By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Failing to Address Data Quality and Consistency – A Series of Unfortunate Data Warehousing/Business Intelligence Events
Share
Notification Show More
Latest News
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Warehousing > Failing to Address Data Quality and Consistency – A Series of Unfortunate Data Warehousing/Business Intelligence Events
Data Warehousing

Failing to Address Data Quality and Consistency – A Series of Unfortunate Data Warehousing/Business Intelligence Events

RickSherman
Last updated: 2010/08/24 at 2:11 PM
RickSherman
5 Min Read
SHARE

If data warehouses could talk, they might say “Don’t shoot me, I am just the messenger!”

If data warehouses could talk, they might say “Don’t shoot me, I am just the messenger!”

People sometimes think the data warehouse (DW) has caused a problem with data quality or inconsistency, when in fact, the problem started in the enterprise’s source systems. But because the DW can be the only place where business people can view enterprise data, it becomes the messenger delivering the bad news.  It’s an easy target to blame.

More Read

The Good Data

A Confederacy of Data Defects
How Does Predictive Analytics Work?
Learning About Data Visualization
The Data Quality of Dorian Gray

Although the DW program can’t be blamed for creating the data quality problems or inconsistency, you can blame it for not identifying and addressing the problems.  Too many people in DW programs believe the following myths:

  • The DW does not create or alter data, but passes on what is in the systems-of-record (SORs). A corollary is that any data quality or inconsistency problems need to be resolved by the SORs.
  • The data is fine and does not have any significant quality issues (The SOR owners, IT or business people may state this.)

Don’t fall into these traps. Don’t assume anything about the state of the data. The areas where data quality and inconsistency problems lurk:

  • Data quality within SOR applications may be “masked” by corrections made within reports or spreadsheets created from this data. The people who told you the data is fine might not even be aware of these “adjustments.”
  • Data does not age well. Although data quality may be fine now, there’s always the chance that you’ll have problems or inconsistencies with the historical data. The problems can also arise when applications like predicative analytics need to use historical data.
  • Data quality may be fine within each SOR application, but may be very inconsistent across applications. Many companies have master data inconsistency problems with product, customer and other dimensions that will not be apparent until the data is loaded into the enterprise DW.

The unfortunate events are that data quality and inconsistency problems will become evident in the enterprise DW and it will be blamed. Even if the DW program can prove the problems reside in the SORs it still will be blamed for being surprised and not proactively dealing with it. The worst case is that the DW program’s credibility will be dealt a blow from which it cannot recover.

What should be done?

Never assume the data quality or inconsistency problems don’t exist or that the DW program can ignore them. The steps you should undertake:

  • Obtain data quality and consistency estimates and assumed metrics as part of a Service Level Agreement (SLA) when gathering the business and data requirements from the business and SOR application owners.
  • Perform a data profiling and source systems analysis to determine the current state of data quality and consistency within and across SORs.
  • Create a gap analysis between current state and desired state, i.e. data quality metrics in SLA.
  • ropose data architecture and data integration tasks that are needed to bridge that gap. This should include timeline, tasks, resources and costs to implement and maintain an ongoing set of data quality processes.
  • Negotiate with business and SOR application owners if effort or costs are too high to lower metrics within SLA. 

You can’t fix a problem unless you can identify and admit that one exists. Data quality and inconsistency problems are fairly common, so don’t be surprised that they exist and don’t lose the DW credibility by being blindsided.

(This is part oSeries_unfortunatef our ongoing Series of Unfortunate Data Warehousing and Business Intelligence Events. Click for the complete series, so far.)

TAGGED: advice
RickSherman August 24, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

The Good Data

3 Min Read

A Confederacy of Data Defects

3 Min Read

How Does Predictive Analytics Work?

5 Min Read

Learning About Data Visualization

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.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?