The Future of Competitive Strategy

8

Introduction

©2022 MIT. This excerpt is from The Future of Competitive Strategy by Mohan Subramaniam, published by The MIT Press.

Table 0.1 Transforming characteristics of data

Prevailing Characteristics

New Characteristics

• Episodic: generated through discrete events (e.g., every time a product—say, a mattress—is sold)

• Interactive: generated through ongoing interactions (e.g., continuous streaming of heart rates and breathing patterns to assess quality of sleep from sensors in the mattress) • Stored to create individual profiles (e.g., how restfully an individual sleeps over time) • Value extraction from both real-­ time aspects of interactive data and stored data (e.g., improving rest as user sleeps using real-time data and understanding sleep patterns through analysis of archived data)

• Stored in aggregate form (e.g., aggregate revenues from different mattress types, retail channels, or geographies) • Value extraction mostly from after-the-fact analysis of stored data (e.g., why sales are up or down for a particular mattress model, in a particular retail channel or geography)

on. Such data can be merged with a firm’s traditional databases and with alternative sources of data such as social media. A host of other advances in technology further elevate what firms can do with such emerging pools of data and by combining real-time and accumulated after-the-fact data. The latest cloud technologies allow firms to maintain vast repositories of profiles and ongoing real-­ time data sourcing for each sensing unit. Technologies such as artificial intelligence (AI), machine learning, and data analytics further amplify insight-building processes for each profile. 10 Firms can also share select facets of real-time data across various connected assets linked through the IoT. With connected parking lots, for example, Ford can, with the driver’s permission, share a car’s location data to guide a driver to an empty parking spot. Moreover, while sensing units communicate with one another with real-time data, their communications can be shaped based on intelligence garnered through its accumulated data. Babolat can use its accumulated data on a tennis player’s skill level acquired from its users’ connected tennis rackets to match the player to other players with similar skills or appropriate coaches. Estimates range from