Data Strategy
A data strategy defines how an organization will use its data assets to support business objectives. This involves assessing the organization’s current data landscape, defining a vision for the future of data, and creating a roadmap for how to get there.
Maturity level, architecture, budgets are already some topics that should definitely be discussed. What was the initial budget? Does our data strategy fit within this budget? Should we adjust the budget or adjust the data strategy? Organizing workshops which can have both a functional and technical background helps to make this concrete. Together we arrive at a roadmap where everyone feels comfortable.
Why is Data Strategy useful?
The key components of a data strategy
Identification
Identifying data, and recognizing its meaning and usefulness, is an indispensable requirement of a good data strategy. After all, using and processing data is not possible if it does not have a name, is not in a specific format and has not been assigned a value. An organization must describe (meta)data so that it is recognized.
Storage
Often there is no plan to easily share and move data between systems. A good data strategy ensures that once data is created, it is easily accessible and can be shared by anyone, without anyone else having to make their own copies. Securely archiving data is also a crucial activity.
Distribution
Today, a large number of systems are used to support business processes and management decisions. Data sharing is no longer a specialized technicality to be set up specifically for a particular application, but rather a need to be addressed in general.
Integration
Data integration includes all data (structured and unstructured) and all data movement between applications. Within most organizations, data integration is not a central function. Each team uses its own logic to link data to their application. As such, people work alongside each other and no synergy benefits are achieved. A good data integration strategy ensures that data is cleaned up and merged into a final dataset that can be used by everyone in a consistent and repeatable way.
Governance
Setting up procedures and defining rules for data use provides better governance. This is especially important when the amount of information to manage and share grows. Policies must be formalized, roles and tasks established and methods implemented to ensure data quality and security.
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