In mid-February, I attended the ARC Forum in Orlando, Fla. There was a strong emphasis on digital transformation as well as platforms and standards supporting it.
Within the digital transformation, a variety of platforms are emerging, such as Infrastructure-as-a-Service, IoT edge, cognitive computing, and cloud application platforms. This makes it even more important to integrate these platforms and establish a semantic layer across them to ensure they can be orchestrated towards company strategies and business goals.
To help companies in today’s world digitally transform their business, here are some trends and observations:
- IT, operations, and engineering departments need to ensure interoperability between and across their domains. Historically, these three entities have all operated in their own silos under different standards set by different organizations with different goals.
- More and more associations and companies are starting to collaborate and converge on open standards that support end-to-end processes, cycles, and value chains. For example, Namur Open Architecture, ZVEI Modul Type Package, and the Open Process Automation Group have a memorandum of understanding in the works to promote common standards and frameworks. Besides integration and simplification, avoidance of vendor lock-in is another key driver behind this.
- Cloud platforms are starting to gravitate around functional needs with an underlying common IT technology (enterprise system of record platform, enterprise innovation platform, intelligent supply chain platform, operations and maintenance platform, asset network platform, product design platform, and so on).
- Seamless, bidirectional data and information flow, supported by rules and workflow engines, are indispensable ingredients for turning data and analytics into action. This goal will be supported by an “intelligent and agile core” enhanced by a peripheral layer of microservices that can be easily consumed via APIs (IT landscape of the future).
- The importance of the “intelligent edge” is increasing. Initially focusing primarily on reducing-data security risks, now operational issues such as analyzing and controlling devices, improving process speed, and reducing latency issues will prompt end users to get a much broader perspective on edge computing. Overall, this is driven by the ongoing need to maximize asset maintenance and production performance. Innovative models are now run on the edge, leveraging inexpensive cloud space for optimization.
- People and processes are as important as technology for the adoption of digital transformation. In other words, machine learning, IoT, and blockchain don’t excel by themselves. They need to be embedded into industry and business contexts as well as processes. From a hiring perspective, the data engineer is an emerging species, as special skills are needed around data mining, data analysis, data orchestration, and data governance. Such data engineers need to be paired with business and process-domain experts to ensure innovative technologies unfold their true potential.
- Change management is more important than ever before. Consistent and clear top-to-bottom communication and measuring transformation program progress by a common set of clearly defined KPIs are pivotal to successfully building relationships and trust across all enterprise entities.
What do you think? Please share your thoughts and observations with us.
For more insight on emerging tech, see Future Of Work 2018: 10 Predictions You Can’t Ignore.