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
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    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
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Share the Love… of Data Quality
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Share the Love… of Data Quality
AnalyticsBig Data

Share the Love… of Data Quality

MIKE20
MIKE20
4 Min Read
Share the Love… of Data Quality
Illustration generated with Qwen Image.
SHARE

A distributed approach to Data Quality may result in better data outcomes

A recent news article on Information-Management.com suggested a link between inaccurate data and “lack of a centralized approach.”

A distributed approach to Data Quality may result in better data outcomes

A recent news article on Information-Management.com suggested a link between inaccurate data and “lack of a centralized approach.”

More Read

What is R?
What is R?
3 Essential Big Data Security Protocols To Be Aware Of
7 Big Data Blunders You’re Thankful Your Company Didn’t Make
Beyond RDBMS: Databases for Modern Applications
4 Ways to Prevent Dirty Data From Spoiling Analytics

But I’m not sure that “lack of centralization” is the underlying issue here; I’d suggest the challenge is generally more down to “lack of a structured approach”, and as I covered in my blog post “To Centralise or not to Centralise, that is the Question”, there are organizational cultures that don’t respond well (or won’t work at all) to a centralized approach to data governance.

When you then extend this to the more operational delivery processes of Data Quality Management, I’d go so far as to suggest that a distributed and end-user oriented approach to managing data quality is actually desirable, for several reasons:

* Many organisations just haven’t given data quality due consideration, and the impacts can be significant, but often hidden.
* Empowering users to thinks about and act upon data challenges can become a catalyst for a more structured, enterprise wide approach.
* By managing data quality issues locally, knowledge and expertise is maintained as close to point-of-use as possible.
* In environments where funding is hard to come by or where there isn’t appetite to establish critical mass for data quality activity, progress can still be made and value can still be delivered

I also observe two trends in business, that have been consistent in the twenty-plus years that I’ve been working, which are contributing to make a centralised delivery of data outcomes ever-more difficult:

1) Human activity has become more and more complex. We’re living in a mobile, connected, graphical, multi-tasking, object-oriented, cloud-serviced world, and the rate at which we’re collecting data is showing no sign of abatement. It may well be that our data just isn’t “controllable” in the classic sense any more, and that what’s really needed is mindfulness. (I examined this in my post “Opening Pandora’s Box“)

2) Left to their own devices, business systems and processes will tend to decay towards a chaotic state over time, and it is management’s role to keep injecting focus and energy into the organisation. If this effort can be spread broadly across the organisation, then there is an overall cultural change towards better data. (I covered aspects of this in my post “Business Entropy – Bringing Order to the Chaos“)

Add the long-standing preoccupation that management consultants have with mapping “Business Process” rather than mapping “Business Data” and you end up in the situation that data does not get nearly enough attention. (And once attention IS payed, then the skills and capabilities to do something about it are often lacking).

Change the culture, change the result – that doesn’t require centralisation to make it happen.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article explains that training robots for physical interaction requires three distinct data cate
Physical AI: What Data Do You Need to Train a Robot?
Artificial Intelligence Exclusive Robotics
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
Analytics Big Data Exclusive Software
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

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

What Does The Rise of Blockchain Technology Mean For Big Data?
Big DataBlockchainData ManagementData QualityExclusivePrivacySecurity

What Does The Rise of Blockchain Technology Mean For Big Data?

6 Min Read
How Can I Prove Content Marketing Generates ROI?
Analytics

How Can I Prove Content Marketing Generates ROI?

5 Min Read
How the New Revenue Recognition Rules Could Impact Budgeting and Planning
AnalyticsBig DataBusiness IntelligenceCloud ComputingSoftware

How the New Revenue Recognition Rules Could Impact Budgeting and Planning

6 Min Read
On AOL and Small Data
Data QualityData Warehousing

On AOL and Small Data

4 Min Read

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

5 Great Tips for Using Data Analytics for Website UX
5 Great Tips for Using Data Analytics for Website UX
Big Data
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

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?