Data science consulting is the process of influencing change through improving a client’s analytical abilities, establishing competencies, and comprehending the inner workings of their company. Though both data science consulting and ordinary consulting are concerned with making data-driven choices, the critical distinction is that data science consultants provide their customers with reusable operational models. On the other hand, most routine consulting assignments solve significant but one-time concerns and do not provide customers with operational decision-making models.
The strategy portion of the consulting is investigating what can be done with data and developing a strategy.
This section necessitates a thorough understanding of the use cases. Depending on the client's industry, the data gathering process, regulation, and objectives might all be quite varied.
The validation stage is required to ensure that the identified approach is correct. While developing a strategy
may be done in a matter of hours in an emergency, putting it into action might take months. As a result, it's critical to test the plan.
Designing and developing a modern data product or internal tool is referred to as development.
This is more along the lines of data science consulting's IT side. The creation of custom-tailored solutions for unique situations necessitates a strong focus on the development process.
The client's team's data literacy is being improved through training. This would ensure that the rest of the team
is aware of the process and can contribute to system improvement. This would also ensure that the team would capture the key points and contribute meaningfully to the process's continual development.
(Consulting, Building Algorithms, Defining Data Processes)
(Infrastructure Configuration, Monitoring and Maintenance)
Batch Data Processing / Real Time Data Processing
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