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: Days Without A Data Quality Issue
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 > Uncategorized > Days Without A Data Quality Issue
Uncategorized

Days Without A Data Quality Issue

JimHarris
Last updated: 2009/10/27 at 11:01 PM
JimHarris
8 Min Read
SHARE

In 1970, the United States Department of Labor created the Occupational Safety and Health Administration (OSHA). The mission of OSHA is to prevent work-related injuries, illnesses, and deaths. Based on statistics from 2007, since OSHA’s inception, occupational deaths in the United States have been cut by 62% and workplace injuries have declined by 42%.

Contents
A Culture of Data QualityData Quality AssessmentsData GovernanceDays Without A Data Quality IssueRelated Posts

OSHA regularly conducts inspections to determine if organizations are in compliance with safety standards and assesses financial penalties for violations. In order to both promote workplace safety and avoid penalties, organizations provide their employees with training on the appropriate precautions and procedures to follow in the event of an accident or an emergency.

Training programs certify new employees in safety protocols and indoctrinate them into the culture of a safety-conscious workplace. By requiring periodic re-certification, all employees maintain awareness of their personal responsibility in both avoiding workplace accidents and responding appropriately to emergencies.

Although there has been some debate about the effectiveness of the regulations and the enforcement policies, over the years OSHA has unquestionably .. .. …

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



In 1970, the United States Department of Labor created the Occupational Safety and Health Administration (OSHA). The mission of OSHA is to prevent work-related injuries, illnesses, and deaths. Based on statistics from 2007, since OSHA’s inception, occupational deaths in the United States have been cut by 62% and workplace injuries have declined by 42%.

OSHA regularly conducts inspections to determine if organizations are in compliance with safety standards and assesses financial penalties for violations. In order to both promote workplace safety and avoid penalties, organizations provide their employees with training on the appropriate precautions and procedures to follow in the event of an accident or an emergency.

Training programs certify new employees in safety protocols and indoctrinate them into the culture of a safety-conscious workplace. By requiring periodic re-certification, all employees maintain awareness of their personal responsibility in both avoiding workplace accidents and responding appropriately to emergencies.

Although there has been some debate about the effectiveness of the regulations and the enforcement policies, over the years OSHA has unquestionably brought about many necessary changes, especially in the area of industrial work site safety where dangerous machinery and hazardous materials are quite common. 

Obviously, even with well-defined safety standards in place, workplace accidents will still occasionally occur. However, these standards have helped greatly reduce both the frequency and severity of the accidents. And most importantly, safety has become a natural part of the organization’s daily work routine.

A Culture of Data Quality

Similar to indoctrinating employees into the culture of a safety-conscious workplace, more and more organizations are realizing the importance of creating and maintaining the culture of a data quality conscious workplace. A culture of data quality is essential for effective enterprise information management.

Waiting until a serious data quality issue negatively impacts the organization before starting an enterprise data quality program is analogous to waiting until a serious workplace accident occurs before starting a safety program.

Many data quality issues are caused by a lack of data ownership and an absence of clear guidelines indicating who is responsible for ensuring that data is of sufficient quality to meet the daily business needs of the enterprise. In order for data quality to be taken seriously within your organization, everyone first needs to know that data quality is an enterprise-wide priority.

Additionally, data quality standards must be well-defined, and everyone must accept their personal responsibility in both preventing data quality issues and responding appropriately to mitigate the associated business risks when issues do occur.

Data Quality Assessments

The data equivalent of a safety inspection is a data quality assessment, which provides a much needed reality check for the perceptions and assumptions that the enterprise has about the quality of its data. 

Performing a data quality assessment helps with a wide variety of tasks including: verifying data matches the metadata that describes it, preparing meaningful questions for subject matter experts, understanding how data is being used, quantifying the business impacts of poor quality data, and evaluating the ROI of data quality improvements.

An initial assessment provides a baseline and helps establish data quality standards as well as set realistic goals for improvement. Subsequent data quality assessments, which should be performed on a regular basis, will track your overall progress.

Although preventing data quality issues is your ultimate goal, don’t let the pursuit of perfection undermine your efforts. Always be mindful of the data quality issues that remain unresolved, but let them serve as motivation. Learn from your mistakes without focusing on your failures – focus instead on making steady progress toward improving your data quality.

Data Governance

The data equivalent of verifying compliance with safety standards is data governance, which establishes policies and procedures to align people throughout the organization. Enterprise data quality programs require a data governance framework in order successfully deploy data quality as an enterprise-wide initiative. 

By facilitating the collaboration of all business and technical stakeholders, aligning data usage with business metrics, enforcing data ownership, and prioritizing data quality, data governance enables effective enterprise information management.

Obviously, even with well-defined and well-managed data governance policies and procedures in place, data quality issues will still occasionally occur. However, your goal is to greatly reduce both the frequency and severity of your data quality issues. 

And most importantly, the responsibility for ensuring that data is of sufficient quality to meet your daily business needs, has now become a natural part of your organization’s daily work routine.

Days Without A Data Quality Issue

Organizations commonly display a sign indicating how long they have gone without a workplace accident. 

Proving that I certainly did not miss my calling as a graphic designer, I created this “sign” for Days Without A Data Quality Issue:

Days Without A Data Quality Issue

Related Posts

Poor Data Quality is a Virus

DQ-Tip: “Don’t pass bad data on to the next person…”

The Only Thing Necessary for Poor Data Quality

Data Governance and Data Quality

Link to original post

TAGGED: data quality
JimHarris October 27, 2009
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

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.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

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?