How the Rise of the Gig Economy Is Changing Startup Investment
The gig economy’s ascent, defined by short-term, project-based work, is
propelled by smartphone pervasiveness and widespread use of the internet.
Enabled by seamless digital interactions, gig workers connect with employers on
platforms. Increasing demand for flexible work, driven by a desire for autonomy
and personalised schedules, fuels this shift. This tech-driven evolution offers
a dynamic alternative to traditional employment models. ... As the gig economy
reshapes work dynamics, startup investors position themselves to capitalise on
the flexibility, efficiency, and rapid scaling of these forward-thinking
businesses. The gig economy’s rise is transforming how investors evaluate
startups. There’s a noticeable shift toward companies that not only understand
the gig economy complexities but also effectively harness its potential.
Seamless integration of gig workers is now a pivotal factor in startup
investment assessments. Investors value companies employing the gig economy for
enhanced flexibility, cost efficiency, and access to specialised skills.
Startups with a strategic approach to the gig economy are increasingly the
preferred choice for investors navigating the evolving business landscape.
The Emergence of Corporate Data Governance
It is both an opportunity and a challenge for corporate governance as new
technologies such as big data platforms and cloud platforms digitize and process
data at scale in firms. The pandemic has also intensified the need for
digitizing data and improving accountability in organizations. Thereby, data
availability in firms started to increase, which has become a strategic asset to
drive the firm’s valuation. A growing amount of data combined with insignificant
and poor-quality information has been a challenge for large corporations for
years. In 2008, by conducting a survey of 200 organizations across the globe,
Pierce, Dismute, and Yonke stated that 58% recognized data as a strategic asset.
The management of data as information and its intelligibility has become a high
priority in corporate governance and regulatory compliance. Data Governance is
generally defined as the allocation of roles, decision-making rights, and
accountability relating to data assets. The goal of Data Governance is to ensure
that policies and ownership of data are enforced within an organization.
The Role of the CISO in Digital Transformation
Digital transformation isn't solely technical. It involves the entire
organization, is driven by business needs and customer expectations, and can
impact the way that work gets done from top to bottom. In the absence of a
strong CISO making their voice heard, it's all too easy for decisions to be made
that may not fully consider critical security implications. A strong CISO is an
effective collaborator, working as an equal partner with key stakeholders such
as the CIO, CTO, and CEO. A CISO needs to connect the dots between security and
business success, using a combination of technical expertise and organizational
influence to ensure security controls are properly incorporated, even during
times of rapid organizational change. The difference between a capable CISO and
an exceptional one often comes down to the ability to see both the big picture
of business strategy and the fine details of technical security at the same
time. Business units seeking new technological solutions may not have the
necessary visibility beyond their individual spans of control to consider
factors like data security and the flow of sensitive information between
multiple different cloud-based tools.
What’s the Go language really good for?
Go binaries run more slowly than their C counterparts, but the difference in
speed is negligible for most applications. Go performance is as good as C for
the vast majority of work, and generally much faster than other languages known
for speed of development (e.g., JavaScript, Python, and Ruby). Executables
created with the Go toolchain can stand alone, with no default external
dependencies. The Go toolchain is available for a wide variety of operating
systems and hardware platforms, and can be used to compile binaries across
platforms. Go delivers all of the above without sacrificing access to the
underlying system. Go programs can talk to external C libraries or make native
system calls. In Docker, for instance, Go interacts with low-level Linux
functions, cgroups, and namespaces, to work container magic. The Go toolchain is
freely available as a Linux, MacOS, or Windows binary or as a Docker container.
Go is included by default in many popular Linux distributions, such as Red Hat
Enterprise Linux and Fedora, making it somewhat easier to deploy Go source to
those platforms.
How Digital Transformation is revolutionizing the Financial-Tech industry
In an age of rising cyber threats and data breaches, security is paramount in
the FinTech industry. Digital transformation has ushered in new security
measures, such as biometric authentication and blockchain technology. These
innovations have made financial transactions more secure, ensuring that
customers’ data and assets remain protected. Furthermore, digital transformation
has revolutionized the way we make payments. The rise of mobile payments and
digital wallets has made traditional methods, such as cash and cheques, almost
obsolete. Today, individuals can make instantaneous payments using their
smartphones or wearable devices, eliminating the need for physical currency or
cards. This has not only made payments more convenient but has also increased
financial inclusion by providing access to financial services for those without
a bank account. Breaking down barriers of financial ways that often fail to
reach underbanked or unbanked populations, leaving millions without access to
vital financial services. FinTech solutions, driven by mobile technology, are
reaching previously untouched markets.
The 3 biggest risks from generative AI - and how to deal with them
As well as assessing data protection risks across external processes,
organizations need to be aware of how employees use data in generative AI
applications and models. Litan says these kinds of risks cover the unacceptable
use of data, which can compromise the decision-making process, including being
slack with confidential inputs, producing inaccurate hallucinations as outputs,
and using intellectual property from another company. Add in ethical issues and
fears that models can be biased, and business leaders face a confluence of input
and output risks. Litan says executives managing the rollout of generative AI
must ensure people across the business take nothing for granted. ... Businesses
deal with a range of cybersecurity risks on a day-to-day basis, such as hackers
gaining access to enterprise data due to a system vulnerability or an error by
an employee. However, Litan says AI represents a different threat vector. "These
are new risks," she says. "There's prompt injection attacks, vector database
attacks, and hackers can get access to model states and parameters."
Demystifying Container Security for Developers
Any container security program must take into account the security of the
creation and contents of the containers themselves. There are several criteria
by which to analyze container security, starting with the foundational elements
of host security, then considering platform security elements, and finally,
examining the elements of the container and orchestrator itself. Infrastructure
security includes the integrity of the physical and virtual resources that
underpin container operations, both metaphorically and literally. Containers run
on physical hardware somewhere, therefore the hosting environment’s security
affects the security of the containerized environment. ... While many
deployments are done in different ways, the hosting operating system’s security
and controls are vital for the same reasons infrastructure security is — if the
OS is compromised, the workloads operating in it cannot be protected. The
security of containerized architectures relies on best practices including
strong identity and access management, OS security controls and secure
deployments under an assumed compromise model.
Striking the right balance in leadership behaviour
Most of the leaders demonstrate either task-driven behaviour or people- and
relationship-driven behaviour. The leaders demonstrating task-driven behaviour
are more focused on allocating tasks, organising the work, providing the
structure, setting the work context, defining roles and responsibilities,
ensuring feedback mechanisms, and diligent processes for ensuring timely
delivery of the task and outcomes. People-driven leaders prioritise
relationships, build camaraderie, work in a more harmonious culture, and value
respect, trust, and other humane aspects of teamwork. While these two
behavioural approaches differ, they are inherently linked. However, an excessive
emphasis on either end can undermine both the organization's progress and team
cohesion. Striking a harmonious balance between these approaches is pivotal.
It's not just about finding equilibrium; it's about seamlessly integrating these
behavioural styles. This integration enhances productivity, fosters stronger
team bonds, and fortifies the organization to tackle challenges for sustained
growth.
Analyst Panel Says Take the Quantum Computing Plunge Now…
Sorensen said, “What’s so magical about quantum right now is, is the beauty of
the low barrier to entry. In the old days if you wanted to get an HPC, and Jay
knows this, you had to drop $25 million to bring a Cray in. You had to hire 25
guys and they lived downstairs in the basement. They never came out and they
wrote code all the time and they spoke a language that you didn’t understand,
and you had to pay them an awful lot of money to do that. The barriers to entry
to get into HPC was high. “The barrier to entry in quantum is you sit down, you
go to AWS or Strangeworks. You pick your cloud access model of choice, you sign
up for a couple of bucks, you grab a couple of new hires that just came out of
with a degree in quantum chemistry or something, and you go and you play, and
you figure out how that’s going to work. So, the barriers to entry of quantum
are amazing. I’ve said it before, and I’ll say it again, if it wasn’t for cloud
access, none of us would be sitting here vaguely interested in quantum; it’s
what really is driving interest.” Boisseau had a similar take.
Adopting Asynchronous Collaboration in Distributed Software Teams
In an async-first culture though, we adopt a bias for action. We make the best
decision we can at the moment, document it, and move on. The focus is on getting
things done. If something’s wrong, we learn from it, refactor and adapt. The
bias for action improves the team’s ability to make and record decisions.
Decisions are, after all, the fuel for high-performing teams. All these benefits
aside, an async-first culture also helps you improve your meetings. When you
make meetings the last resort, the meetings you have, are the ones you need.
You’ll gain back the time and focus to make these few, purposeful meetings
useful to everyone who attends. Going async-first does not mean that synchronous
interactions are not valuable. Team interactions benefit from a fine balance
between asynchronous and synchronous collaboration. On distributed teams, this
balance should tilt towards the asynchronous, lest you fill everyone’s calendars
with 80 days of meetings a year. That said, you can’t ignore the value of
synchronous collaboration.
Quote for the day:
"If you want people to to think, give
them intent, not instruction." -- David Marquet
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