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: Top Ten Root Causes of Data Quality Problems: Part 2
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > Top Ten Root Causes of Data Quality Problems: Part 2
Culture/LeadershipData Quality

Top Ten Root Causes of Data Quality Problems: Part 2

SteveSarsfield
SteveSarsfield
5 Min Read
Top Ten Root Causes of Data Quality Problems: Part 2
Photo by ACWells on Pixabay (https://pixabay.com/photos/man-computer-tired-zoom-meeting-6982044/)
SHARE

Part 2 of 5: Renegades and Pirates

Part 2 of 5: Renegades and Pirates
In this continuing series, we’re looking at root causes of data quality problems and the business processes you can put in place to solve them.  In part two, we examine IT renegades and corporate pirates as two of the root causes for data quality problems.

Root Cause Number Three: Renegade IT and Spreadmarts
A renegade is a person who deserts and betrays an organizational set of principles. That’s exactly what some impatient business owners unknowingly do by moving data in and out of business solutions, databases and the like. Rather than wait for some professional help from IT, eager business units may decide to create their own set of local applications without the knowledge of IT. While the application may meet the immediate departmental need, it is unlikely to adhere to standards of data, data model or interfaces. The database might start by making a copy of a sanctioned database to a local application on team desktops. So-called “spreadmarts,” which are important pieces of data stored in Excel spreadsheets, are easily replicated to team desktops. In this scenario, you lose control of versions as well as standards. There are no backups, versioning or business rules.

Root Cause Attack Plan

More Read

CERN: A Case Study in "Big Science" Data Management
CERN: A Case Study in “Big Science” Data Management
Social Media Analytics: Performance Measurement Done Right
No Data, No Problem: My Lean Six Sigma Data Collection Secrets
From “The Farm” to FarmVille
The Importance of Cleaning Up Your Dark Data
  • Corporate Culture – There should be a consequence for renegade data, making it more difficult for the renegades to create local data applications.
  • Communication – Educate and train your employees on the negative impact of renegade data.
  • Sandbox – Having tools that can help business users and IT professionals experiment with the data in a safe environment is crucial. A sandbox, where users are experimenting on data subsets and copies of production data, has proven successful for many for limiting renegade IT.
  • Locking Down the Data – A culture where creating unsanctioned spreadmarts is shunned is the goal.  Some organizations have found success in locking down the data to make it more difficult to export.


Root Cause Number Four: Corporate Mergers

Corporate mergers increase the likelihood for data quality errors because they usually happen fast and are unforeseen by IT departments. Almost immediately, there is pressure to consolidate and take shortcuts on proper planning. The consolidation will likely include the need to share data among a varied set of disjointed applications. Many shortcuts are taken to “make it happen,” often involving known or unknown risks to the data quality.
On top of the quick schedule, merging IT departments may encounter culture clash and a different definition of truth.  Additionally, mergers can result in a loss of expertise when key people leave midway through the project to seek new ventures.

Root Cause Attack Plan

  • Corporate Awareness – Whenever possible civil division of labor should be mandated by management to avoid culture clashes and data grabs by the power hungry.
  • Document – Your IT initiative should survive even if the entire team leaves, disbands or gets hit by a bus when crossing the street.  You can do this with proper documentation of the infrastructure.
  • Third-party Consultants – Management should be aware that there is extra work to do and that conflicts can arise after a merger. Consultants can provide the continuity needed to get through the transition.
  • Agile Data Management – Choose solutions and strategies that will keep your organization agile, giving you the ability to divide and conquer the workload without expensive licensing of commercial applications.

This post is an excerpt from a white paper available here. More to come on this subject in the days ahead.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

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
Emergency responder and nurse reviewing tablet with data dashboards
Evaluating Workforce Assessment Tools: Looking Beneath the Dashboard at Psychometric Data
Exclusive Software

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

In a World Full of Data, Can Analytics See the Market Trends?
AnalyticsBig DataBusiness IntelligenceBusiness RulesData QualityPredictive AnalyticsWeb Analytics

In a World Full of Data, Can Analytics See the Market Trends?

4 Min Read
Does Data Quality Matter in Social Media?
CRMData QualitySocial Data

Does Data Quality Matter in Social Media?

4 Min Read
How Big is Big Data?
AnalyticsData Quality

How Big is Big Data?

5 Min Read
Text Analytics for Legacy BI Analysis
AnalyticsData QualitySecurity

Text Analytics for Legacy BI Analysis

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 chatbot
How AI Website Chatbots Improve Customer Support and Lead Generation
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?