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
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Building a More Powerful Data Quality Scorecard
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > Building a More Powerful Data Quality Scorecard
Uncategorized

Building a More Powerful Data Quality Scorecard

SteveSarsfield
SteveSarsfield
4 Min Read
SHARE

Most data governance practitioners agree that a data quality scorecard is an important tool in any data governance program. It provides comprehensive information about quality of data in a database, and perhaps even more importantly, allows business users and technical users to collaborate on the quality issue.

However, if we show that 7% of all tables have data quality issues, the number is useless – there is no context. You can’t say whether it…


Most data governance practitioners agree that a data quality scorecard is an important tool in any data governance program. It provides comprehensive information about quality of data in a database, and perhaps even more importantly, allows business users and technical users to collaborate on the quality issue.

However, if we show that 7% of all tables have data quality issues, the number is useless – there is no context. You can’t say whether it is good or bad, and you can’t make any decisions based on this information. There is no value associated with the score.

More Read

Image
Are US Data Centers Too Outdated to Handle Current Needs?
How to use Symantec products to stop buying (more) storage
Shovel-ready SOA
Digg Getting Faceted Search?
Building Relationships: 52 First Dates

In an effort to improve processes, the data governance teams should roll-up the data into metrics into slightly higher formulations. In their book “Journey to Data Quality”, authors Lee, Pipino, Funk and Wang correctly suggest that making the measurements quantifiable and traceable provide the next level of transparency to the business. The metrics may be rolled up into a completeness rating, for example if your database contains 100,000 name and address postal codes and 3,500 records are incomplete, 3.5% of your postal codes failed and 96.5% pass. Similar simple formulas exist for Accuracy, Correctness, Currency and Relevance, too. However, this first aggregation still doesn’t support data governance, because business users aren’t thinking that way. They have processes that are supported by data and it’s still a stretch figuring out why this all matters.

Views of Data Quality Scorecard
Your plan must be to make data quality scorecards for different internal audiences – marketing, IT, c-level, etc.

The aggregation might look something like this:You must design the scorecards to meet the needs of the interest of the different audiences, from technical through to business and up to executive. At the beginning of a data quality scorecard is information about data quality of individual data records. This is the default information that most profilers will deliver out of the box. As you aggregate scores, the high-level measures of the data quality become more meaningful. In the middle are various score sets allowing your company to analyze and summarize data quality from different perspectives. If you define the objective of a data quality assessment project as calculating these different aggregations, you will have much easier time maturing your data governance program. The business users and c-level will begin to pay attention.

Business users are looking for whether the data supports the business process. They want to know if the data is facilitating compliance with laws. They want to decide whether their programs are “Go”, “Caution” or “Stop” like a traffic light. They want to know whether the current processes are giving them good data so they can change them if necessary. You can only do this by aggregating the information quality results and aligning those results with business.

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

Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Look, Ma. No ETL

4 Min Read

No Privacy Through Difficulty

1 Min Read

Change.gov

2 Min Read

Oracle Financials Is in the Cloud

15 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
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

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?