I will be speaking as part of a moderated panel on big data at the Licensing Executive Society (LES) annual meeting in Boston next month and am thinking about our upcoming discussion.
As it will be a sophisticated but not deeply technical (in matters of data science and mathematics) audience, the focus of the discussion will be less around implementation questions and more around considerations regarding how to best apply our evolving understanding of the role of big data in business, innovation, and collaborative projects.
Here are some of the questions and topics that we’ll be talking about in detail:
- Who are the key stakeholders in big data? Who and in what industries / sectors are affected? Even with all the hype around big data, AI and machine learning, there are still plenty of people especially in less hyped sectors that are resisting thinking about how big data will ultimately affect their areas of expertise or even their lives… In the long run, the answer will be… everyone is affected! Either by being involved in data creation (willingly or not) or by having data applied “to them.” This is no longer just the domain of mathematicians and computer scientists. The real question isn’t who is or isn’t affected; rather, we need to be discussing how we ensure that the generation and the usage of data happen in a way that makes it accessible (and fair) to people of all backgrounds.
- What impact does this diversity of stakeholders have on projects, and on how data are generated, analyzed, and used? Since “everyone” who is involved in projects, especially collaborative ones, will be part of generation and use of data… what does that mean for how we think about data?
- On the one hand, without some structure and forethought, chaos can quickly ensue. This is a bit like thinking that brainstorming is best done without any guidance or structure whatsoever, whereas in reality, creativity happens best when there is a combination of structure and free-flowing ideas. How do we apply this in the – we hope creative – generation and application of data?
- At the same time, how do we think about future proofing our system? No one person/team of data analysts or strategists will know what future directions the intelligence of “everyone” will move in and what will become important and useful data. Unstructured data and the flexibility to do new and unique things with it are increasingly important as we bring in more and more diverse stakeholders – how do we handle that delicate balance?
- How has the innovation cycle for big data evolved? What is the impact on business models? If we think about the 3-S model of the innovation cycle proposed by Lee*, we can consider how big data has impacted the Substitution, Scale, and Structural Transformation stages of innovation. How do we proactively move from substitution to transformation, to gain the most value from data? Who actually gains from this value, and how do we ensure greater inclusivity in benefiting from this value creation, especially in transformational stages?
- We might also think about big data in non-commercial activities, how do we think about data transforming business models in this context?
- What role does user generated big data play in the overall innovation cycle? Here, we really need to think about true value creation, not just value extraction, particularly in light of the huge range of diversity of stakeholders. This discussion must include ethical implications to consider (privacy, ownership, security, bias, benefits, etc.) – and is a big enough topic that I’ll write about it separately.
- For those of us who are designing new products and technologies (rather than dealing with legacy systems), what are some of the big data considerations that we can and maybe should bake into the early design process (of hardware, software, workflows, etc.)? I.e., how do we make data not an afterthought, for example in the way that cyber-security is all too often, but an integral part of how and what we design?
- This includes questions such as, how do we think about bias early on in our algorithm design, especially considering the diversity of stakeholders? What techniques do we have for minimizing it? Since eliminating it entirely is not possible, we also need a discussion of minimizing the impact of bias, since its presence will be a reality.
- Again, a big topic that I’ll address separately, especially in the context of product design.
If these topics are of interest to you, and you are in the Boston area, please consider attending this event. I also welcome hearing from others with thoughts on these topics, so please feel free to email me with your thoughts and comments, either via this website or my LinkedIn page.
*Lee, “Big Data and the Innovation Cycle”, Production and Operations Management, 2018.