Many companies continue to search for new means to effectively provide knowledge management across their organization. A key driver to this is the ability to manage the underlying data sources that support that knowledge. This can be data you own inside of your company, or data that exists in the public domain. FAIR Data Principles have been developed within the semantic technology community to help guide people on how to better access and utilize their data in a more comprehensive and sharable manner. FAIR Data Principles describe the importance of making data Findable, Accessible, Interoperable, & Reusable – a theme which has grown considerably in just the past couple of years.
However, while FAIR Data is a very useful set of generic guidelines, it is not easily implementable within large organizations. Many companies do not have the semantics backgrounds to fully deploy such a strategy easily. The literature around FAIR Data is still fairly high level and requires a lot of “filling in the blanks” in terms of architecting and building working software that reflects FAIR Principles in practice.
This white paper discusses a highly pragmatic approach to FAIR Data and how it can be quickly and easily implemented inside of any organization using the LeapAnalysis software product. LeapAnalysis provides an engine that makes FAIR Data implementation a reality in a matter of months. We have removed the guesswork for people looking to implement FAIR Data to provide better knowledge management, to access, share and use data more widely, and to drive tangible business results that matter. LeapAnalysis provides customers with a straightforward means to actually implement a FAIR Data strategy at scale with confidence and drive your company’s digital transformation faster, with more precision.
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