In Business, data collection is everywhere. Today we have an information goldrush. However, the gold isn’t in the data itself. Instead, the gold is in the knowledge of things that haven’t yet happened. According to Eric Seigel, we live in a predictive society and the best way to prosper in it is to understand the objectives, techniques, and limits of predictive models.
Prediction is power. Predictive analysis is the process by which an organization learns from the experience of all its team members and computer systems. It’s about forward-thinking from the experience about what has previously happened. This gives rise to the Prediction Effect. Which basically says that as long as the predictions are better than guessing, predictive analytics is believable.\
Predictive Analytics has two parts:
- What’s predicted: the kind of behavior (i.e., action, event, or happening) to predict for each individual, stock, or another kind of element.
- What’s done about it: The decisions are driven by prediction; the action taken by the organization in response to or informed by each prediction.
A model is then created based on this. The higher the score a prediction is given, the more likely it is that someone will take that action. The score is then used to guide an organization or business’ decision.
The Ethics of PA
Data’s value is the very thing that also makes it sensitive. The more data, the more power. The more powerful, the more sensitive. There are things we must decide in order to reap the benefits of predictive analytics as well as avoid being unethical or other malcontents. These things include:
- What is stored and for how long?
- Which employees, types of personnel, or group members may retrieve and look at which data elements.
- What data may be disseminated to which parties within the organization, and to what external organizations.
- What data elements may be brought together, aggregated, or connected.
- How may each data element be acted upon, determining an organization’s response or other behavior?
The Data Effect
The Data Effect says that data is always predictive. However, keep in mind that correlation does not imply causation. But when utilizing predictive analysis, we don’t necessarily need to know causation. We want to predict things rather than explain why they happen.
It’s not what you ask, it’s how you react
Often, an organization needs to decide what next action to take. It doesn’t just want to predict what individuals will do, it wants to know what to do about it.
Think about the following example. Your cell phone provider knows your contract is about to expire so they send you a brochure with their latest offerings. This is a mistake. The company just reminded you that your contracted commitment is ending and you’re free to defect. The cell phone provider hopes you renew your contract but you have been given the opportunity to look at alternatives. Here is what the cell phone provider needs to analyze:
What’s predicted: How will customers react to the reminder brochure.
What’s done about it: Retention efforts target at-risk customers.
However, because we can’t both send a brochure and not send a brochure, we need a different model. Seileg calls this an Uplift Model: A predictive model that predicts the influence on an individual’s behavior from applying one treatment over another.
As Siegel states, millions of people, companies, and governments make operational decisions in order to provide a product or a service. Prediction is the key to guiding these decisions. Predictive analysis is the means with which to improve the efficiency of these operations.