When should we perform maintenance on our equipment? How much stock should we purchase of this item? Will this project stay within budget? These are a few of the many questions which can be addressed with predictive analytics. However, the question of when to get started with predictive analytics might be difficult to answer, as it has many possible applications and it could be unknown territory for your organisation. The intention of this blog post is to give some support in answering this question.
Getting started with predictive analytics does not mean that a large scale project has to be initiated right away. It is recommended to start on a smaller scale, with a pilot. This has several advantages:
The costs and benefits of the implementation can be estimated more accurately
The preconditions can be properly verified
For a relatively low cost, the organisation can get a first impression of predictive analytics
During the pilot all required data will be collected and some first data exploration is performed. Among others, it is tested whether the expected business logic is found in the data. For the ice cream truck example, this would mean that sales are higher when it’s 25 degrees celsius compared to 15 degrees celsius. If this is not the case, the cause should be found and solved, before the actual predictive analytics are performed. It could be that there are insufficient observations or that relevant variables are missing.
Applications of predictive analytics are available in practically every organisation. Before getting started, it should be checked whether the preconditions are met. Thereafter a business case is made to consider the return on investing in predictive analytics. This is followed by a pilot, which might lead to an implementation. Are you curious to find out whether predictive analytics can be of value for your organisation? Then feel free to reach out to us, and we can set up a call to discuss your specific situation.
Jelle co-founded Cadran Analytics. He spends most of his time on sales and project management. In addition, he can often be found coding in R.