AIoT Data Strategy

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

The AIoT Data Stategy proposed by the AIoT Framework is designed to work well for AIoT product/solution initiatives in the context of a larger enterprise. Consequently, it is focusing on supporting the product/solution implementation and long-term evolution, and is trying to avoid replicating typical elements of an enterprise data strategy.

The AIoT Data Stategy has four main elements. First, the development of a prioritization framework which aims to make the relationship between use cases and their data needs visible. Second, management of the data-specific implementation aspects, as well as the Data Lifecycle Management. Third, Data Capabilities required to support the data strategy. Fourth, a lean and efficient Data Governance approach designed to work on the product/solution level.

Of course, each of these 4 elements of the AIoT Data Strategy has to be seen in the context of the enterprise which is hosting the product/solution development: Enterprise Business Strategy must be well aligned with the use cases. The data-specific implementation projects often have to take cross-organization dependencies into consideration, e.g. if data is imported or exported across the boundaries of the current AIoT product/solution. Product/solution-specific data capabilities must be aligned with the existing enterprise capabilities. And Product/solution-specific data governance always has to take existing enterprise-level governance into consideration.

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.