In the era of management with information, understanding the strategic significance of data processing is vital. Because the final goal of data use is to maximize profits, leanness and cost-efficiency is needed. The question is how the collected data is used to maximize the profits.
When aiming for leanness, matters that generate expenses by causing delays, or, to quote Lean, adding “waste” to the process, must be identified. The aim of lean IT should be a situation where things can be tested quickly using the fail fast model. The starting point for lean operations is an IT pricing model for the service provider that is genuinely flexible and encourages customers toward lean operations.
A cost-efficient IT architecture should allow for at least the following:
- Scalability of the resource planning for work volumes
- Extensive catalogue instance and saving resource options
- Horizontal scalability
- Customer-friendly pricing
- Efficient management and optimization of resources
When the above list is compared to the traditional on-premises model, it becomes evident that, in the traditional model, fixed hidden costs are everywhere: Servers are on 24/7. Online architecture has not been optimized based on actual need, as the infrastructure has been designed based on maximum need. There is always free storage capacity. These hidden costs can be managed through cloud services that allow optimization of leanness and cost-efficiency extremely efficiently in comparison to the on-premises model.
Once the modernization of the IT architecture has been started, the next step is to prepare a data strategy for the company. One of the main tasks of information management is to serve business operations, which must always be considered the starting point of data strategy planning.
What actually happens to data that is saved in a cloud service? Where are individual parts of the data stored physically? Which data is made part of the value chain that serves business operations, and how? Are there any concrete sample cases? These questions illustrate that each company has their unique data strategy. A good data strategy answers the above questions and also takes the company closer to the maximization of profits.