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Вміст надано Let’s Talk Data. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Let’s Talk Data або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
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Bad Data Costs the U.S. $3 Trillion Per Year

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Manage episode 385028728 series 2304478
Вміст надано Let’s Talk Data. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Let’s Talk Data або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Join Dr. Thomas C. Redman, aka “the Data Doc,” President of Data Quality Solutions, and Kim Huth-Ziegler and Autumn Moss from the Global Product and Solution marketing team as they discuss an article written by Tom Redman titled “Bad Data Costs the U.S. $3 Trillion Per Year.”

In the original article, published in September of 2016, consideration is given to the astronomical cost of bad data in the U.S. Companies are spending more than ever to correct mistakes in data, but have also absorbed the cost of “checking the data and correcting the data” as just another work task in daily work activities. Reducing the costs of bad data is no secret: you have to shine a light on it and reduce it as much as possible, as quickly as possible.

Join Dr. Thomas C. Redman, aka “the Data Doc” President of Data Quality Solutions, and Kim Huth-Ziegler and Autumn Moss from the Global Product and Solution marketing team as they discuss an article written by Tom Redman titled “Bad Data is Sapping Your Teams Productivity” The original article, published in Nov 2022 , explores how data quality hasn’t kept up with the current pace of business. Data quality is costing businesses not only time and money but is also contributing to the growing problem of distrust in business operations leading to increased customer frustration. Many managers in today’s business are seeking to obtain all of the data, when in reality they are given a set of incomplete or bad data leaving them to manage “by the seat of their pants.” In this podcast, we will explore the root of the problem and ways to overcome it, which may not be as hard as it seems.

Speakers: Thomas C. Redman, Ph.D., the Data Doc, President Data Quality Solutions. Kim Huth- Ziegler Solutions & Product Marketing Senior Specialist – BTP SAP

Autumn Moss Data Management | Business Technology Platform (BTP) Global Solution Marketing Manager, SAP

CTA's:

This article was written by Thomas C. Redman, “the Data Doc,”

and originally published at Harvard Business Review on

HBR Can Your Data Be Trusted

March 2022

What Boards and Senior Executives Need To Know, and Do, About Data

*Aberdeen Report June 2022

A ROADMAP TO MODERNIZATION WITH A DATA-FIRST MINDSET

*SAP.com – Data Management

Data Management Solutions from SAP

Data Management Solutions from SAP

https://letstalkdatapodcast.podbean.com/

The Friday Afternoon Measurement - YouTube

  continue reading

100 епізодів

Artwork
iconПоширити
 
Manage episode 385028728 series 2304478
Вміст надано Let’s Talk Data. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Let’s Talk Data або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

Join Dr. Thomas C. Redman, aka “the Data Doc,” President of Data Quality Solutions, and Kim Huth-Ziegler and Autumn Moss from the Global Product and Solution marketing team as they discuss an article written by Tom Redman titled “Bad Data Costs the U.S. $3 Trillion Per Year.”

In the original article, published in September of 2016, consideration is given to the astronomical cost of bad data in the U.S. Companies are spending more than ever to correct mistakes in data, but have also absorbed the cost of “checking the data and correcting the data” as just another work task in daily work activities. Reducing the costs of bad data is no secret: you have to shine a light on it and reduce it as much as possible, as quickly as possible.

Join Dr. Thomas C. Redman, aka “the Data Doc” President of Data Quality Solutions, and Kim Huth-Ziegler and Autumn Moss from the Global Product and Solution marketing team as they discuss an article written by Tom Redman titled “Bad Data is Sapping Your Teams Productivity” The original article, published in Nov 2022 , explores how data quality hasn’t kept up with the current pace of business. Data quality is costing businesses not only time and money but is also contributing to the growing problem of distrust in business operations leading to increased customer frustration. Many managers in today’s business are seeking to obtain all of the data, when in reality they are given a set of incomplete or bad data leaving them to manage “by the seat of their pants.” In this podcast, we will explore the root of the problem and ways to overcome it, which may not be as hard as it seems.

Speakers: Thomas C. Redman, Ph.D., the Data Doc, President Data Quality Solutions. Kim Huth- Ziegler Solutions & Product Marketing Senior Specialist – BTP SAP

Autumn Moss Data Management | Business Technology Platform (BTP) Global Solution Marketing Manager, SAP

CTA's:

This article was written by Thomas C. Redman, “the Data Doc,”

and originally published at Harvard Business Review on

HBR Can Your Data Be Trusted

March 2022

What Boards and Senior Executives Need To Know, and Do, About Data

*Aberdeen Report June 2022

A ROADMAP TO MODERNIZATION WITH A DATA-FIRST MINDSET

*SAP.com – Data Management

Data Management Solutions from SAP

Data Management Solutions from SAP

https://letstalkdatapodcast.podbean.com/

The Friday Afternoon Measurement - YouTube

  continue reading

100 епізодів

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