Improve your data science ROI with a portfolio and risk management lens

While data science value is well recognized within tech, experience across industries shows that the ability to realize and measure business impact is not universal. A core issue is that data science programs face unique risks many leaders arent trained to hedge against. Brian Dalessandro addresses these risks and advocates for new ways to think about and manage data science programs.

Building a data science capability requires tremendous investment in people, processes, and technologies. While the return on such investments can be large, such returns are not immediately observable or even measurable. Such issues often leave business executives in the position of making data science investments more on faith than on evidence. As the data science industry matures, both data science leaders and their executive stakeholders need to develop robust and better frameworks to deliver business impacts and justify spending levels. Brian Dalessandro identifies multiple levels of systemic risk that data science projects face and introduces a portfolio-based approach to hedge against such risks and thus increase the likelihood of creating value through data science. Most business projects face both hypothesis and execution risks, where the former is defined by the business’s ability to identify the right opportunities, and the latter by the ability to execute well to capitalize on a such opportunities. These risks extend to data science projects, but within this domain is another, unique risk, which is called signal risk. Signal risk is the underlying uncertainty in being able to acquire the right data related to a problem and then finding exploitable patterns within it (analogous to the concept of statistical power but extended to broader business concerns). All together, standard business and product development processes fail to incorporate the compounded effects of these three risks. Brian addresses the issue and presents concrete strategies to better hedge against such risks to improve the success rate of data science programs. You’ll learn techniques and strategies that help mitigate the aforementioned risks, learned through years of consulting and data science leadership. Ultimately, the right approach requires negotiation and engagement from all stakeholders, and Brian provides a starting basis to have such a conversation.