Gartner Top 10 Strategic Technology Trends for 2020 (II)
Read Part I of Gartner Top 10 Strategic Technology Trends for 2020 here.
Trend No. 5: Transparency and Traceability
An important concept of the technology trends is “ethics”. As the amount of data accumulated and analyzed by the companies, institutions, governments increase, the issues like digital ethics and privacy have become growing concerns. The autonomous decisions by the AI systems made digital ethics a very important concept. We need the transparency and traceability, that is defined as “a range of attitudes, actions, and supporting technologies and practices designed to address regulatory requirements, enshrine an ethical approach to use of AI and other advanced technologies, and repair the growing lack of trust in companies”, in this new highly digitized environment. This concept involves five elements of “trust”: ethics, integrity, openness, accountability, competence, consistency.
Enterprises should implement explainable and ethical AI and algorithms as they affect many decisions we take form buying a house to hiring an employee. This topic will be more important in the future for the reputation of institutions and will affect the business results more. The biased AI algorithms might endanger society and business, may create social polarization and political implications. The solution is explainable and ethical AI by which the companies, institutions, governments transparently explain their algorithmic decision making.
Another aspect of the issue is data privacy and ownership. People have the right to know how their personal information is being used by organizations in both the public and private sector. In Europe, GDPR regulates the data usage in the private sector, but for the public sector there is not any legal control. By 2020, Gartner expects that companies that are digitally trustworthy will generate 20% more online profit than those that are not.
Trend No. 6: The Empowered Edge
For Gartner, edge computing is a kind of “computing topology in which information processing and content collection and delivery are placed closer to the sources, repositories and consumers of this information.” In edge computing, the processing of information distributed not centralized. Giving greater autonomy to edge enables keeping the traffic and processing local and reduce latency. Definitely this new trend emerged from the need for embedded IoT world for specific industries such as manufacturing or retail. Complex edge devices including robots, drones, autonomous vehicles accelerated this shift. In the long term, Gartner expects more unstructured architectures with a wide range of “things”. The layers of edges will be connected to the centralized data centers and cloud services. Gartner expects an increase in the embedding of sensor, storage, compute and advanced AI capabilities in edge devices through 2028. This issue of edge computing is a topic that should be on the agenda of data analytics leaders.
Trend No. 7: Distributed Cloud
Gartner defines distributed cloud as “the distribution of public cloud services to different locations outside the cloud providers’ data centers, while the originating public cloud provider assumes responsibility for the operation, governance, maintenance and updates.” In the initial phase, that we went through about cloud, we had centralized modes of public cloud services. Now, it is a new era of cloud computing in which there are private and hybrid cloud options. A private cloud is dedicated to companies and the data is run in their own data centers. A hybrid cloud is used to have the external services from a provider while internal services are run on-premises.
When location is an important issue of cloud computing, because of data sovereignty and latency related issues, distributed cloud services is used to meet the requirements of the companies. In the distributed cloud, the originating public cloud provider is responsible for all aspects of cloud service architecture, delivery, operations, governance and updates. Distributed cloud is in its early phase and some companies provide only a subset of their services through cloud services.
Trend No. 8: Autonomous Things
Gartner provides a very simple definition for “autonomous things”: they are “the physical devices that use AI to automate functions previously performed by humans”. The most well known autonomous devices are robots, drones, autonomous vehicles/ships, some industrial equipments. Autonomy is not new for us, but it used to be through rigid programming models, whereas now we see AI-based autonomy that creates a more natural and direct interaction with people. They are improving very rapidly and we can expect them to be around us in uncontrolled public areas as well as controlled public and private areas.
There is not one level of autonomy, but a spectrum from low levels to full automation. According to Gartner, a system has to operate unsupervised within a defined context to complete a task in order to be autonomous. The spectrum is described by Gartner from “no automation” to “full automation” having the concepts of human-assisted automation, partial automation, conditional automation, high automation, respectively. Even in full automation, there is the involvement of human control and direction, at least human would define the destination in the case of an autonomous car.
Companies should focus on well-scoped purposes for automation by creating business scenarios and customer journey maps to see where they can automate human routine tasks. According to the report, the most visible areas for automation would be advanced agriculture, transportation (Gartner predicts more than 1 million Level 3 and above cars will be produced annually by 2025), search and rescue.
Trend No. 9: Practical Blockchain
We have been talking about blockchain for more than 5 years. It can be defined as “an expanding list of cryptographically signed, irrevocable transactional records shared by all participants in a network.” It involves “distributed ledgers” and eliminates the central authority and drives trust by distributing the right to access and check every transaction to a group.
How can blockchain drive value? Gartner asserts that “Blockchain has the potential to reshape industries by enabling trust, providing transparency and enabling value exchange across business ecosystems — potentially lowering costs, reducing transaction settlement times and improving cash flow.” So we see many banks and institutions who use blockchain for international trade related transactions and identity management. According to the 2019 Gartner CIO Survey, 60% of CIOs expect some kind of blockchain deployment in the next three years.
Blockchain Will Be Scalable by 2023
As we had seen blockchain as part of many technology trends reporsts for several years, companies still could not create relevant live use cases for blockchain at scalable level because of some technical issues. It is a revolutionary development as it erases the central authority and carries it to the decentralized public consensus. According to Gartner, “by 2023, blockchain will be scalable technically, and will support trusted private transactions with the necessary data confidentiality.” In most of the cases, a consortium controls the system, which can be technology-centric; geographically centric; industry-centric and process-centric. So choosing the right consortium is important for the companies.
Gartner specifies some use cases for blockchain:
- Asset Tracking: Tracking automobiles through loan processes
- Claims: Product recalls, insurance
- Identity Management/Know Your Client (KYC): Educational achievement, patient health, election identity and national identities
- Internal Record Keeping: Master data management, internal document management, purchase order and invoice records, and treasury record keeping
- Loyalty and Reward: (For retailers, travel companies and others) and providing internal rewards, such as employees or students
- Payment/Settlement: Loyalty payments, stock settlements, interbank payments, commercial lending, procure-to-pay processing and remittance processing
- Provenance: Tracking biological samples and organs; establishing the provenance of wine, coffee, fish and other foods; certifying the authenticity of components; and tracking pharmaceuticals through their life cycle
- Shared Record Keeping: Corporate announcements, multiparty hotel booking management, recording of flight data and regulatory reporting
- Smart Cities/the IoT: Peer-to-peer energy trading, administration of electric vehicle charging, smart grid management and control of wastewater systems
- Trade finance: Managing letters of credit, simplifying trade finance and facilitating cross-border trade
- Trading: Dealing with derivatives, trading of private equity and sports trading
Trend No. 10: AI Security
AI is a technological field that is involved within or augment all the trends we mentioned here. It is also mentioned as a top trend in many technology trends reports. As AI and machine learning specifically get mature and find real use cases more, the challenges and security risks become more apparent. Therefore, all the institutions using AI technologies should be focusing on:
1) Protecting AI-powered systems: IT leaders should ensure data quality, integrity, confidentiality and privacy. Gartner predicts that through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft or adversarial samples to attack AI-powered systems.
- Training-data poisoning: Hackers might have unauthorized access to training data and cause an AI system to fail by feeding it incorrect or compromised data.
- Model theft: Competitors can reverse-engineer ML algorithms or implement their own AI systems to use the output of your algorithms as training data.
- Adversarial samples: Classifiers are susceptible to a single sample of input/test data, which can be altered slightly to cause an AI classifier to misclassify it.
2) Leveraging AI to enhance security defense:
Security tool vendors are using ML to enhance their tools, decision support and response operations because AI systems can learn what is “normal” and warn when there is an anomaly. However, on the other hand, attackers use more complex attack techniques that force security tool developers create more advanced systems. This is a vicious circle and never ends.
3) Anticipating nefarious use of AI by attackers: New technologies are not only used for good purposes but they are used nefariously as well. AI is coming top on the list. The next frontier of AI-related security concerns is emerging as attackers begin to use ML and other AI techniques to power their attacks. Attackers have just started to leverage ML. They explore ML in many security areas and commoditization of ML tools and the availability of training data made it easy. Nefarious ML can learn what is normal and adjust attacks within this “learned normal.” It can learn, simulate and abuse writing styles, social graphs and communication patterns for deception.
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