If not, is it their fault-or yours? “Do Your Data Scientist Know the ‘Why’ Behind Their Work?” Harvard Business Review, May 16, 2019.
On the job learning is how most of us will get the data skills we need. “5 Concepts That Will Help Your Team Be More Data Driven,” Harvard Business Review, November 1, 2018.
“Ensuring high-quality private data for responsible data science: vision and challenges” Journal of Data and Information Quality (JDIQ), January 2019. Coauthored with Divesh Srivastava and Monica Scannapieco
How to avoid failure. “5 Ways Your Data Strategy Can Fail,” Harvard Business Review, October 11, 2018.
Strike the right balance between specialized terminology and common vocabulary. “What to Do When Each Department Uses Different Words to Describe the Same Thing,” Harvard Business Review, July 30, 2018.
Five steps to ensure higher quality data. “If Your Data Is Bad, Your Machine Learning Tools Are Useless,” Harvard Business Review, April 4, 2018.
Four steps for avoiding common mistakes. "Are You Setting Your Data Scientists Up to Fail?" Harvard Business Review, January 25, 2018.
The cost of bad data is an astonishing 15% to 25% of revenue for most companies. "Seizing Opportunity in Data Quality." Sloan Management Review, November 27, 2017.
Data is in far worse shape than most managers realize. "Only 3% of Companies Data Meets Basic Quality Standards." Harvard Business Review, September 11, 2017.
Seven ways to create value and profit. "Does Your Company Know What to do With All Its Data?" Harvard Business Review, June 15, 2017.
Challenge your thinking at every step. "Root Out Bias From Your Decision-Making Process," Harvard Business Review, March 10, 2017.
They know the stories behind the numbers. "The Best Data Scientists Get Out and Talk to People," Harvard Business Review, January 26, 2017.
How much is it costing you? "Bad Data Costs the U.S. $3 Trillion Per Year," Harvard Business Review, September 22, 2016.
A simple exercise to see the errors and calculate the costs. "Assess Whether You Have a Data Quality Problem," Harvard Business Review, July 28, 2016.
How to be a Data Provocateur. "Data Quality Should be Everyone's Job," Harvard Business Review, May 20, 2016.
Good measurements enlighten, but bad ones mislead.
"Four Steps for Thinking Critically About Data Measurements," Harvard Business Review, March 17, 2016.
"Customer Data: Get the Basics Right," LinkedIn, February 1, 2016.
Flawed doesn't mean unusable:
"Can Your Data Be Trusted," Harvard Business Review, October 29, 2015.
Start by becoming a data-driven leader:
"Dispel Your Teams Fear of Data," Harvard Business Review, July 16, 2015.
The only way to address data quality for connected devices is to build it from the very beginning:
"Build Data Quality Into the Internet of Things"
Co-authored with Tom Davenport, The Wall Street Journal, August 26, 2015.
Fear has replaced apathy as the number one enemy of data. This three-part 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"
Coauthored with Tom Davenport, 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.
most important and influential works
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"
Harvard Business Review, September 17, 2014.
How AT&Ts approach to data helped them solve a big problem:
"Even the Tiniest Error Can Cost a Company Millions"
Harvard Business Review, August 7, 2014.
An easy exercise to learn data analytics:
“How to Start Thinking Like a Data Scientist”
Harvard Business Review, November 29, 2013.
The great data scientist in four traits:
“What Separates a Good Data Scientist from a Great One”
Harvard Business Review, January 28, 2013.
The potential for "this changes everything discoveries are real."
“Integrate Data into Products, or Get Left Behind”
Harvard Business Review, June 28, 2012.
Too often businesses trust what they hear from the outside without question, while discarding inside sources.
“Why Outsiders Trump Insiders (And Why They Shouldn’t)”
Sloan Management Review, Winter, 2009, p. 96.
Treated as assets, data offer many ways to create value.
“Putting Your Data to Work in the Marketplace”
Harvard Business Review, September, 2008, p 34.
Presents a framework for selecting the most appropriate accuracy measurement under different circumstances.
“Measuring Data Accuracy: A Framework and Review”
Contributed chapter in Information Quality, M.E.Sharpe, 2005, pp. 21-36.
Compares data against other assets. The first series attempt to treat them (data) that way (as assets).
“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.
The first recognition that data quality impacted profit and competitive position.
“Improve Data Quality for Competitive Advantage”
Sloan Management Review, 36, No 2, p. 99-107, Winter 95.
Rather than viewing data as "static," stored away in databases, view them as "organic," coming into existence, changing, being used, combining with other data etc. “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).
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.
WEBINARS AND PODCASTS
“Data in Every Employees Hands,” Harvard Business Review, March 25, 2019.
Data Quality is critical to being able to make informed business decisions. Still why are companies not utilizing data to its fullest?
"Fear is the Number One Enemy of Data"
October 6, 2015. The Business Sherpas podcast, production of Bedrock Data.
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
“Organizational Imperatives in the Era of Big Data”
Harvard Business Review Webinar, December 5, 2012
BOOKS & PERIODICALS
Getting in Front on Data
By Thomas C. Redman, Ph.D.