8April 2021IN MY OPINIONCloud based Data as a Service (DaaS)By Brian Skapura, Former Managing Director, The American Institute of Architects and CIO, Technology JuntoToday's CIO is challenged to turn data into wisdom at all levels of the organization. We need to transform our organizational data into wisdom, cybersecurity data into wisdom and our client data into wisdom. This process is commonly known as the Data Information Knowledge Wisdom (DIKW) pyramid, which refers loosely to a class of models for representing structural and/or functional relationships between data, information, knowledge, and wisdom. Data as a Service (or DaaS) is a way enable this process. Traditionally, organizations have used data stored in a self-contained repository, with software specifically developed to access and present the data in a human-readable form. This same software prevents real data sharing and the discovery of knowledge and wisdom. DaaS breaks that model and shares the sources via a federated enterprise information architecture model across a range of platforms, data publishers, and users. DaaS enables sharing by automatically identifying and adding data to a catalog though a set of ontologies and taxonomies (information models). Data is given context and meaning by mapping dataset metadata to an information model (giving semantic meaning to the data) using an auto-recommendation algorithm. A good ontology integrates major open-source, highly-utilized standard ontologies. This article focuses on applying DaaS for cloud based ingestion and redaction.Best PracticesThere are several best practice / design principle to ensure your DaaS has the best chance for success. If you implement DaaS, make sure data resources and capabilities are:· Discoverable ­ ability to discover data and analytics across multiple instances· Consistent ­ consistent use of syntax and terminology (if not necessarily naming)· Distributed ­ ability to share data and analytics across multiple platforms & instances· Standardized ­ analytics design patterns provided and largely adhered to· Decentralized and Organic - no requirement for centralized approvals, only central registries· Scalable ­ ability to accept and manage multiple data feeds and analyticsWhat problems does DaaS solve?Large organizations are unable to discover, access, and share data across users and groups ­ a problem that wastes huge amounts of time and money and often results in failing to achieve critical mission goals. This problem often comes from legacy conditions:· In a federated data environment, each data publisher has different methods of describing, storing, and accessing data, this makes sharing data difficult· Users do not know what the data is or how it can be used· Silos do not see how the data applies to the enterprise· Data publishers often describe the same terms using different names, inhibiting discovery, use and integration. DataWisdomKnowledgeInformation
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