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
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
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!
- 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),
- 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).
- 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.
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