Fast Data Science data strategy five step process: business requirements, data exploration, problem scoping and prioritisation, analytics and strategy report
The five step process of data analytics at Fast Data Science: business requirements, data exploration, problem scoping and prioritisation, analytics and strategy report

You may not need a machine learning model developed at all. In fact many projects start with a simple analysis of what is going on in your business. Often as a data scientist consultant brought in from outside, Fast Data Science has the benefit of fresh insight.

Fast Data Science normally offers data analytics consulting as a five step process:

  • Business requirements: in a series of initial stakeholder discussions we can begin to understand the domain and identify the potential for ROI.
  • Data exploration: after the necessary steps to obtain data access we can begin to explore the data to identify where we can extract value.
  • Problem scoping and prioritisation: again in discussions with the stakeholder we can begin to define exactly what we want to achieve.
  • Analytics: we run analytics on the data, which may consist of traditional statistics or machine learning models, in close collaboration with the domain expert in your organisation
  • Strategic report: at the end of the project before sign off we present findings to stakeholders and ideally executives in the organisation, which include recommendations for further action.

Given a small amount of known data on your customers, we can produce a report analysing their demographic profile, estimated lifetime value, a cohort analysis, and other useful insights. For example we may be able to spot if there is a segment of your target market which is under-engaged.

If you would like to use Fast Data Science’s services for data analytics please contact us.