Lucent Technologies:  Optical Networking Group

Marco   Iansiti; Barbara  Feinberg

Case | HBS Case Collection | November 1999

Lucent Technologies: Optical Networking Group

by Marco Iansiti and Barbara Feinberg

Abstract

Set in June 1999, this case describes the development of a new platform product, the Wavestar OLS 400G, that responded both to a demand for greater "bandwidth" and aggressive competitors seeking to supply it. The 400G's development process took only 14 months and pioneered a number of new development approaches, while expanding customer and supplier bases. Yet traditional customers remained concerned that the 400G was not as "feature-rich" as they wanted, and many in Lucent's Optical Networking Group felt "burned out" by the hectic development pace. Students debate what should follow the 400G project. The first option is an evolutionary product, relatively consistent with the later releases of 400G but extending its performance by as much as a factor of two. The second option is to ignite another technological revolution, using a "clean sheet of paper" to completely redesign the 400G platform and potentially achieve more than a four-times performance improvement.

Keywords: Customers; Operations; Product Development; Performance Improvement; Technology; Technology Networks;

Format: Print

14 pages

Find at Harvard

Citation:

Iansiti, Marco, and Barbara Feinberg. "Lucent Technologies: Optical Networking Group." Harvard Business School Case 600-053, November 1999.

In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing. Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna’s pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as “a COVID-19 company?”

Working Paper | HBS Working Paper Series | 2020

Time and the Value of Data

Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and, thereby, a decline in created business value. We model time-dependency as a shift in the probability distribution and derive several counter-intuitive results.
We, theoretically, prove that even an infinite amount of data collected over time may have limited substance for predicting the future, and an algorithm that is trained on a current dataset of bounded size can attain a similar performance. Moreover, we prove that increasing data volume by including older datasets may put a company in a disadvantageous position.
Having these results, we answer questions on how data volume creates a competitive advantage. We argue that time-dependency weakens the barrier to entry that data volume creates for a business. So much that competing firms equipped with a limited, but sufficient, amount of current data can attain better performance. This result, together with the fact that older datasets may deteriorate algorithms' performance, casts doubt on the significance of first-mover advantage in AI-based markets.
We complement our theoretical results with an experiment. In the experiment, we empirically measure the value loss in text data for the next word prediction task. The empirical measurements confirm the significance of time-dependency and value depreciation in AI-based businesses. For example, after seven years, 100MB of text data becomes as useful as 50MB of current data for the next word prediction task.

Keywords: economics of AI; Machine learning; non-stationarity; perishability; value depreciation;

Citation:

Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. View Details

Cite View Details SSRN Read Now

The broad adoption of contact-tracing apps would greatly help combat the spread of COVID-19. But a number of barriers—especially privacy concerns—have hindered progress in many countries that can’t or won’t mandate adoption. A solution is to start with small communities where they would be instantly useful (families, religious communities, workplaces, schools, bars and restaurants, beaches, hotels, trains, airplanes), get everyone to download and use it, and then scale from there. To achieve that, the app should be designed so it is instantly valuable to anyone in the targeted community who downloads it. Managing users’ expectations is also important.

Faculty & Research

Lucent Technologies: Optical Networking Group

Abstract

Citation:

About the Author

More from the Author