By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
    hire a marketing agency with a background in data analytics
    5 Reasons to Hire a Marketing Agency that Knows Data Analytics
    7 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Don’t Sweat the Small Stuff, Except in Data Quality
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Don’t Sweat the Small Stuff, Except in Data Quality
Uncategorized

Don’t Sweat the Small Stuff, Except in Data Quality

SteveSarsfield
Last updated: 2009/05/05 at 1:16 AM
SteveSarsfield
5 Min Read
SHARE

April was a busy month. I was the project manager on a new web application, nearly completed my first German web site (also as project manager) and released the book “Data Governance Imperative.” All this real work has taken me away from something I truly love – blogging.

I did want to share something that affected my project this month, however. Data issues can come in the smallest of places and can have a huge effect on your time line.

For the web project I completed this month, the goal was to replace a custom-coded application with a similar application built within a content management system. We had to migrate log in data of users of the application, all with various access levels, to the new system.

During go live, we were on a tight deadline to migrate the data, do final testing of the new application, and seamlessly switch everyone over. That all had to happen on the weekend. No one would be the wiser come Monday morning. If you’ve ever done an enterprise application upgrade, you may have followed a similar plan.

More Read

analyzing big data for its quality and value

Use this Strategic Approach to Maximize Your Data’s Value

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing
Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC
Quality Control Tips for Data Collection with Drone Surveying
3 Huge Reasons that Data Integrity is Absolutely Essential

We had done our profiling and knew that there were no data issues. However, when the migration actually took place, lo and behold – the old system allowed # as a…


April was a busy month. I was the project manager on a new web application, nearly completed my first German web site (also as project manager) and released the book “Data Governance Imperative.” All this real work has taken me away from something I truly love – blogging.

I did want to share something that affected my project this month, however. Data issues can come in the smallest of places and can have a huge effect on your time line.

For the web project I completed this month, the goal was to replace a custom-coded application with a similar application built within a content management system. We had to migrate log in data of users of the application, all with various access levels, to the new system.

During go live, we were on a tight deadline to migrate the data, do final testing of the new application, and seamlessly switch everyone over. That all had to happen on the weekend. No one would be the wiser come Monday morning. If you’ve ever done an enterprise application upgrade, you may have followed a similar plan.

We had done our profiling and knew that there were no data issues. However, when the migration actually took place, lo and behold – the old system allowed # as a character in the username and password while the new system didn’t. It forced us to stop the migration and write a rule to handle the issue. Even with this simple issue, the time line came close to missing its Monday morning deadline.

Should we have spotted that issue? Yes, in hindsight we could have better understood the system restrictions on the username and password and set up a custom business rule in the data profiler to test it. We might have even forced the users to change the # before the switch while they were still using the old application.

The experience reminds me that data quality is not just about making the data right, it’s about making the data fit for business purpose – fit for the target application. When data is correct for one legacy application, it can be unfit for others. It reminds me that you can plan and test all you want, but you have to be ready for hiccups during the go live phase of the project. The tools, like profiling, are there to help you limit the damage. We were lucky in that this database was relatively small and reload was relatively simple once we figured it all out. For bigger projects, more complete staging of the project – making dry run before the go live phase would have been more effective.

Covering the world of data integration, data governance, and data quality from the perspective of an industry insider.

Link to original post

TAGGED: data quality
SteveSarsfield May 5, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence
data Analytics instagram stories
Data Analytics Helps Marketers Make the Most of Instagram Stories
Analytics
data breaches
How Hospital Security Breaches Devastate Local Communities
Policy and Governance
analyst,women,looking,at,kpi,data,on,computer,screen
What to Know Before Recruiting an Analyst to Handle Company Data
Analytics

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

analyzing big data for its quality and value
Big Data

Use this Strategic Approach to Maximize Your Data’s Value

6 Min Read
data lineage tool
Big Data

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

6 Min Read
data quality and role of analytics
Data Quality

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

8 Min Read
data collection with drone use
Data Collection

Quality Control Tips for Data Collection with Drone Surveying

9 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
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?