Data Science projects are complicated in nature and are always prone to be unfeasible. But there are many ways to quickly act on the same in a sandbox environment. This lets for a fail fast approach and lets you allocate the resources in a sustainable way towards those projects which may create value, via the optimization of processes, increasing customer loyalty and enabling new services. A POC or proof of concept is a famous way for businesses to analyse the viability of a system, service or product to make sure the specific needs are met. POCs also represent a bigger value of a system, making sure that it is aligned with taking the longer-term strategic objectives of the company forward. If you want to run a Data Science POC effectively, here are the suggested by data analytics services company:

  1. Concrete use case

Without a use case, a POC cannot exist. For the same, you need to begin with a list of important business issues like gathering feedback and ideas from teams in the company.

Then address them and determine:

  • What is the existing process?
  • Would the usage of data be helpful in addressing the business issue, if yes, how?
  • Can this data be used for POC?
  • Where the data is preserved and how it is accessed?
  • Can this use case be worked with an external partner?
  • Can it help in saving or making money?
  • Reasonable deadline

2 months are sufficient for POC as it lets you properly evaluate without wasting any time. When it comes to small and medium sized companies, you can go with a use case that can be completed in that time period. You may want modest goals, addressing the most complicated problems with the most significant effects. When it comes to larger companies, this may not be possible. You may want to run the project separately.

  1. Clear deliverables

Certainly, the main factor to assign a deadline to a POC is the existence of clear deliverables. Without them, the process may unnecessarily elongate and there is no way to declare success. The final deliverable is integrating the data project based on the chosen use case into the production. By installing deliverables along the way and for the teams for evaluating their subset of the use case for keeping the POC moving forward. For more insight on big data analytics services, visit the website.