A Data Strategy: Why?

 "If you want to use data, you must always start with a data strategy" 

(Bernard Marr)

A data strategy is a synonym of having a plan and a specific goal(s) to be achieved in order to create business value and take advantage (more efficiency, more right decisions...). It usually starts with the identification of the strengths and weaknesses that already exist in our data (or in the data environment of a company). 

For a better idea of the context of a data strategy in a company, the following schema (from cc-cdq ) shows the place of it in a company and its relations with other strategies, data monetization and data foundation. 

Data strategy goals are long-term (more than a year) and short-term (quarterly and yearly delivery milestones). As a data strategy should by aligned with business strategy, it means that a data strategy should also stay adaptable, evolutionary (innovative) and flexible. 

However, the business strategy is not the only component that a data strategy should respect. There are three other important components: 

• organizational roles (who does what with the data to facilitate collaboration and avoid duplication (data analysts do this, data scientists do that, data engineers do...)); 
• data architecture (is composed by the tools and processes, which allow us to work with and analyze data); 
• data management (is related to data governance and, according to stitchdata, “it establishes the processes and responsibilities that ensure the quality and security of the data used across an organization”).