Back in July 2020, I examined c’s PhD thesis, looking at how incumbent firms can leverage big data. This PhD was really pleasant to read and easy to examine. So, it was with some pleasure that, last week, I came across the paper “Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms”, co-authored by Brewis and her supervisors, Sally Dibb and Maureen Meadows, and based on that PhD work.
Drawing on dynamic capabilities as a theoretical framework, and multiple case studies as a methodology, the paper (and the thesis) examined, specifically, the managerial capabilities that enable managers to spot opportunities, and put together the resources and make the decisions to materialise them. As the authors write, in the paper’s opening paragraph:
The global uptake of new technologies, ranging from mobile computing devices to social media platforms and artificial intelligence, is digitally transforming organisations’ operating environments. These technologies are underpinned by digitised big data, with the proliferation of data and big data analytics (BDA) bringing considerable opportunities to build market knowledge, identify target markets and gain strategic marketing insights. The strategic use of big data has been shown to improve organisations’ outputs, productivity levels, performance and earnings’ growth. However, with fewer than half of UK and US firms treating data as a business asset, and many incumbent firms failing to exploit the benefits of this new resource, there are costly implications for profitability and competitiveness. Low levels of engagement with big data are often due to the challenges organisations face in using and generating value from this resource. For example, acquiring and embedding the necessary specialist expertise to process and analyse big data can be highly disruptive to organisations’ existing internal systems and strategic choices.
Their research identified 5 distinct capabilities (section 5.1):
- The capability to engage with big data, which alerts managers to new trends and opportunities, and stimulates business transformation;
- The capability to adopt techniques that balance the exploitation of existing resources with the exploration of new data resources, thereby straddling legacy and technology;
- The capability to identify novel solutions and build partnerships to address the data-related capability gaps;
- The capability to apply technological thinking to assimilate big data, aligning the data with the organisation’s strategy, and converting it into an asset that adds value;
- The capability to emulate digital ventures in their agile response to market changes and new information.
While the thesis was focused on big data, I do think that these capabilities are relevant for other technological contexts. For instance, the issue of skills was very much mentioned in the adoption of AI paper that I co-authored with Ben Keegan and Dorothy Yen. Moreover, generative AI technology, like ChatGPT, certainly creates a case of “opposing business models” in many sectors.
For me, though, the main challenge is, actually, in sorting the wheat from the chaff. Brewis and her co-authors talk about the importance of agility, such as using experimental teams and fast fail techniques. And I agree that that is helpful. But it does require a culture that rewards people for trying, rather than necessarily succeeding… but which does not tip into “breaking things”, either. And that, I think, is easier said than done.
Do you have good experiences – or suggestions – for turning disruptive technological promises (e.g., Generative AI) into reality? Moving fast but without breaking things?
Ana Canhoto 