- Descriptive
- Predictive
- Prescriptive
When we think about answering the question of what an organization should do, that brings us into the domain of prescriptive analytics. Let’s take a look at what the world of prescriptive analytics is all about and how it can benefit your operation.
Prescriptive Analytics Compared to Other Methods
One way to understand what a prescriptive approach actually involves comparing it to other forms of business analytics. Descriptive analytics helps us get a handle on what a problem looks like now and what it looked like in the past. In particular, it generally does not attempt to address questions about causal relationships. The goal, instead, is simply to lay the bag of snakes out straight.
For example, historical economic analysis is a form of descriptive analytics. An economist looking back at data regarding the Tulip Mania during the 1600s probably isn’t trying to create a model for how bubbles form in the modern economy.
The world of predictive analytics is at the opposite end of the scale. Researchers there are trying to examine current data and trends in order to determine where things will land in the future. For example, a report on the global impact of climate change might be intended to just figure out what the heck is on the horizon.
Prescriptive Analytics
Prescriptive analytics cuts to the core of three questions:
- What can we do?
- What should we do?
- What might others do?
The oil and gas industries are big spenders on prescriptive analytics. Exploring regions for oil, for example, opens up questions that go well beyond what descriptive or predictive analytics can do. An oil company does need to take a descriptive view of a deposit, and it does need to predict things like global demand and supply trends. When drilling a well, an oil company has to prescribe solutions to problems like:
- Boring through rock
- Fluid and gas pressures in deposits
- Where to position rigs
- How many workers to assign to projects
These kinds of business analytics are meant to assess risks, exploit opportunities and maximize returns. A state government might, for example, want to know at what grade level it should spend the most money to ensure that economically disadvantaged kids can get ahead. To do this, they have to figure out where the risks to those kids arise and what opportunities aren’t being presently exploited.
Conclusion
For many organizations, prescriptive analytics projects represent their goals. Decision-makers are empowered to take action when prescriptions are grounded in hard data. Rather than producing tons of data that just goes into spreadsheets and databases never to be read, organizations can convert massive amounts of information into answers to pressing questions.
