Forging the Path to Engineered Decision Intelligence
Our monthly panel of female data and analytics leaders discusses how to get started with engineered decision intelligence
And, though the weight of reasons cannot be taken with the precision of algebraic quantities, yet, when each is thus considered, separately and comparatively, and the whole matter lies before me, I think I can judge better, and am less liable to make a rash step; and in fact, I have found great advantage for this kind of caution, in what may be called moral or prudential algebra.Benjamin Franklin, Letter to Joseph Priestly, September 19, 1772
The science behind decision making has been developing for many years. Indeed, ‘decision science’ as a discipline has been around at least since the 1970s, and arguably far longer.
Today, enterprise businesses are investing significant time and resources into formalizing their approach to data-driven decision making with engineered decision intelligence.
This is an old problem that we are trying to resolve,” says RCS Bank Head of Data Maritza Curry. “And the problem is to support decision making across the business value chain, and to support causal decision making.”
In this LinkedIn live panel, three of our leading female data and analytics executives discuss their progress and share their tips on getting started with engineered decision intelligence.
Augmenting the Power of Human Decision-Making
For a physician making healthcare decisions daily, accurate insights available quickly and at scale could make the difference between life and death.
However, engineered decision intelligence is not designed to replace human decision-making, as Besa Bauta, Chief Data Officer at MercyFirst, explains. Rather, it accentuates it.
“It’s getting a lot of information for a patient and client in a way that highlights the risks for that patient and allowing the physician to glance at that information and take that information into their decision-making process,” Bauta says.
She continues: “[It’s not about] replacing thought and human cognition. It’s augmenting that in a way that raises awareness of additional things to look at but not completely replaces the human in the chain.”
Outside of the healthcare sector, data and analytics executives are looking to engineered decision intelligence to help improve business decision-making.
“It’s really fascinating. It makes you observe your own decision-making abilities inside your organization and try to figure out how to layer in the science to that,” says Mastercard Chief Data Officer JoAnn Stonier. “So, I do think it’s going to generate more insights as well as more consistency in our data practices.”
Establishing a Proof of Concept
For many large organizations, engineered decision intelligence is still in the early stages of implementation. When establishing a proof of concept, our panel recommends caution when selecting the initial use case.
“Simplicity and focus are key, there is definitely the opportunity here to over-engineer,” notes Curry.
“Be very careful about the use case you choose and make sure that the use case is linked to your organization’s core capability,” she continues. “Because the outcome is important. Understand what that outcome’s going to be and what the decisions across the value chain that are going to give you that specific outcome.”
“Make sure that you can map out a good value chain of the decisions first manually so that you understand what you’re going after,” adds Stonier. [Then] you can understand what the data points that you’re going to need are before you begin to kind of take on the project.”
- Engineered decision intelligence is a nascent discipline. It has great potential to help organizations rethink how they optimize decision-making.
- It’s not designed to replace humans. However, it can help to augment decisions made by humans
- Pick a use case carefully. Making sure it is tied to the core capabilities of the business will help to make the proof of concept a success.