Publications
Publications
- 2022
- HBS Working Paper Series
Data Architecture, Machine Learning and Firm Productivity
By: Sam (Ruiqing) Cao and Marco Iansiti
Abstract
As enterprise IT systems increasingly incorporate machine learning technology, it is crucial to
understand complementary organizational practices that allow firms to unleash productivity benefits from
adoption. This study examines the relationship between machine learning adoption and data architecture
capabilities using detailed corporation-level data. We utilize a survey to measure two clusters of data
architecture practices--data fabric and ML infrastructure, for 82 large corporations. We use data on legacy
systems and software varieties to produce out-of-sample predictions of data architecture capabilities for an
additional 143 large corporations on the Fortune 500 list. We find that corporations with higher data fabric
capability adopt machine learning more intensively. A one-standard-deviation increase in machine learning
adoption is associated with a 1.5% increase in productivity among corporations with above-median data
fabric capability. However, machine learning adoption can negatively affect firm productivity without a
sufficiently developed data fabric. These results suggest that data fabric capability is complementary to
machine learning adoption. Data fabric facilitates more coordinated and less fragmented adoption of ML
software products across the organization because it allows organizations to integrate large data streams
across diverse sources and locations.
Keywords
Organizations; Information Technology; Performance Productivity; Growth and Development; Transformation
Citation
Cao, Sam (Ruiqing), and Marco Iansiti. "Data Architecture, Machine Learning and Firm Productivity." Harvard Business School Working Paper, No. 21-122, May 2021. (Revised May 2022.)