In the decade (or two) I have spent in Decision Management, and Artificial Intelligence at large, I have seen first-hand the war raging between knowledge engineers and data scientists. Each defending its approach to supporting ultimately better decisions. So what is more valuable
In the decade (or two) I have spent in Decision Management, and Artificial Intelligence at large, I have seen first-hand the war raging between knowledge engineers and data scientists. Each defending its approach to supporting ultimately better decisions. So what is more valuable? Insight from data? Or knowledge from the expert?
Mike Loukides wrote a fantastic article called “The unreasonable necessity of subject experts“ on the O’Reilly radar, that illustrates this point very well and provides a clear picture as to why and how we would want both.
Data knows stuff that experts don’t
In the world of uncertainty that surrounds us, experts can’t compete with the sophisticated algorithms we have refined over the years. Their computational capabilities goes way above and beyond the ability of the human brain. Algorithms can crunch data in relatively little time and uncover correlations that did not suspect.
Adding to Mike’s numerous example, the typical diaper shopping use case comes to mind. Retail transaction analysis uncovered that buyers of diapers at night were very likely to buy beer as well. The rationale is that husbands help the new mom with shopping, when diapers run low at the most inconvenient time of the day: inevitably at night. The new dad wandering in the grocery store at night ends up getting “his” own supplies: beer.
Mike warns against the pitfalls of data preparation. A hidden bias can surface in a big way in data samples, whether it over-emphasizes some trends or cleans up traces of unwanted behavior. If your data is not clean and unbiased, value of the data insight becomes doubtful. Skilled data scientists work hard to remove as much bias as they can from the data sample they work on, uncovering valuable correlations.
Data knows too much?
When algorithms find expected correlations, like Mike’s example of pregnant women being interested in baby products, analytics can validate intuition and confirm fact we knew.
When algorithms find unexpected correlations, things become interesting! With insight that is “not so obvious”, you are at an advantage to market more targeted messages. Marketing campaigns can yield much better results than “shooting darts in the dark”.
Mike raises an important set of issues: Can we trust the correlation? How to interpret the correlation?
Mike’s article includes many more examples. There are tons of football statistics that we smile about during the Super Bowl. Business Insider posted some even more incredible examples such as:
- People who dislike licorice are more likely to understand HTML
- People who like scooped ice cream are more likely to enjoy roller coasters than those that prefer soft serve ice cream
- People who have never ridden a motorcycle are less likely to be multilingual
- People who can’t type without looking at the keyboard are more likely to prefer thin-crust pizza to deep-dish
There may be some interesting tidbit of insight in there that you could leverage. but unless you *understand* the correlation, you may be misled by your data and make some premature conclusions.
Expert shines at understanding
Mike makes a compelling argument that the role of the expert is to interpret the data insight and sort through the red herrings.
This illustrates very well what we have seen in the Decision Management industry with the increased interplay between the “factual” insight and the “logic” that leverages that insight. Capturing expert-driven business rules is a good thing. Extracting data insight is a good thing. But the real value is in combining them. I think the interplay is much more intimate than purely throwing the insight on the other side of the fence. You need to ask the right questions as you are building your decisioning logic, and use the available data samples to infer, validate or refine your assumptions.
As Mike concludes, the value resides in the conversation that is raised by experts on top of data. Being able to bring those to light, and enable further conversations, is how we will be able to understand and improve our systems.
