These are top must read data analytic articles from Harvard Business Review (HBR) based on popularity and content. These articles provide crucial updates on the latest technology and innovations happenings around the world in the field of big data and analytics. Even some of the very old articles are still worth reading.
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If you are wondering, what is HBR?
The Harvard Business Review (HBR) is a general management magazine published by Harvard Business Publishing, a wholly owned subsidiary of Harvard University. It is published 6 times a year and is headquartered in Brighton, Massachusetts. HBR's articles cover a wide range of topics that are relevant to various industries, management functions, and geographic locations. These focus on areas including leadership, organizational change, negotiation, strategy, operations, marketing, finance, and managing people.
1. What’s Your Data Strategy? by Leandro Dalle and MuleThomas H. Davenport (May-Jun 2017 issue)
More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all. So, what is the strategy to manage torrents of data?
2. How to Choose the Right Forecasting Technique: By John C. Chambers, Satinder K. Mullick, and Donald D. Smith (Jul 1971)
Here the authors try to explain the potential of forecasting to managers, focusing special attention on sales forecasting for products of Corning Glass Works as these have matured through the product life cycle. Also included is a rundown of forecasting techniques.
3. Data Scientist: the sexiest job of the 21st century: By Thomas H. Davenport and D.J. Patil (Oct 2012 issue)
How the idea of LinkedIn's People You May Know feature really clicked! The key player involved was a "Data Scientist", a title coined by the two authors.
4. How to Make Better Decisions with Less Data: By Tanya Menon and Leigh Thompson (Nov 2016)
Often, teams will stuck in analysis paralysis. Despite the many meetings, task forces, brainstorming sessions, and workshops created to solve any given issue, the teams tend to offer the same solutions — often ones that were recycled from prior problems. How to make better decisions from the date?
5. A Refresher on Regression Analysis: By Amy Gallo (Nov 2015 issue)
One of the most important types of data analysis is regression. You do need to correctly understand and interpret the analysis created by using regression. How to do that?
6. What Artificial Intelligence Can and Can’t Do Right Now: By Andrew Ng (Nov 2016)
Lately the media has sometimes painted an unrealistic picture of the powers of AI. This is what Andrew opens the article with, "As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu’s AI team of some 1,200 people, I’ve been privileged to nurture many of the world’s leading AI groups and have built many AI products that are used by hundreds of millions of people. Having seen AI’s impact, I can say: AI will transform many industries. But it’s not magic. To understand the implications for your business, let’s cut through the hype and see what AI really is doing today."
7. Four Steps to Forecast Total Market Demand: By William Barnett (Jul 1988 issue)
History is filled with stories of companies and sometimes even entire industries that have made grave strategic errors because of inaccurate industrywide demand forecasts. How to accurately forecast market demand?
8. How P&G and American Express Are Approaching AI: By Thomas H. Davenport and Randy Bean
There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. Does that work all the time, how does How P&G and American Express are approaching AI?
9. Know What Your Customers Want Before They Do: By Thomas H. Davenport, Leandro DalleMule, and John Lucker (Dec 2011 issue)
Today’s distracted consumers, bombarded with information and options, often struggle to find the products or services that will best meet their needs. The shorthanded and often poorly informed floor staff at many retailing sites can’t begin to replicate the personal touch that shoppers once depended on—and consumers are still largely on their own when they shop online.
How can analytics help to bridge this gap?
10. The Sexiest Job of the 21st Century is Tedious, and that Needs to Change: by Sean Kandel (Apr 2014 issue)
Which phase does a data scientist spend more time on? Data Discovery, data structuring and creating context. Should they shift their focus?
11. What Separates a Good Data Scientist from a Great One: by Thomas C. Redman (Jan 2013 issue)
Better than the Best! Great data scientists bring four mutually reinforcing traits to bear that even the good ones can’t.
12. 10 Kinds of Stories to Tell with Data: by Tom Davenport (Nov 2013 issue)
Narrative is—along with visual analytics—an important way to communicate analytical results to non-analytical people. Explore the 10 types.
13. How to Start Thinking Like a Data Scientist: by Thomas C. Redman (Nov 2013 issue)
You don’t have to be a data scientist or a Bayesian statistician to tease useful insights from data. The author demonstrates how to think with a small exercise.
14. Stop Searching for That Elusive Data Scientist: by Michael Schrage(Sep 2014 issue)
Stop hunting for that data science unicorn and/or silver bullet. What to do instead?
15. How to Explore Cause and Effect Like a Data Scientist: by Thomas C. Redman (Feb 2014 issue)
While we can use data to understand correlation, the more fundamental understanding of cause and effect requires more.
16. Simplify Your Analytics Strategy: by Narendra Mulani (Jun 2015 issue)
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics. How to strategize to avoid this?
17. Making Advanced Analytics Work for You: by Dominic Barton and David Court
Big data could transform the way companies do business, delivering performance gains. How to get the strategy suited to your needs?
18. A Predictive Analytics Primer: by Tom Davenport (Sep 2014 issue)
A brief read on predictive analytics with a focus on customers.
19. The Persuasiveness of a Chart Depends on the Reader, Not Just the Chart: by Scott Berinato (May 2015 issue)
What's more a better way to persuade people than visual information? An interesting read on how good is your data chart is based on the audience's understanding of it and cognitive state.
20. Analytics 3.0: by Thomas H. Davenport (Dec 2013 issue)
A new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.
21. What People Analytics Can’t Capture: by Daniel Goleman (July 2015 issue)
The latest fad in human resources, using big data analytics and personality test scores to predict who is best for a given job – so-called “XQ.”. Do the scores capture accurately all the required skills?
22. Gamification Can Help People Actually Use Analytics Tools: by Lori Sherer-(Feb 2015 issue)
You have to identify the right data and develop useful tools, such as predictive algorithms. But then comes an even tougher task: getting people to actually use the new tools.
23. What Popular Baby Names Teach Us About Data Analytics: by Kaiser Fung (Apr 2015 issue)
Find out what FiveThirtyEight’s Nate Silver and Allison McCann did with the baby names dataset sets an example for all data analysts. Their article represents the best of data journalism.
24. A Better Way to Tackle All That Data: by Chris Taylor (Aug 2013 issue)
Hampered by a shortage of qualified data scientists to perform the work of analysis, big data’s rise is outstripping our ability to perform analysis and reach conclusions fast enough.
25. What Every Manager Should Know About Machine Learning: by Mike Yeomans (July 2015 issue issue)
With the right mix of technical skill & human judgment, machine learning could be a new tool for decision makers. Learn what mistakes to avoid.