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• Case | HBS Case Collection | September 2020

  True North: Pioneering Analytics, Algorithms and Artificial Intelligence

  Karim R. Lakhani, Kairavi Dey and Hannah Mayer

  True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented businesses. Then, in 2007, True North invested in Meru Cabs, a ride-sharing service with a growing fleet of cabs that had to change its business model with the launch of tech-based platforms like Uber. This experience led True North to significantly alter its stance on the importance of technology-powered business models. The team learned that all businesses needed to effectively become technology businesses to remain relevant. True North initiated the “Analytics, Algorithms and Artificial Intelligence” (3A) project. A system, Kelp, was designed and developed internally to facilitate the transformation of True North into an AI-first firm based on the introduction of data-driven decision making and productivity-enhancing digital tools. In 2020, as different elements of Kelp were launched, the leadership team met to discuss the future of this technology.

  Keywords: Technology; Management; Operations; Organizations; Leadership; Innovation and Invention; Computer Industry; Technology Industry;

  Citation:

  Lakhani, Karim R., Kairavi Dey, and Hannah Mayer. "True North: Pioneering Analytics, Algorithms and Artificial Intelligence." Harvard Business School Case 621-042, September 2020.  View Details

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• 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

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• Case | HBS Case Collection | September 2019 (Revised August 2020)

  Obsidian: Product or Platform?

  Karim Lakhani, Peter Barrett, Julia Kelley and Kerry Herman

  Citation:

  Lakhani, Karim, Peter Barrett, Julia Kelley, and Kerry Herman. "Obsidian: Product or Platform?" Harvard Business School Case 620-018, September 2019. (Revised August 2020.)  View Details

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