You Tube Articles for next weeks video
Here is why you and your company need us for AI. We have been talking about this for a long time.
Our most popular:
“If Your Data is Bad Your Machine Learning Tools Are Useless”
“ Ensure High Quality Data Powers Your AI”
“ Can Your Data Be Trusted?”
“What Managers Should Ask About AI Models and Data Sets”
“What Does It Actually Take to Build a Data Driven Culture?”
Professionals Cannot Do their work.
The epidemiologist who can’t properly model the spread of the disease and is scared that their advice to re-open may kill people.
The technician sent to the wrong address on a service call and annoyed that they will be late for his daughter’s big game.
The commuter wanting to know when the next 6 train will arrive.
The purchasing manager who can’t trust that they are getting a fair price on personal protective equipment.
The risk manager who can’t tell for sure whether a potential new client is on a watch list.
The marketing manager, who simply doesn't know what features customers want next.
The pollster who doesn’t know whether responders lie more now than before.
The environmentalist, who is concerned that measurements in the field are drifting.
Evecutives Cannot Lead
The CEO, moments away from deciding how many people to furlough, and frustrated that the sales forecasts just look wrong.
The head of IT who system gets blamed because the people don’t trust the numbers.
The head of Finance whose staff must work overtime checking, cross-checking, and checking again before every quarterly report
The Operations Manager, who wonders where the inventory is.
The senior manager who can’t get a straight answer to “how many customers do we have?”
The judge who worries if the evidence is tainted.
The military leader who needs better estimates of a potential enemy’s capabilities.
The Board member trying to vet a candidate for CEO
Our personal lives grow More complex
The parent who still doesn’t know if their child is going back to the classroom,..., so they can go back to the office!
The baker who wants to know when yeast will be available at their grocery store.
The hotel worker who doesn’t know when (or even if) they will have a job and worried about feeding their children.
The DIYer, angrily returning home empty-handed, after the stores’ website told them a needed part was in-stock when it wasn’t.
The highschool kid trying to catch some waves during their lunch break and wants to know when high tide is.
The construction worker who needs to know how deep to dig the support beams.
The fitness nut who loves to go to the gym but doesn't know when it will be safe to do so.
Old Content Bibliography
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Most Recent:
Tension between business executives and IT professionals can be managed in ways that build respect for each other’s capabilities — and open new doors in the process. “The Trust Problem That Slows Digital Transfomation,” MIT Sloan Management Review, July, 26, 2022.
Five guidelines to help you decide who does what. “Build Better Management Systems to Put Your Data to Work,” Harvard Business Review, June 30, 2022.
Data Science project failure can often be attributed to poor problem definition, but early intervention can prevent it. “Framing Data Science Problems the Right Way From the Start,” MIT Sloan Management Review, April 14, 2022. *with Roger Hoerl and Diego Kounen
Companies need to design projects around the employees who actually use them. “Your Data Initiatives Can’t Just Be for Data Scientists,” Harvard Business Review, March 22, 2022.
For example, the word “customer” can mean different things to different departments.“Effective Digital Transformation Depends on Common Language,” with Dave Hay, Lwanga Yonke, and John Zachman, Harvard Business Review, December 14, 2021.
To deliver on the promise of data-backed technology, such as AI, companies must address underlying restraining forces. “What’s Holding Your Data Program Back?” MIT Sloan Management Review, August 2, 2021.
“Visualizing Change with Force-Field Analysis,” MIT Sloan Management Review, August 2, 2021. (Note: complete toolkit also available here as well)
Data science initiatives should be integrated with the overall business strategy, and then overseen by an intermediary group that works between the company and its data scientists. “The Data Science Management Process,” MIT Sloan Management Review, July 13, 2021.
The same processes and quality management that inform physical supply chains should be applied to data. “Your Data Supply Chains Are Probably a Mess. Here's How to Fix Them,” Harvard Business Review, June 24, 2021.
Testing can guide decisions such as who needs to work in an office and what work hours are optimal. “Experiments and Data for Post-Covid-19 Work Arrangements,” MIT Sloan Management Review, March 23, 2021.
Leaders can initiate successful data strategies by focusing on data quality, building organizational capabilities, and putting data to work in new ways. “Top Down Leadership for Data: Seven Ways to Get Started,” MIT Sloan Management Review, December 2, 2020.
To better align data teams with business operations, a new organizational structure is needed. “To Succeed With Data Science, First Build the ‘Bridge,’” MIT Sloan Management Review, October 22, 2020.
To implement successful data programs companies need to shift goals, muster resources and align people. “Getting Serious About Data and Data Quality,” MIT Sloan Management Review, September 28, 2020.
The U.S. needs professional management and leadership of its health data supply chain. “To Fight Pandemics, We Need Better Data,” MIT Sloan Management Review, August 25, 2020.
“When the Data Tide Goes Out, You Find Out Whose Swimming Naked,” Medium-Towards Data Science, June 11, 2020.
Its about technology, data, process and organizational change. “Digital Transformation Comes Down to 4 Key Areas,” Harvard Business Review, May 21, 2020.
Protect your competitive advantage. “Your Organization Needs a Proprietary Data Strategy,” Harvard Business Review, May 4, 2020.
As the COVID-19 pandemic unfolds misinformation seems to travel even faster than the virus. “Fighting Misinformation, and Building Trust, in a Crisis,” Medium, April 22, 2020.
There's no shortage of great opportunities.
“Use Data to Accelerate Your Business Strategy,” Harvard Business Review, March 3, 2020.
You’ll thank yourself later.
“To Improve Data Quality, Start at the Source,” Harvard Business Review, February 10, 2020.
If you don’t protect users’ data, they’ll find a company who will.
“Do You Care About Privacy as Much as Your Customers Do?” Harvard Business Review, January 28, 2020.
Don’t make yours more ambitious than it needs to be.
“Most Analytics Projects Don’t Require Much Data,” Harvard Business Review, October 3, 2019.
Analytics has to be about more than averages. “Do You Understand the Variance in Your Data?” Harvard Business Review, August 16, 2019.
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).
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
"Getting in Front of Data Quality"
Harvard Business Review Webinar, December 3, 2013. Check out the recap or replay of the event.
“Organizational Imperatives in the Era of Big Data”
Harvard Business Review Webinar, December 5, 2012
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.