Technology and business trends for 2021
Blog: AuraQuantic Blog
Like every year,
Gartner has published its annual report with its forecasts on what will be the
most influential technology and business trends in 2021.
According to the
consultancy firm, next year will continue to be marked by the COVID 19
pandemic, and companies will have to continue working in a volatile, unknown
and difficult to predict environment. Therefore, they will need technologies
that give them flexibility and adaptability, empowering people as the center of
all businesses regardless of their physical location.
“The need for operational
resiliency across enterprise functions
has never been greater”
The pandemic
crisis has left businesses in a delicate situation, and is forcing them to
rethink their operational strategies in order to strengthen their resilience
and better cope with any future crisis.
In its annual
report, Gartner suggests that organizations have already overcome the first impact
of the pandemic, and are moving from a reactive to a proactive strategy, they
should focus on these three areas: People Centricity, Location Independence and
Resilient Delivery.
People Centricity
The pandemic has
changed the way people work and interact with organizations, but people remain
at the center of all businesses and need digitized processes to carry out their
activities.
Gartner
identifies three technologies that can help companies remain focused on the people.
Internet of
behavior (IoB)
The goal, of the
Internet of behavior, is to identify habits or behaviors to influence people’s
decisions or behaviors.
To achieve this,
IoB uses data from different sources such as: Internet, facial recognition,
location tracking, physical activity monitoring, biometric parameters, actions
in social networks, consumption analysis and purchase habits, etc.
Obviously, this
technology has important social and ethical implications, and its social
acceptance will largely depend on the use that organizations make of it.
For example,
everyone would agree to give up their driving data if they were to benefit from
it, but no one would agree to share this data with the police.
IoB brings many
advantages to companies and individuals, but it also poses a risk that ethics
and legal regulation must harmonize.
Towards a ‘total
experience’
Traditionally,
multi-experience, user, customer and employee experiences have been treated
separately. However, now Gartner goes a step further and indicates that they
must connect to evolve and give a better experience to all parties.
According to the
consultancy firm, since companies are trying to optimize all experiences, this
combination can offer an excellent opportunity to differentiate themselves from
the competition.
As interactions
become more mobile, virtual, and distributed, a total experience strategy that
satisfies customers, employees, and users alike becomes more necessary.
To achieve this
goal, it makes sense to focus on an improvement strategy that is useful to all
groups.
Privacy-enhancing
computation
Data privacy has
become a critical issue in recent years and more and more regulations are being
added to force organizations to focus on this issue.
Today, options
such as remote work are part of the corporate culture, but nevertheless access
from outside the corporate network can compromise the security of the data.
Therefore, it is
necessary to use data protection technologies that also guarantee security,
privacy and anonymity.
Gartner
highlights three technologies aimed at improving privacy:
- The first provides a trusted environment to process or analyze sensitive data. It includes trusted third parties and trusted hardware-based execution environments (Confidential Computing) that protect data while it is in use.
- The second performs the processing and analysis in a decentralized way (example COVID 19: tracking system and coincidence analysis carried out on the device) with the aim of anonymizing the data before it is processed. It includes Federate Machine Learning and Privacy-aware Machine Learning models that enable training models without transferring potentially sensitive user data from devices, or local deployments, to a central server.
- And the third transforms data and algorithms before they are processed or analyzed. It includes the following technologies:
- Differential privacy: allows you to collect and share data, mathematically guaranteeing that the privacy of each person will be maintained.
- Homomorphic encryption: the data is sent encrypted and a code is provided so that operations are carried out on the data without decrypting it to later return it encrypted to the sender.
- Secure Multiparty Computing (MPC / SMPC): It is a cryptographic protocol so that data can be calculated between multiple parties without any individual party being able to see the data of the other parties.
- Zero knowledge proofs: it is a cryptographic method by which one of the parties can demonstrate to the other the veracity of information, without revealing sensitive information about said information.
- Private set intersection: it is a cryptographic technique that allows two parties to compare data sets without giving up the privacy of their individual data. In other words, PSI (Private set intersection) allows you to test if the parts share a common data point (location, identification, etc.).
- Private information retrieval: it is a protocol that allows to recover an element from a database without the owner being able to determine which one.
Organizations
have discovered the potential of their data, but at the same time, new laws are
emerging in all countries to protect the privacy of their citizens. For this
reason, companies must adapt their data analysis and processing technologies
and their security controls to guarantee privacy.
LOCATION INDEPENDENCE
The COVID 19
pandemic has given the definitive boost to remote work, and organizations
around the world have had to adapt their organization, and introduce
technological improvements, to adapt to the new times.
Distributed
cloud
Unlike the highly
centralized public cloud (Cloud), a distributed network offers its services
from different physical locations, maintaining the operation, governance and
evolution of these services, under the responsibility of the public cloud
provider.
The objective of this type of cloud is to bring the location of computing resources closer to the place where data and business activities are produced, reducing latency and improving response times.
This type of
cloud also solves scenarios where the law of a country or area, such as the
European Union, has specific regulations that prevent the data from traveling
outside the user’s country of origin.
On the other
hand, customers can benefit from the advantages of a public cloud, but without
the high costs and complicated solutions that a private network implies.
These are some
of the distributed cloud modalities:
- On-premises public cloud: It is composed of cloud computing resources that are exclusively used by a company or organization. It can be hosted in the local data center or with an external service provider.
- Internet of Things (IoT) edge cloud – A distributed cloud substation designed to directly interact with or allow edge devices to host public cloud services. It includes industrial and consumer IoT capabilities, and supports use cases such as collection, broadcast, and movement. As with the local public cloud, these resources can be restricted in access and visible to a single company if necessary.
- Metropolitan Area Community Cloud: Cloud substations with local capabilities for a city or metropolitan area.
- 5G mobile edge cloud: a cloud substation distributed in a 5G telecommunications operator network with the aim of bringing service provision closer to the edge of the network and obtaining responses in near real time.
- Global Network Edge Cloud – Distributed cloud substations designed to integrate with the global network infrastructure. A connected car will incorporate cameras and sensors that will provide information about the environment in real time and will generate about 25 GB per hour and approximately 300 TB of data per year. That information will need to be processed, but moving that amount of data between the servers and the car is unaffordable, so the processing needs to occur much closer to where the data is being generated, at the edge of the network ( mobile phone towers, hubs, routers, smartphones, etc.).
Interest in
hybrid cloud computing is on the rise. And factors such as technology duplication,
latency requirements, or data residency regulations justify this trend.
Anywhere Operations
This trend
refers to a model that allows offering business solutions to customers from
anywhere, and empowering employees to carry out their job functions regardless
of where they are.
“A location-independent digital-first mindset is a prerequisite for anywhere operations.”
But it’s not
just about operating remotely. The model must offer a unique, fluid and
scalable experience. And, therefore, it implies changes at the level of
technological infrastructure, management, security and government policies, and
models of customer and employee participation.
This
technological base is comprised of five blocks:
- Collaboration and Productivity – Workflow collaboration, meeting solutions, cloud office suites, digital whiteboard, and smart workspaces
- Secure remote access: multi-factor and passwordless authentication, Zero Trust Network Access (ZTNA), Secure Access Service Edge (SASE) and identity as the new security perimeter
- Cloud and edge infrastructure: Distributed cloud, IoT, API gateways, AI at the edge, and edge processing
- Quantification of digital experience: digital experience monitoring, workplace analytics, remote support and contactless interactions
- Automation to support remote operations: artificial intelligence for IT operations (AIOps), endpoint management, SaaS management platforms, self-service and zero-touch provisioning.
Demand for
digital services increased exponentially during the pandemic, and organizations
that adopt digital business models to support their customers and employees
will recover faster than others.
Cybersecurity mesh
This concept is
closely linked to ” Anywhere operations” and is essential for the
business to function safely. To operate from anywhere it is necessary to
control access to data, devices, etc., from any location, people, etc. Now data
is more distributed than ever and secure access must be ensured no matter where
it is located.
Today, much of
the company’s critical assets and documentation are outside the organization’s logical
and physical perimeter. But, with the arrival of the pandemic, many of these
goods have been moved to the homes of employees, partners and collaborators.
In this
situation, companies must establish a security mesh that guarantees safe access
to assets, regardless of their physical location, and that takes into account
the permissions and restrictions established by role or user profile.
As a result of this new scenario, a transition to a model based on APIs is being observed, which is committed to security strategies based on the cloud such as ZTNA, CASB (Security Agents for Cloud Access) and SASE (Secure Access Service Edge) .
“As
operations continue to evolve, the cybersecurity mesh will become the most
practical approach to ensuring secure access and use of cloud-based
applications and distributed data from uncontrolled devices.”
RESILIENT
DELIVERY
This trend is
about the ability of companies to adapt, or pivot, with agility in a dynamic
business or IT environment.
The COVID 19
pandemic has created a highly volatile environment, and having the right
skills, technical capabilities, processes and systems to constantly adapt to
changing patterns is critical.
Consequently,
organizations must be composable, use artificial intelligence, and continue
with their digital transformation process, striving for hyper-automation.
Intelligent composable
business
The pandemic
highlighted vulnerabilities in business models that have been focused on
efficiency for years. Organizations that were once efficient suddenly became
fragile at a time when they needed to be flexible.
Faced with this situation, companies are obliged to face a change that allows them to move towards a more autonomous and efficient decision-making model. And to achieve this goal, technology platforms must move towards an architecture that facilitates access and content creation, prioritizing democratization and composable elements.
It is about
composing complete solutions, based on different technological elements, that are
inter-connected, and that would provide applications to solve the business
issues, and offer a greater control and understanding of the data.
From an IT architecture point of view, a composable business solution is based on the use of APIs (Application Programming Interface).
“Composable business is a natural acceleration of the digital business that you live every day. It allows us to deliver the resilience and agility that these interesting times demand.”
Daryl Plummer
Artificial
intelligence engineering
Gartner research
reveals that almost 50% of artificial intelligence projects do not make it from
prototype to production because organizations do not apply artificial
intelligence engineering that the consultancy firm defines as “a discipline
focused on the governance and life cycle management of a wide range of
operationalized AI and decision models”. That includes machine learning,
knowledge graphs, rules, optimization and linguistic and agent-based models.
AI engineering
is built on three basic pillars: DataOps, MLOps, and DevOps.
- DevOps is a set of practices that combines software development (Dev) and information technology operations (Ops) with the goal of shortening the system development lifecycle and providing continuous delivery with high-quality software.
- DataOps is an automated, process-oriented methodology used by data and analytics teams to improve quality and reduce data analysis cycle time.
- MLOps is a practice of collaboration and communication between data scientists and operations professionals to help manage the lifecycle of production machine learning (or deep learning).
What is
responsible AI?
Responsible AI
implies applying the principles of trust, transparency, ethics, fairness,
interpretability, security, etc., to operations carried out with this
technology.
Hyperautomation
Gartner defines hyperautomation as an
effective combination of complementary sets of tools that can integrate
functional and process silos to automate and augment business processes.
These technologies include the
application of advanced technologies, such as artificial intelligence (AI),
machine learning (ML), RPA, BPM, and data mining.
Organizations
have clearly opted for hyperautomation in recent years, however, they still
have many processes supported by obsolete and ineffective technologies.
In 2019, the
CEOs who demanded greater advances in digital operations, considered legacy technologies
as their main obstacle to achieve efficiency and democratization of process automation,
and the required data integration. But in 2020, with the COVID 19 pandemic and
many people working from home, digital excellence has become imperative and is
evolving towards operational resilience.
“Hyperautomation
is irreversible and inevitable. Everything that can and should be automated
will be automated.”
Brian Burke,
Research Vice President, Gartner
Competitive
pressures for business efficiency, effectiveness, and agility are forcing
organizations to address all possible scenarios for operations – back office,
middle office, and front office. And organizations that resist change will have
a hard time staying competitive.
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