Usually when you talk to people about data, first thing that comes up is the size, and not all the people that I talk to are males.
The amount of data is important, but the key for any data analytics project starts by asking the right questions, a stage that many are neglecting.
The definition stage can be very valuable. If you take the relevant stakeholders you will eventually create a uniform language in the organization.
A language that will define the business questions that your organization is facing with, in a way that can be answered using data.
Expect getting insights and better understanding of your business once this stage is completed. What you can and can’t do, and what is important to others in your organization.
No matter if you already established a solid BI (business intelligent) solution in your company, you still need to face the definition stage.
Having a reliable BI solution can help you speedup the 2nd phase of the predictive modeling process, the DATA and ETL (will not cover here).
Froude, Credit risk, Targeting, lifetime value, Churn and many other models are being used across different industries, but the definition of each one is different in various industries.
How can you build a Lifetime value model if your mobile app doesn’t generate any income?
What is the best “Churn” definition to your business?
How can you optimize your targeting efforts? What is it that you would like to optimize?
When you model your business questions, keep that in mind:
- What is the entity that I try to describe?
- What is my target?
- What is my prediction timeframe?
Business question modeling is not always an easy task. Adjusting and adopting a “predictive thinking” strategy will help you leverage your data and gain the competitive advantage that everybody is talking about.
