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Revision as of 22:52, 2 January 2021

AIoTArtificial IntelligenceInternet of ThingsAIoT Data StrategyBusiness ModelProduct ArchitectureAIoT DevOps & InfrastructureTrust & SecurityReliability & ResilienceVerification & ValidationProduct OrganizationSourcing and ProcurementService OperationsOverview of Ignite AIoT Framework

As part of their digital transformation initiatives, many companies are putting data strategy at the center stage. Most enterprise data strategies are a mixture of high-level vision, strategic principles, goal definitions, priority setting, data governance models, as well as architecture tools and best practices for managing semantics and deriving information from raw data.

Since both AI and IoT are also very much about data, every AIoT initiative should also adopt a data strategy. However, it is important to notice that this data strategy must work on the level of an individual AIoT-enabled product or solution - not the entire enterprise (unless, of course, the enterprise is pretty much build around said product/solution). This section of the AIoT Framework is proposing a setup for an AIoT Data Strategy, as well as identifying the typical dependencies which must be managed.

Overview

AIoT Data Strategy

Business Alignment & Prioritization

Implementation & Data Lifecycle Management�

Data Capabilities

Data Governance

Authors and Contributors

DIRK SLAMA
(Editor-in-Chief)

CONTRIBUTOR
Dirk Slama is VP and Chief Alliance Officer at Bosch Software Innovations (SI). Bosch SI is spearheading the Internet of Things (IoT) activities of Bosch, the global manufacturing and services group. Dirk has over 20 years experience in very large-scale distributed application projects and system integration, including SOA, BPM, M2M and most recently IoT. He is representing Bosch at the Industrial Internet Consortium and is active in the Industry 4.0 community. He holds an MBA from IMD Lausanne as well as a Diploma Degree in Computer Science from TU Berlin.