Finally, at the technology and data level, the company sees the following trends emerging:
- Conflicts over data access are delaying business impact - Asset owners are increasingly placing restrictions on who is allowed to view and use data coming from their machines. Moreover, many governments have implemented strict data sovereignty and privacy regulations, often for good reasons, but in practice are creating further restrictions and complications. The company sees two basic scenarios emerging: (1) companies will be open to sharing data with OEMs, since this provides more value to the operator than going it alone (for example, aircraft engines); or (2) operators will keep control of data to differentiate performance.
- Cost pressures are determining whether the cloud or the edge environment wins out as the IoT host environment - In many industrial sectors with mobile and/or remote assets (such as oil and gas, aviation, and transportation), shifting some analytics intelligence to the edge may be more cost effective than having them in the cloud. Autonomous vehicles face a similar challenge; even with better data-transport technologies such as 5G, response times for rapidly moving vehicles may make an edge-based solution more relevant. The debate about whether to store data and analytics at the edge or centrally on the cloud hinges on which is decreasing faster: the cost and latency of data transmission or the cost of "smarter" edge equipment.
- Cyberattacks are not noticeably derailing existing IoT efforts - Cybersecurity is top of mind for virtually every CXO who is involved in IoT. According to McKinsey's research and surveys, almost 50% admit (or realize) they have been attacked, and of those who know they've been attacked, more than 25% experienced what they call high or severe damage as a result. However, even companies that have been attacked and significantly damaged are for the most part not significantly curtailing their IoT activities.
- Artificial intelligence (AI) has caught on in IoT in the past two years - Real use cases of AI with valuable results are emerging, particularly around machine learning (ML), as adoption steadily increases. According to McKinsey's research, AI and ML are now being used in 60% of IoT activities, the increase a result of three major factors: the convergence of algorithmic advances, data proliferation, and tremendous increases in power and storage capabilities at a lower cost.
For more, see "Ten trends shaping the Internet of Things business landscape."