By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: It’s in the Language We Use… Isn’t It?
Share
Notification Show More
Latest News
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > It’s in the Language We Use… Isn’t It?
Culture/LeadershipITMarketing

It’s in the Language We Use… Isn’t It?

Gayle Nixon
Last updated: 2012/12/24 at 3:30 PM
Gayle Nixon
2 Min Read
SHARE

Len Dubois low res pic[1] By Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte Hanks Trillium Software

Len Dubois low res pic[1] By Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte Hanks Trillium Software

Ten years ago, analysts and vendors began to tell us that poor data quality is a business issue and not just an IT issue. But listening to industry expert jargon during these ten years, it’s been quite obvious who the buyers of data quality solutions have been…Information Technology professionals only. Not any more….

Terms such as “parse, integrate, standardize, and match” – and more recently buzz words like “parallel processing, pre-built, The Cloud, and Big Data” – have littered the canvas of vendor sales and marketing messaging. I think we can all admit that these terms neither endear us to business people, nor make it easy for line-of-business managers to understand their role in improving the value of information – and in particular its quality. 

More Read

Contextomy No More

More and more though, it certainly appears that business consumers of information are coming to the fore in the Data Quality market. They’re not only expressing their frustration with the data’s inability to meet their business needs, but also with the solutions being used to solve those data quality problems.

In the past, our buyers mostly occupied positions in the IT realm and asked for ways to “parse data” and “integrate systems data” but that’s changing fast. Today, Claims VPs demand to know how we will “mitigate claims losses” and “improve claims cycle time,” while Risk and Compliance executives are focused on “data due diligence in the face of regulatory requirements.”

So, it’s in the language, terminology and business objectives we use that dictates who will be most able to determine how poor data quality manifests itself in your business processes.  It is also in how we will help the business take responsibility for solving these challenges.

TAGGED: context, operationalizing data
Gayle Nixon December 24, 2012
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

Contextomy No More

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.

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

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?