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A data ecosystem is the architecture of the big data world—encompassing the elements that allow organizations to store, process and analyze data. Modern data ecosystems house all the infrastructure and applications that organizations used to harness data and present a unified view that strengthens the work of cross-functional teams and helps them collaborate. To build a data ecosystem, first define the problem it is trying to solve through data science. .” For organizations to receive top-notch analytics and business insights, raw data goes through a rigorous process. This workflow is at the heart of the data ecosystem and should ideally be improved over time ad technology advances and more data knowledge is derived.
Every organization creates its unique ecosystem depending on its needs and abilities. There are three general types—closed (organizations share data in a closed environment), strategic partnerships (a small number of organizations share data for a dedicated purpose) and open data ecosystems (where organizations share data for the public good openly).
While the goal should be for organizations to achieve a holistic data environment, a data ecosystem is built in layers, made up of several components—alternatively referred to as a “technology stack. The infrastructure— hardware and software such as servers, search languages and hosting platforms—is the base. Infrastructure works with three segments of data—structured (information organized in some kind of system), unstructured (data compiled from an untouched source) and multi-structured (data derived from an array of sources and formats). Analytics is the next layer—the process which summarizes the data presented through infrastructure (i.e. machine learning, business intelligence, etc.) The last layer is usually built up of applications—systems powered by the data.
Creating data ecosystems is beneficial to organizations because: