The Four Strategic Resources of Applied AI: IT Infrastructure

A Strategic Management Framework for Leveraging Artificial Intelligence - Part 3

On what resources do management strategists need to focus, if they want to leverage Artificial Intelligence and create Competitive Advantage? To provide an answer from business academia, a framework for leveraging Artificial Intelligence in a business context is presented in this 5-part article series. In Part 3, the relevance of IT infrastructure is elaborated, especially for Big Data. The first part of this series can be found here.

Big Data requires new infrastructure

The second physical capital resource needed for AI is a suitable IT infrastructure, which enables businesses to deal with the data. Especially Big Data requires new technologies and architectures, which can deal with the high Volume, Velocity and Variety in a reasonably efficient manner.

Four strategies to deal with Big Data

Ebner et al. (2014) present four different strategies for IT infrastructure to handle Big Data, along with their strengths and weaknesses:

1. Relational Database Management Systems

First, traditional Relational Database Management Systems (RDBMS) can be used. Parallel RDBMS can handle and analyze large Volumes of data in a very fast and stable manner, but they struggle with Velocity and Variety, since loading data is very time-consuming. Therefore, a RDBMS-based strategy can only be suitable for Big Data, if new data is not frequently loaded and mostly from a structured nature.

2. MapReduce & Distributed File Systems

On the other hand, the second strategy based on a MapReduce engine (like Hadoop) and Distributed File Systems (DFS) is somewhat complementary to the first. It can cope with large Variety and Velocity of Big Data since it enables fast loading and analysis of unstructured data, but standard processing tasks like Select or Join are far slower. Also, ad-hoc queries are difficult because writing a MapReduce program takes significantly longer than a SQL query. Therefore, the MapReduce strategy is favorable, when large and unstructured datasets need to be loaded frequently with unchanging query patterns.

3. Hybrid Approach: RDBMS + MapReduce + DFS

The third strategy is a combination of RDBMS, MapReduce and DFS. However, in practice, this hybrid strategy does not surpass the performance of specialized architectures, but rather limits respective weaknesses within acceptable thresholds.

4. Big Data Analytics as a Service

Finally, a company can pursue the fourth strategy: Big Data Analytics as a Service (BDAaaS). While hosting the infrastructure via cloud technology is the most cost- and resource-efficient alternative, data privacy and security concerns remain as the highest risks. Therefore, Ebner et al. recommend this strategy particularly to small organizations with limited resources for which BDA capabilities are not strategically important.

IT infrastructure as a source for Competitive Advantage?

Already in 1995, Mata et al. claimed that IT infrastructure is becoming increasingly generic and available to most firms, which is why it cannot be source of Sustained Competitive Advantage. In fact, Bhatt and Grover (2005) showed, that higher quality of IT infrastructure has no significant effect on Competitive Advantage.

Even today, in the age of AI, this might not have changed. Brynjolfsson&McAfee (2017) make the point that necessary hardware for modern AI can be bought or rented as needed, and companies who want to experiment with Machine Learning can do it in an increasingly cost-efficient manner.

In the forth part of this 5-part series, the relevance of skilled labor is elaborated, especially the role of Data Scientists. Stay tuned!

Show Sources
  • Bhatt, G. D., & Grover, V. (2005): Types of Information Technology Capabilities and Their Role in Competitive Advantage: An Empirical Study. Journal of Management Information Systems, 22(2), 253–277.
  • Brynjolfsson, E., & McAfee, A. (2017): The Business of Artificial Intelligence. Retrieved from Harvard Business Review website: hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence on 07/06/2018.
  • Ebner, K., Bühnen, T., & Urbach, N. (2014): Think Big with Big Data: Identifying Suitable Big Data Strategies in Corporate Environments. In HICSS (Ed.), Proceedings of the 47th Hawaii International Conference on System Sciences. 6-9 January 2014, Waikoloa, Hawaii (pp. 3748–3757). IEEE.
  • Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995): Information Technology and Sustained Competitive Advantage: A Resource-Based Analysis. MIS Quarterly, 19(4), 487–505.

About the author

Keesiu Wong

Co-Founder Design AI
Keesiu Wong is Co-Founder & CEO of Design AI, a start-up focusing on agile AI development and use case identification through Design Thinking. He is a trained Data Scientist with an academic background in Mathematics, Management and Data Engineering at the Technical University of Munich. In addition to 5+ years of experience with AI projects, he has entrepreneurial experience in 4 start-ups, as well as experience in Design Thinking, top management consulting and as a start-up coach at UnternehmerTUM.