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

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.

AIoT 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.

Business Alignment & Prioritization

The starting point for business alignment & prioritization should be the actual use cases, which are defined and prioritized by the business sponsors - or, alternatively, Epics which have been prioritized in the agile backlog. Sometimes, Epics might be too coarse grained. In this case, Features can be used alternatively.

For each Use Case / Epic, an analysis from the data perspective should be done:

  • What are the actual data needs to support the Use Case / Epic?
  • Which of this data is believed to be already available, which must be newly acquired?
  • How can the required data quality be ensured for the particular use case?
  • What are potential financial aspects of the data acquisition?
  • And how does the use cases support the monetization side of things?
  • Is this a case where the required data is adding functional value to the use case, or is there a direct data monetization aspect to it?
  • What are the relationships between the identified data and the other elements of the AIoT Data Strategy: Implementation & Data Lifecycle Management, specific capabilities applying to this particular kind of data, and Data Governance.

A key aspect of the analysis will be the Data Acquisition perspective. For data which can (at least theoretically) be acquired within the boundaries of the AIoT product/solution organization, the following questions have to be answered:

  • Is the required technical infrastructure already available?
  • Does the team have the required capabilities and resources available?
  • Especially in the case of AIoT data acquired via sensors:
    • Are new sensor required?
    • If so, what is the additional development & unit cost?
    • Is there an additional downstream cost from the asset/sensor line-fit point of view?
    • What is the impact on the business plan?
    • What is the impact on the project plan?
    • What are the technical risks for new, unknown sensor technologies?
    • What are required steps in terms of sourcing and procurement?

For data that has to be acquired from other business units, a number of additional questions will have to be answered:

  • Is it technically feasible to access the data (availability of APIs, bandwidth, support of required data access frequency and volume, etc.)
  • Can the neighboring business unit support your requirements not only in terms of technical access, but also in terms of project support and timelines?
  • Are there costs involved for the technical implementation and/or the data access (internal billing)?
  • Are there potential limitations or restrictions due to existing internal data governance guidelines, regional or organizational boundaries, etc.

For data which has to be acquired from external partners or suppliers, there are typically a number of additional complexities which will have to be addressed:

  • Technical feasibility across enterprise boundaries
  • Legal framework required for data access
  • SLA ensurance
  • Billing and cost management

Based on all of the above, the team should be able to make an assessment of the overall feasibility and costs / efforts involved on a per use case / per data item basis. This information is then used as part of the overall prioritization process.

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.