As the coming decade nears, the initial excitement attendant to technologies deemed “new” in the present decade is considerably waning.
Copyright by aibusiness.com
Organizations are devoting much greater focus to the business value derived from various applications of cognitive computing, the Internet of Things, and even Blockchain, than to the hype surrounding them.
Realizing the promise of this assortment of technologies and applications, however, necessitates overcoming the inherent obstacles of dealing with data at scale, external to the enterprise, and with low latency. Each of these factors, each of the aforementioned technologies and their applications, only increase the importance of data strategy to effectively dealing with what simply equates to more data, faster, and more distributed than ever before.
The basic paradigm of utilizing both offensive and defensive dimensions of data strategy remains as relevant as it ever was. “To give this analogy, if you are only doing offense when it comes to data strategy, or only doing defense, you are losing the game,” cautioned EWSolutions president and CEO David Marco. “Your data strategy must include a strong offense coupled with a strong defense.”
The defensive aspect of data strategy includes reducing enterprise risk by dealing with regulatory compliance, legal issues, discovery, and cost. The offensive side is predicated on the reusability of data assets to increase monetization, personalization, and optimization so that, according to David Schweer, product marketing director at Aprimo , organizations can ideally “reuse this [data] multiple times” for competitive advantage—even across use cases. Shades of metadata
Shades of metadata
Metadata management will likely always remain the core of data strategy, data management, and data governance. Metadata management is foundational to accounting for the massive quantities of training data for machine learning models. When pairing facets of the Internet of Things with applications of Artificial Intelligence (the AIoT), organizations must standardize their metadata management. Whether utilized for offensive or defensive purposes, astute metadata management involves:
Classifications: According to Marco, the most strategic means of classifying metadata is binary and based on business meaning: “When you look at it, you must first have your classification built from the business concept, which is a thing, state, city: something granular that I cannot break down further. And then also from a business group level, because then each one of those business concepts are tied to a technical instantiation.” Classifications are helpful for identifying PII.
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Tagging: Tags are used to describe various dimensions of data so, for example, users can “describe an image in as many unique ways as possible so I can find what I’m searching for,” Schweer explained. “Metadata is broader, but tagging is a form of metadata.” Tagging is particularly advantageous for reusing data or content. Numerous options exist for automating tagging via natural language technologies and machine learning.
Taxonomies: The hierarchies of the terms used to describe data are especially important for leveraging a centralized approach to accommodate the burgeoning decentralized data landscape. Schweer said the relationship between tagging and taxonomies is “not important to the user experience; it is the user experience.” When several users are relying on the same repository for multiple purposes or use cases, “how that’s organized and what you have access to is courtesy of your taxonomy,” Schweer added. “My user experience and ability to get my personal job done is 100 percent driven by taxonomy, metadata, and tagging capabilities.” […]
Read more – aibusiness.com
As the coming decade nears, the initial excitement attendant to technologies deemed “new” in the present decade is considerably waning.
Copyright by aibusiness.com
Realizing the promise of this assortment of technologies and applications, however, necessitates overcoming the inherent obstacles of dealing with data at scale, external to the enterprise, and with low latency. Each of these factors, each of the aforementioned technologies and their applications, only increase the importance of data strategy to effectively dealing with what simply equates to more data, faster, and more distributed than ever before.
The basic paradigm of utilizing both offensive and defensive dimensions of data strategy remains as relevant as it ever was. “To give this analogy, if you are only doing offense when it comes to data strategy, or only doing defense, you are losing the game,” cautioned EWSolutions president and CEO David Marco. “Your data strategy must include a strong offense coupled with a strong defense.”
The defensive aspect of data strategy includes reducing enterprise risk by dealing with regulatory compliance, legal issues, discovery, and cost. The offensive side is predicated on the reusability of data assets to increase monetization, personalization, and optimization so that, according to David Schweer, product marketing director at Aprimo , organizations can ideally “reuse this [data] multiple times” for competitive advantage—even across use cases. Shades of metadata
Shades of metadata
Metadata management will likely always remain the core of data strategy, data management, and data governance. Metadata management is foundational to accounting for the massive quantities of training data for machine learning models. When pairing facets of the Internet of Things with applications of Artificial Intelligence (the AIoT), organizations must standardize their metadata management. Whether utilized for offensive or defensive purposes, astute metadata management involves:
Classifications: According to Marco, the most strategic means of classifying metadata is binary and based on business meaning: “When you look at it, you must first have your classification built from the business concept, which is a thing, state, city: something granular that I cannot break down further. And then also from a business group level, because then each one of those business concepts are tied to a technical instantiation.” Classifications are helpful for identifying PII.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Tagging: Tags are used to describe various dimensions of data so, for example, users can “describe an image in as many unique ways as possible so I can find what I’m searching for,” Schweer explained. “Metadata is broader, but tagging is a form of metadata.” Tagging is particularly advantageous for reusing data or content. Numerous options exist for automating tagging via natural language technologies and machine learning.
Taxonomies: The hierarchies of the terms used to describe data are especially important for leveraging a centralized approach to accommodate the burgeoning decentralized data landscape. Schweer said the relationship between tagging and taxonomies is “not important to the user experience; it is the user experience.” When several users are relying on the same repository for multiple purposes or use cases, “how that’s organized and what you have access to is courtesy of your taxonomy,” Schweer added. “My user experience and ability to get my personal job done is 100 percent driven by taxonomy, metadata, and tagging capabilities.” […]
Read more – aibusiness.com
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