Today, digital technologies are accelerating and enabling the development of other technologies at a pace never seen before. As we create and innovate at ever greater speed, are we losing the ability to reflect on the impact of our creations in a broader societal context?
For all the opportunity it brings, the acceleration of technology through digitalization generates economic, social and ethical challenges. Before society has had a chance to reflect on the broader implications of a given technology, is already packaged for sale in a variety of channels. Today, anyone can get free access to a cloud provider and begin experimenting with very powerful machine learning capabilities, without thinking about things such as gender or race bias1. And, while enterprises struggle to manage the explosion of technologies coming to market, new business models are disrupting existing businesses more rapidly than ever2.
This concept is key to the success of enterprises adopting emergent technologies. It refers to the ability to take proven and emerging technologies, combine them with data, and leverage new business models to deliver on customers’ changing expectations3. Take precision agriculture as an example. The Internet of Things (IoT), advanced data processing, machine learning, advanced visualization techniques, and next generation capabilities such as automated nutrient and water delivery are combined to help the farmer increase both quality and yield, while reducing environmental impact – all developments that will change the face of farming in many parts of the world by 20304,5.
Tesla is using compositional architectures combined with novel business models. They use massive data collection from their cars, which, unlike their combustion counterparts, are designed from the wheels up for high-speed, real-time connectivity. This facilitates fast learning on emerging issues and testing of new features, while collecting data necessary to improve that feature. This speed of innovation is one reason why we expect electric vehicles to outsell their combustion counterparts globally as early as 2032 – a key finding of DNV GL’s Energy Transition Outlook (2019)6.
The pipeline of data, from creation to value extraction, will become an even greater opportunity for organizations managing their data ecosystems, as well as for companies creating technologies for each part of the value chain.
The ecosystem has three distinct parts:
- Connectivity consists of the IoT combined with the networks the data traverses. With 5G networks, enterprises will be instrumented and measured. The 5G market is expected to reach USD 700 bn by 20307.
- Processing and management are fundamental to every organization, encompassing the data they create, consume, and share. Tools providing data quality management, provenance and compliance to organizational and regulatory standards will form a key part of any enterprise’s architecture.
- Presentation and use is when an organization extracts value from the data pipeline. Examples are dashboards, mixed reality remote assists, and automation of normally manual operations in factories and plants.
Data, algorithms, and AI will pose a great number of opportunities and risks over the next ten years. Organizations will instrument their operations with more sensors providing data replacing a greater variety of physical properties, convert manual data entry to digital data entry, and implement more automation to increase performance. Data provenance, or ensuring that the data is valid, correct and untampered as it moves through the pipeline, will be imperative.
As customers become more aware of the value of their data, they expect this data to be treated in a secure manner, compliant with relevant regulations and laws. With the implementation of ML and AI, organizations will be expected to show how these systems made decisions8,9. As more countries pass legislation on the regulation of data, data management tasks will increase in complexity, potentially exposing enterprises to litigation.
- IBM Quantum Experience
- Girotra, K. and Netessine, S. (2013) “Why Large Companies Struggle With Business Model”, Harvard Business Review
- Cisco (2019) ‘At a glance. The Internet of Things.’
- CHS Farmers Alliance (2017) The Benefits of Precision Agriculture
- National geographic (2018) ‘Farming: There’s an App for That’
- Ericsson (2019) ‘5G report: industry digitalization could be a USD 700 billion market by 2030’
- Gershgorn, D. (2017) ‘AI is now so complex its creators can’t trust why it makes decisions’, Quartz (7 December 2017)
- Tao Tong, J. K. (2017) ‘AI Regulation: understanding the real challenges’ in Paris Innovation Review