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To Make Better Decisions, Combine Datasets
by Rita McGrath
A complicated system is somewhat like a complicated recipe.
You know what the outcome will be because you understand what will cause what — combine a given
number of ingredients together in a certain way, put them in the oven, and the results will be consistent as
long as you repeat the same procedure each time.
In a complex system, however, elements can potentially interact in different ways each time because they
are interdependent. Take the airline control system—the outcomes it delivers vary tremendously by
weather, equipment availability, time of day, and so on.
So being able to predict how increasingly complex systems (as opposed to merely complicated systems)
interact with each other is an alluring premise. Predictive analytics increasingly allow us to expand the
range of interrelationships we can understand. This in turn gives us a better vantage point into the
behavior of the whole system, in turn enabling better strategic decision-making.
This idea is not new, of course. Firms have been developing models that predict how their customers will
behave for years. Companies have developed models that indicate which customers are likely to defect,
what advertising pitches they will respond to, how likely a debtor is to default (and what can be done to
avoid making loans to that person), what will prompt donors to up the ante on their giving, and even who
is likely to pay more for services like car insurance. Organizations such as Blue Cross Blue Shield have
used their considerable databases about chronically ill people to target and influence their care, reducing
to some extent the total cost of care, much of which is concentrated in helping a small portion of their total
consumer base.

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What is new is that the advent of predictive analytics, in which disparate information that was never
before considered as or looked at as related parts of a system, is giving us new ways to see
interrelationships across, and think comprehensively about, entire systems. Rather than arguing about
what various kinds of activities will drive which outcomes, the questions can now be answered
quantitatively. Indeed, as I argued in Harvard Business Review, complex systems with their continually
changing interrelationships often defy understanding by using conventional means. This in turn creates
the opportunity for strategic action.
An example of exactly this kind of action caught my eye in an unlikely setting—city government. New
York City Comptroller Scott Stringer, in an effort to help the city reduce its considerable cost of defending
against and paying out legal claims made against the city, has turned to predictive analytics to help. The
program is called ClaimStat and is modelled after Richard Bratton’s famous CompStat program of
collecting crime data. The system tracks the incidences that led to the city’s paying out $674 million in
payments for claims. Stringer’s website observes that “These costs are projected to rise over the next
four years to $782 million by FY 2018, a figure that is greater than the FY 2015 budget for the Parks
Department, Department of Aging, and New York Public Library combined.”
Using analytics, the city found a non-obvious systemic relationship—one where the dots may never have
been connected otherwise—with costly unintended consequences: In fiscal year 2010, the budget
allocated to the Parks and Recreation department for tree pruning was sharply reduced. Following the
budget reductions, tree-related injury claims soared, as the Comptroller reports, leading to several multi-
million dollar settlements with the public. One settlement actually cost more than the Department’s entire
budget for tree pruning contracts over a three-year period! Once funding was restored for tree-pruning,
claims dropped significantly. Such a relationship might never have been spotted absent the connected
database, as the budget for Parks and the budget for lawsuits are managed as separate and unrelated
resources. By bringing them together, the system-wide consequences of individual decisions becomes
obvious and something that can be tackled in a strategic way.
In the coming years, we can expect to see smart organizations increasingly leveraging the power of
multiple databases to get a real vantage point on their strategic challenges.