How to start applying R in Enterprise. Practical example
Why is this question relevant?
Business cases are different, the technical essence is the same
- Call Center Performance Analytics
- Sales analytics, including forecasts
- Antifraud system
- Business process mining
- Various audits (technical, financial)
- Warehousing and logistics tasks
- Activity-based costing
- Business process monitoring
- Log-based analytics
- Capacity management
- Text analytics (e-mail, service-desk)
- Flexible dashboards and reports
- "smart tires" between accounting systems (1C, ACS, SAP, ...) and executive
It is a continuation of previous publications .
- a lot of such tasks come down to mathematical manipulation of data (CRUD systems are beyond the scope, we consider precisely various processing and transformation);
- 80% of data manipulation tasks can be quickly and efficiently solved "turnkey" by using the R tools;
- in business, as a rule, tasks and requirements are quickly adjusted, incl. due to external factors or intermediate results obtained;
- "modular" technologies take root well in IT; the construction of the "monolith" may take 2-3 years, which is comparable to the life of a small solution. It is much more efficient to quickly assemble a “modular” design, accumulate practical experience, and after 2-3 years build a new solution taking into account the knowledge gained and past changes in IT and business.
Typical “urban legends” about R
- R slow
- R hard to read
- R is for stat. calculations by complex algorithms
- R is designed for interactive use.
All this arises from a superficial study of the topic and the tools used.
City legends - misconceptions from the 90s
- R is a complete programming language, not a console calculator.
- R acts well as a universal “glue” between various platforms and C components - it counts quickly!
- The readability of the code depends on the experience of the developer. The modern style of R is metaprogramming. The code is compact and fast.
- R is an ecosystem that allows you to implement a complete data processing cycle from importing data to providing AWPs and preparing presentations.
Previous publication - "Using the computing power of R to test the hypothesis of equality of means . "