Daily Tech Digest - November 29, 2023

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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