique data to differentiate because there is too much data and too much of it is available publicly.

He has a point, of course. Lots of data is available publicly and using it won’t necessarily give you much of a competitive advantage. What I think he misses is the value of data that you have about your customers and their behavior. You know, or at least could know, which of your customers bought what and when. You can track what they look at on your website and map that to your products and offerings. You can track who calls and what they call about. And you can use this data to segment them, make predictions about them and assess them. To a large extent, your competitors cannot do this. This gives you some critical, defensible, advantages:

  • You should be able to make retention offers that are more compelling than the acquisition offers your competitors make when trying to steal away your customers
  • You should be able to target your customer acquisition efforts on those people who look the most like your existing customers – after all people like that chose you over your competitors before.
  • You should be able to enhance the publicly available data with your own data to form a picture that is richer and more actionable than someone working from the public data alone.

Of course all this only works if you have the ability to effectively and rapidly develop and use analytic models based on this data (to minimize decision latency) and, in particular, if you have a way to put these analytics to work in the production systems that interact with customers and prospects. Putting a decision management framework in place allows you to do both these things, turning your unique data into decisions that are, in fact, defensible.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article examines AI agents that escalate from legitimate data retrieval to attempted intrusions
OpenAI’s Government Website Incidents Raise a Hard Question for AI Agents: When Should They Stop?
Artificial Intelligence News Security
Flat editorial illustration: The article's core relationship is the alignment between customer behavioral data (visit frequency,
Data-Driven Loyalty: How Restaurants Use Behavioral Analytics to Optimize Revenue
Exclusive
Flat editorial illustration: The article's core relationship is that reliable eCommerce attribution depends on a unified, well-st
How eCommerce Data Teams Can Build Attribution That Holds Up
Big Data Exclusive
Flat editorial illustration: The article's core relationship is the contrast between fragmented inherited data infrastructure (wh
Data Stack Consolidation as a Data Quality and Governance Strategy for Mid-Market Teams
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Big Data, Big Payoff? Why Your Company Needs Data Literate Employees, Now
Best PracticesBig DataBusiness IntelligenceCulture/LeadershipData Management

Big Data, Big Payoff? Why Your Company Needs Data Literate Employees, Now

5 Min Read
Are You A Data Whisperer?
Business IntelligenceData QualityDecision Management

Are You A Data Whisperer?

4 Min Read
Derailing Your Supply Chain BI Project
AnalyticsBusiness IntelligenceData QualityDecision ManagementPredictive AnalyticsWorkforce Data

Derailing Your Supply Chain BI Project

7 Min Read
Can Teachers Use AI-Driven Tools for Remote Teaching More Effectively?
Artificial IntelligenceExclusive

Can Teachers Use AI-Driven Tools for Remote Teaching More Effectively?

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.

How To Get An Award Winning Giveaway Bot
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?