Tom Redman's newest articles, blogs and webinars.


"Build Data Quality Into the Internet of Things," coauthored with Tom Davenport, The Wall Street Journal, August 26, 2015.

Fear has replaced apathy as the number one enemy of data.  This blog series explores the implications.

"Fear has Replaced Apathy As the Number One Enemy of Data: Implications for Lovers of Data," Dataversity, July 27, 2015.
"Dispel Your Team's Fear of Data," Harvard Business Review, July 16, 2015.
"Fear has Replaced Apathy As the Number One Enemy of Data," OCDQ Blog, July 13, 2015.

 

Rethinking old strategies

"4 Business Models for the Data Age," Harvard Business Review, May 20, 2015.

Urging companies to focus on proprietary data so they can distinguish themselves from others:
"Getting Advantage from Proprietary Data," The Wall Street Journal, March 11, 2015.

Leading change is always hard:
"Overcome Your Companies Resistance to Data" Harvard Business Review, March 30, 2015.

Even small inaccuracies can lead to bad decisions:
"Stop Making Excuses for Your Flawed Data," Harvard Business Review, February 12, 2015. 

Marketers today have easy access to capabilities, from social media to big data to cloud computing, that their predecessors could but dream about just a few years ago. As a result, they can customize their message to each individual customer and deliver it even more powerfully. But is it really so simple?
"Data Driven Marketing: How to Engage Your Customers," Harvard Business Review:webinar, January 15, 2015.


**Here are Tom's most important and  influential works**

For a complete bibliography please contact us at info@dataqualitysolutions.com

How to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability:
Data Driven: Profiting From Your Most Important Business Asset, Harvard Business Press, 2008.

Management-not technology-is the solution:
"Data’s Credibility Problem," Harvard Business Review, December 2013, p. 84-88.

Analytics can't replace intuition:
"Algorithms Make Better Predictions-Except When They Don't,"  HBR Blog, September 17, 2014.

How AT&Ts approach to data helped them solve a big problem:
"Even the Tiniest Error Can Cost a Company Millions." HBR Blog, August 7, 2014.

An easy exercise to learn data analytics:
How to Start Thinking Like a Data Scientist,” HBR Blog, November 29, 2013.

The great data scientist in four traits:
What Separates a Good Data Scientist from a Great One,” HBR Blog, January 28, 2013. 

The potential for "this changes everything discoveries are real."
Integrate Data into Products, or Get Left Behind,” HBR Blog, June 28, 2012. 

“Why Outsiders Trump Insiders (And Why They Shouldn’t),” Sloan Management Review, Winter, 2009, p. 96. 

Putting Your Data to Work in the Marketplace,” Harvard Business Review, September, 2008, p 34. 

Measuring Data Accuracy:  A Framework and Review,” Contributed chapter in Information Quality, Advances in Management Information Systems Series, Armonk: M.E.Sharpe, 2005, pp. 21-36.

Data as a Resource:  Properties, Implications, and Prescriptions for Management,”with A. V. Levitin.  Sloan Management Review, Volume 40, Number 1, p. 89-101, Fall 1998.

Improve Data Quality for Competitive Advantage,” Sloan Management Review, 36, No 2, p. 99-107, Winter 95.

A Model of Data (Life) Cycles with Applications to Quality,” with A. V. Levitin, Information and Software Technology, 35, No 4, p. 217-224, April 1993. (available in print only)

PUBLISHED ARTICLES

"Overcome Your Company's Resistance to Data," Harvard Business Review, March 30, 2015.

"Stop Making Excuses for Your Companies Flawed Data," Harvard Business Review, February 20, 2015.

"Algorithms Make Better Predictions-Except When They Don't,"  Harvard Business Review, September 17, 2014.

"Its Not About the Algorithm Anymore," Wall Street and Technology," September 2, 2014.

"Even the Tiniest Error Can Cost a Company Millions." Harvard Business Review, August 7, 2014.

"Data Doesn't Speak for Itself," Harvard Business Review, April 29, 2014.

"How to Explore Cause and Effect Like a Data Scientist," Harvard Business Review, February 19, 2014.

Data’s Credibility Problem,” Harvard Business Review, December, 2013, p. 84-88.

How to Start Thinking Like a Data Scientist,” Harvard Business Review Blog site, November 29, 2013.

"Are You Ready for a Chief Data Officer" Harvard Business Review Blog site, October 30, 2013.

Seven Questions to Ask Your Data Geeks” (with Bill Sweeney), Harvard Business Review Blog site, June 10, 2013. 

Invest in Proprietary Data for Competitive Advantage,” Harvard Business Review Blog site, March 28, 2013. 

What Separates a Good Data Scientist from a Great One,” Harvard Business Review Blog site, January 28, 2013.

Get Responsibility for Data Out of IT,” Harvard Business Review Blog site, October 22, 2012. 

Integrate Data into Products, or Get Left Behind,” Harvard Business Review Blog site, June 28, 2012. 

“Why Outsiders Trump Insiders (And Why They Shouldn’t),” Sloan Management Review, Winter, 2009, p. 96. 

Putting Your Data to Work in the Marketplace,” Harvard Business Review, September, 2008, p 34. 

Measuring Data Accuracy:  A Framework and Review,” Contributed chapter in Information Quality, Advances in Management Information Systems Series, Armonk: M.E.Sharpe, 2005, pp. 21-36.

Data as a Resource:  Properties, Implications, and Prescriptions for Management,” with A. V. Levitin.  Sloan Management Review, Volume 40, Number 1, p. 89-101, Fall 1998.

Improve Data Quality for Competitive Advantage,” Sloan Management Review, 36, No 2, p. 99-107, Winter 95.

A Model of Data (Life) Cycles with Applications to Quality,” with A. V. Levitin, Information and Software Technology, 35, No 4, p. 217-224, April 1993. (available in print only)

 


PATENTS

No. 6,028,970, Method and Apparatus for Enhancing Optical Character Recognition, with DiPiazza, P., 2000.

No. 5,396,612, Data Tracking Arrangements for Improving the Quality of Data Stored in a Database, with Huh, Y., and Pautke, R., 1995.
 

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