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
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 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
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

Your Data Quality Wish List [Part Two]
Who will manage Big Data?
Using Your Human Database to Select a Database
Data Quality Industry: Problem Solvers or Enablers?
Business Trip Yields Even a Lesson for Consumer Goods Companies
  • 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

composable analytics
How Composable Analytics Unlocks Modular Agility for Data Teams
Analytics Big Data Exclusive
fintech startups
Why Fintech Start-Ups Struggle To Secure The Funding They Need
Infographic News
edge networks in manufacturing
Edge Infrastructure Strategies for Data-Driven Manufacturers
Big Data Exclusive
data mining to find the right poly bag makers
Using Data Analytics to Choose the Best Poly Mailer Bags
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

ViDEO: Strata 2012 Talk on Humans, Machines and the Dimensions of Microwork

0 Min Read

What’s up with Watson?: Responses to comments in Wall Street Journal

6 Min Read

Text Analytics for Legacy BI Analysis

8 Min Read

DQ-View: From Data to Decision

1 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 chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
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.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?