Ment.io: Knowledge Analytics for Team Decision Making

Yael  Grushka-Cockayne; Jeffrey T. Polzer; Susie L. Ma; Shlomi  Pasternak

Case | HBS Case Collection | April 2020

Ment.io: Knowledge Analytics for Team Decision Making

by Yael Grushka-Cockayne, Jeffrey T. Polzer, Susie L. Ma and Shlomi Pasternak

Abstract

Ment.io was a software platform that used proprietary data analytics technology to help organizations make informed and transparent decisions based on team input. Ment was born out of founder Joab Rosenberg’s frustration that, while organizations collected ever increasing amounts of data, the information gathering process did not capture human insights, which he viewed as critical to the use of the data. In early 2020, Rosenberg felt that the company he had started five years ago was taking off, but he wondered how to manage the team’s limited engineering resources to help Ment stand out in a crowded software market. Should the company improve Ment’s user interface, develop new features, or focus on its innovative algorithm?

Keywords: Decision Making; Technology; Knowledge; Knowledge Acquisition; Knowledge Management; Operations; Information Management; Product; Product Development; Entrepreneurship; Business Startups; Communications Industry; Information Industry; Information Technology Industry; Web Services Industry; Middle East; Israel;

Language: English

Format: Print

11 pages

Educators

Citation:

Grushka-Cockayne, Yael, Jeffrey T. Polzer, Susie L. Ma, and Shlomi Pasternak. "Ment.io: Knowledge Analytics for Team Decision Making." Harvard Business School Case 420-078, April 2020.

In December 2018, the blockchain startup Fluidity was about to participate in its first tokenization deal, which would create digital access to property rights in a 12-unit Manhattan condominium complex. The deal was proof-of-concept for Fluidity, which hoped to develop a business digitizing securities issuance for other real estate assets on the basis of its technical facility with blockchain technology, and in conjunction with its broker-dealer partner, Propellr. A successful transaction would only prompt larger and more urgent strategic questions, however. Among them was the appropriate revenue model for Fluidity—whether to grow by providing its technical services to broker-dealers, by going into tokenized securities issuance, or by harnessing its trading technology to facilitate a secondary market in tokenized securities.

This is a supplement to the “People Analytics at Teach For America (A)” case. In this supplement, Managing Director Michael Metzger must decide how to extend his team’s predictive analytics work.

As of mid-2016, national nonprofit Teach For America (TFA) had struggled with three consecutive years of declining application totals, and senior management was re-examining the organization’s strategy, including recruitment and selection. A few months earlier, former TFA corps member Michael Metzger had spearheaded the launch of a predictive analytics team responsible for integrating strategy across TFA’s recruitment and admissions continuum. Metzger believed that predictive analytics could help address TFA’s recruitment and admissions challenges. Now, TFA’s senior management wanted Metzger and his team to put their data models to the test. From a data modeling standpoint, what variables and models should TFA consider as it began a new recruitment season, and, more generally, how heavily should TFA rely on predictive models to reverse the negative trends?

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Ment.io: Knowledge Analytics for Team Decision Making

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