Which candidate should we hire? Who should be promoted? How should we choose which people get which shifts? In the hope of making better and fairer decisions about personnel matters such as these, companies have increasingly adopted AI tools only to discover that they may have biases as well. How can we decide whether to keep human managers or go with AI? This article offers … [ Read more ]
Content: Article | Author: Peter Cappelli | Source: Harvard Business Review | Subjects: Human Resources, IT / Technology / E-Business
[An] effective way to detect what [customer data] has leaked, and through which possible partner, is to pre-poison the data, sowing into it information that can provide a reference point in the case of a leak.
Content: Quotation | Author: Ariel Silverstone | Source: Fast Company | Subjects: Best Practices, IT / Technology / E-Business
Common misconceptions about cloud are holding companies back from capturing the full benefits available.
Content: Article | Authors: Isabelle Tamburro, Leandro Santos, Mark Gu, Rich Isenberg | Source: McKinsey Quarterly | Subject: IT / Technology / E-Business
Many organizations have spent decades understanding how to make trade-offs in their businesses, where the profit pools are, and the structure of their value chains. Digital changes all that. First, on an overarching level, digital currently destroys more economic value for incumbents than it creates. There are two main drivers. The first one is that digital creates transparency that allows a much higher percentage of … [ Read more ]
Content: Quotation | Author: Laura LaBerge | Source: McKinsey Quarterly | Subjects: IT / Technology / E-Business, Strategy, Trends / Analysis
Focus on six organizational elements to build a world-class data and insights capability in the post–COVID-19 world.
Content: Article | Authors: Amy Peirce, Anil Khurana, Roger Wery | Source: strategy+business | Subject: IT / Technology / E-Business
Shortsighted solutions to recurring problems—antipatterns—often sabotage a company’s transformation.
Content: Article | Authors: Kartikeya Swami, Sven Blumberg, Thomas Delaet | Source: McKinsey Quarterly | Subjects: IT / Technology / E-Business, Organizational Behavior
Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation. The key to a successful upgrade—and significant potential rewards—is agility.
Content: Article | Authors: Antonio Castro, Henning Soller, Jorge Machado, Matthias Roggendorf | Source: McKinsey Quarterly | Subject: IT / Technology / E-Business
We make decisions every day about who we hire, how we deploy them, what teams we put them in, what we have them working on. Then we sit in judgment of their performances. Every one of those decisions can be made better with data. Not all those decisions are equally important, so you don’t have to bring it to bear in all of them, but … [ Read more ]
Content: Quotation | Author: Bill Schaninger | Source: McKinsey Quarterly | Subjects: Human Resources, IT / Technology / E-Business
Wouldn’t it be useful if you had a checklist to make sure your search engine optimization (SEO) is Web-worthy in 2019—kinda like the massive checklists that airplane pilots have to make sure the plane takes off, gets to where it’s supposed to, and lands safely?
Now you do: a comprehensive SEO checklist from LeapFroggr, a mobile app and software development firm.
The massive infographic covers major components … [ Read more ]
Content: Article | Author: Vahe Habeshian | Source: MarketingProfs | Subjects: IT / Technology / E-Business, Marketing / Sales
Algorithms have taken a lot of heat recently for producing biased decisions. Should we be outraged by bias reflected in algorithmic output? Yes. But the way organizations respond to their algorithms determines whether they make strides in debiasing their decisions or further perpetuate their biased decision making.
Content: Article | Author: Jennifer M. Logg | Source: Harvard Business Review | Subjects: Human Resources, IT / Technology / E-Business
Companies gather and analyze data to fine-tune their operations, whether it’s to help them figure out which webpage design works best for customers or what features to include in their product or service to boost sales. Marketers, in particular, use data analytics to answer questions like this: To put people in a shopping mood, is it better to make the webpage banner blue or yellow? … [ Read more ]
Content: Article | Authors: Christophe Van den Bulte, Ron Berman | Source: [email protected] | Subjects: IT / Technology / E-Business, Marketing / Sales
Today’s technology platforms are not just new versions of legacy systems. They allow you to design a completely new digital enterprise — as long as you follow these guidelines. For more insight, see “A Guide to Modernizing Your Company’s Technology.”
Content: Article | Authors: Leon Cooper, Milan Vyas | Source: strategy+business | Subject: IT / Technology / E-Business
New automation techniques can provide the first step toward continuous, tech-enabled redesign of critical operations—forming an intuitive ops-to-tech cycle in which tech improves ops, and vice versa.
Content: Article | Authors: Allen Weinberg, David Taylor, Federico Berruti | Source: McKinsey Quarterly | Subjects: IT / Technology / E-Business, Operations
The most valuable companies in the world have one thing in common: all are leaders in the platform economy. In a new book, David Yoffie and colleagues identify key strategies and tactics for success on digital platforms.
Content: Article | Authors: David Yoffie, Martha Lagace | Source: Harvard Business School (HBS) Working Knowledge | Subject: IT / Technology / E-Business
When looking at artificial intelligence from the perspective of economics, we ask the same, single question that we ask with any technology: What does it reduce the cost of? Economists are good at taking the fun and wizardry out of technology and leaving us with this dry but illuminating question. The answer reveals why AI is so important relative to many other exciting technologies. AI … [ Read more ]
Content: Quotation | Author: Ajay Agrawal | Source: McKinsey Quarterly | Subjects: Economics, IT / Technology / E-Business
In the midst of predictive analytics and machine learning, with big data sweeping across sectors and industries, the importance of small data cannot be overstated. Big data and machine learning concern themselves with correlation, not causation. Computers excel at precision, rigor, and consistency, but they are not designed to integrate social interactions across domains, or traverse data online and offline, or come up with a … [ Read more ]
Content: Quotation | Author: Howard Yu | Source: strategy+business | Subject: IT / Technology / E-Business
It can be difficult to discern how a mathematical model trained by deep learning arrives at a particular prediction, recommendation, or decision. A black box, even one that does what it’s supposed to, may have limited utility, especially where the predictions or decisions impact society and hold ramifications that can affect individual well-being. In such cases, users sometimes need to know the “whys” behind the … [ Read more ]
Content: Quotation | Authors: James Manyika, Mehdi Miremadi, Michael Chui | Source: McKinsey Quarterly | Subject: IT / Technology / E-Business
Companies can determine whether they should invest in blockchain by focusing on specific use cases and their market position.
Content: Article | Authors: Askhat Zhumaev, Brant Carson, Giulio Romanelli, Patricia Walsh | Source: McKinsey Quarterly | Subject: IT / Technology / E-Business
Companies should do two things to harness the power of analytics in their marketing functions. First, rather than create data and then decide what to do with it, firms should decide what to do first, and then which data they need to do it. This means better integrating marketing and IT, and developing systems around the information needs of the senior management team instead of … [ Read more ]
Content: Quotation | Authors: Carl F. Mela, Christine Moorman | Source: Harvard Business Review | Subjects: IT / Technology / E-Business, Marketing / Sales
Rotman School of Management professor Ajay Agrawal explains how AI changes the cost of prediction and what this means for business.
Content: Article | Author: Ajay Agrawal | Source: McKinsey Quarterly | Subjects: Economics, IT / Technology / E-Business