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
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: How “Dirty Data” Derails Your Company’s Data Analytics and ROI
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Best Practices > How “Dirty Data” Derails Your Company’s Data Analytics and ROI
Best PracticesData QualityUnstructured Data

How “Dirty Data” Derails Your Company’s Data Analytics and ROI

Brett Stupakevich
Brett Stupakevich
3 Min Read
SHARE

Data Analytics and Dirty Data photo (uncategorized)How much of what your company knows is useful? Which data is current?

Data Analytics and Dirty Data photo (uncategorized)How much of what your company knows is useful? Which data is current? In a recent webinar about the dangers of “Dirty Data” Jay Hidalgo of The Annuitas Group says many companies simply don’t know. He estimated that 30 percent of companies have no strategy for “data hygiene” – removing duplicates or obsolete information. He said 34 percent of companies ask the front-end sales team to update customer and prospect files but even that can be flawed among multi-product or different “views” of the same company, client or transaction.

For sales and marketing especially any data analytics and business intelligence based on “bad data” will yield misleading or incorrect results. In fact, 8 of 10 companies indicated that dirty data is hindering their lead generation campaigns. Instead of cleaning and clarifying, companies pile ever increasing data into storage with a “we’ll sort it all out later” approach, particularly sales and marketing role companies.  But real-time data changes things: the sheer volume of information and how quickly it makes previous knowledge and data obsolete requires changing habits.

Enter a new support industry to recover, clean and manage that information. Data Service Providers, companies that analyze, double-check and connect the dots when you have partial details and need more. Hoovers and ZoomInfo are two examples for finding business people and companies; ThinkorSwim takes every transaction from financial markets to analyze patterns, trends and activity levels. News aggregators, auction pricing guides and search engines are just a few other examples of meta-data (data ABOUT your data).

More Read

Forecast Bleak for Bad UIs
Thinking Efficiency: IT, OT, And The Issue Of Migration
Derailing Your Supply Chain BI Project
BYOD: An Unstoppable Force?
Data Quality is not an Act, it is a Habit

“The problem of data decay is that it’s faster than it’s ever been. Seventy one percent of business cards you collect have at least one change within 12 months,” said Sam Zales, president of ZoomInfo. An estimated 600,000 small businesses are created and vaporize in a five-year-period. Knowing which ones can be critical if that is your marketplace.

No human-powered research effort could possibly keep pace, so a regular schedule and program is an important first-step. Like a health inspector, you can’t expect to check every restaurant daily – but responding to complaints and maintaining a rigorous plan for oversight is a good policy. Testing data for accuracy on a regular schedule answers questions such as “What do you know? And how do you know?”

Then you can confidently answer the question “Are you sure?”

Subscribe to our blog to stay informed on how to improve your company’s data quality and other data analytics topics.

David Wallace
Spotfire Blogging Team

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

composable analytics
How Composable Analytics Unlocks Modular Agility for Data Teams
Analytics Big Data Exclusive
fintech startups
Why Fintech Start-Ups Struggle To Secure The Funding They Need
Infographic News
edge networks in manufacturing
Edge Infrastructure Strategies for Data-Driven Manufacturers
Big Data Exclusive
data mining to find the right poly bag makers
Using Data Analytics to Choose the Best Poly Mailer Bags
Analytics Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Questions to Ask When Choosing a Social Media Monitoring Vendor

6 Min Read

Predictive Analytics World New York City Conference Announces Speaker Line-Up

5 Min Read
cybersecurity mistakes
Best PracticesData ManagementExclusiveITPrivacyRisk ManagementSecurity

7 Disastrous Cybersecurity Mistakes In A Big Data World

8 Min Read

Klout CEO Gets Sauteed at LeWeb London

6 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
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

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?