3 Key Metrics to Measure Developer Productivity
The team dimension considers business outcomes in a wider organizational
context. While software development teams must work efficiently together, they
must also work with teams across other business units. Often, non-technical
factors, such as peer support, working environment, psychological safety and job
enthusiasm play a significant role in boosting productivity. Another framework
is SPACE, which is an acronym for satisfaction, performance, activity,
communication and efficiency. SPACE was developed to capture some of the more
nuanced and human-centered dimensions of productivity. SPACE metrics, in
combination with DORA metrics, can fill in the productivity measurement gaps by
correlating productivity metrics to business outcomes. McKinsey found that
combining DORA and SPACE metrics with “opportunity-focused” metrics can produce
a well-rounded view of developer productivity. That, in turn, can lead to
positive outcomes, as McKinsey reports: 20% to 30% reduction in
customer-reported product defects, 20% improvement in employee experience scores
and 60% improvement in customer satisfaction ratings.
Metadata Governance: Crucial to Managing IoT
Governance of metadata requires formalization and agreement among stakeholders,
based on existing Data Governance processes and activities. Through this
program, business stakeholders engage in conversations to agree on what the data
is and its context, generating standards around organizational metadata. The
organization sees the results in a Business Glossary or data catalog. In
addition to Data Governance tools, IT tools significantly contribute to metadata
generation and usage, tracking updates, and collecting data. These applications,
often equipped with machine learning capabilities, automate the gathering,
processing, and delivery of metadata to identify patterns within the data
without the need for manual intervention. ... The need for metadata governance
services will emerge through establishing and maintaining this metadata
management program. By setting up and running these services, an organization
can better utilize Data Governance capabilities to collect, select, and edit
metadata. Developing these processes requires time and effort, as metadata
governance needs to adapt to the organization’s changing needs.
CISOs Tackle Compliance With Cyber Guidelines
Operationally, CISOs will need to become increasingly involved with the
organization as a whole -- not just the IT and security teams -- to understand
the company’s overall security dynamics. “This is a much more resource-intensive
process, but necessary until companies find sustainable footing in the new
regulatory landscape,” Tom Kennedy, vice president of Axonius Federal Systems,
explains via email. He points to the SEC disclosure mandate, which requires
registrants to disclose “material cybersecurity incidents”, as a great example
of how private companies are struggling to comply. From his perspective, the
root problem is a lack of clarity within the mandate of what constitutes a
“material” breach, and where the minimum bar should be set when it comes to a
company’s security posture. “As a result, we’ve seen a large variety in
companies’ recent cyber incident disclosures, including both the frequency,
level of detail, and even timing,” he says. ... “The first step in fortifying
your security posture is knowing what your full attack surface is -- you cannot
protect what you don’t know about,” Kennedy says. “CISOs and their teams must be
aware of all systems in their network -- both benign and active -- understand
how they work together, what vulnerabilities they may have.”
AISecOps: Expanding DevSecOps to Secure AI and ML
AISecOps, the application of DevSecOps principles to AI/ML and generative AI,
means integrating security into the life cycle of these models—from design and
training to deployment and monitoring. Continuous security practices, such as
real-time vulnerability scanning and automated threat detection, protection
measures for the data and model repositories, are essential to safeguarding
against evolving threats. One of the core tenets of DevSecOps is fostering a
culture of collaboration between development, security and operations teams.
This multidisciplinary approach is even more critical in the context of
AISecOps, where developers, data scientists, AI researchers and cybersecurity
professionals must work together to identify and mitigate risks. Collaboration
and open communication channels can accelerate the identification of
vulnerabilities and the implementation of fixes. Data is the lifeblood of AI and
ML models. Ensuring the integrity and confidentiality of the data used for
training and inference is paramount. ... Embedding security considerations from
the outset is a principle that translates directly from DevSecOps to AI and ML
development.
Translating Generative AI investments into tangible outcomes
Integration of Generative AI presents exciting opportunities for businesses, but
it also comes with its fair share of risks. One significant concern revolves
around data privacy and security. Generative AI systems often require access to
vast amounts of sensitive data, raising concerns about potential breaches and
unauthorised access. Moreover, there’s the challenge of ensuring the reliability
and accuracy of generated outputs, as errors or inaccuracies could lead to
costly consequences or damage to the brand’s reputation. Lastly, there’s the
risk of over-reliance on AI-generated content, potentially diminishing human
creativity and innovation within the organisation. Navigating these risks
requires careful planning, robust security measures, and ongoing monitoring to
ensure the responsible and effective integration of Generative AI into business
operations. Consider a healthcare organisation that implements Generative AI for
medical diagnosis assistance. In this scenario, the AI system requires access to
sensitive patient data, including medical records, diagnostic tests, and
personal information.
Beyond the table stakes: CISO Ian Schneller on cybersecurity’s evolving role
Schneller encourages his audience to consider the gap between the demand for
cyber talent and the supply of it. “Read any kind of public press,” he says,
“and though the numbers may differ a bit, they’re consistent in that there are
many tens, if not hundreds of thousands of open cyber positions.” In February of
last year, according to Statista, about 750,000 cyber positions were open in the
US alone. According to the World Economic Forum, the global number is about 3.5
million, and according to Cybercrime magazine, the disparity is expected to
persist through at least 2025. As Schneller points out, this means companies
will struggle to attract cyber talent, and they will have to seek it in
non-traditional places. There are many tactics for attracting security
talent—aligning pay to what matters, ensuring that you have clear paths for
advancing careers—but all this sums to a broader point that Schneller
emphasizes: branding. Your organization must convey that it takes cybersecurity
seriously, that it will provide cybersecurity talent a culture in which they can
solve challenging problems, advance their careers, and earn respect,
contributing to the success of the business.
Quantum Computing Demystified – Part 2
Quantum computing’s potential to invalidate current cryptographic standards
necessitates a paradigm shift towards the development of quantum-resistant
encryption methods, safeguarding digital infrastructures against future quantum
threats. This scenario underscores the urgency in fortifying cybersecurity
frameworks to withstand the capabilities of quantum algorithms. For
decision-makers and policymakers, the quantum computing era presents a
dual-edged sword of strategic opportunities and challenges. The imperative to
embrace this nascent technology is twofold, requiring substantial investment in
research, development, and education to cultivate a quantum-literate workforce.
... Bridging the quantum expertise gap through education and training is vital
for fostering a skilled workforce capable of driving quantum innovation forward.
Moreover, ethical and regulatory frameworks must evolve in tandem with quantum
advancements to ensure equitable access and prevent misuse, thereby safeguarding
societal and economic interests.
The Comprehensive Evolution Of DevSecOps In Modern Software Ecosystems
The potential for enhanced efficiency and accuracy in identifying and addressing
security vulnerabilities is enormous, even though this improvement is not
without its challenges, which include the possibility of algorithmic errors and
shifts in job duties. Using tools that are powered by artificial intelligence,
teams can prevent security breaches, perform code analysis more efficiently and
automate mundane operations. This frees up human resources to be used for
tackling more complicated and innovative problems. ... When using traditional
software development approaches, security checks were frequently carried out at
a later stage in the development cycle, which resulted in patches that were both
expensive and time-consuming. The DevSecOps methodology takes a shift-left
strategy, which integrates security at the beginning of the development process.
This brings security to the forefront of the process. By incorporating security
into the design and development phases from the beginning, this proactive
technique not only decreases the likelihood of vulnerabilities being discovered
after they have already been discovered, but it also speeds up the development
process.
How Generative AI and Data Management Can Augment Human Interaction with Data
In contrast with ETL processes, logical data management solutions enable
real-time connections to disparate data sources without physically replicating
any data. This is accomplished with data virtualization, a data integration
method that establishes a virtual abstraction layer between data consumers and
data sources. With this architecture, logical data management solutions enable
organizations to implement flexible data fabrics above their disparate data
sources, regardless of whether they are legacy or modern; structured,
semistructured, or unstructured; cloud or on-premises; local or overseas; or
static or streaming. The result is a data fabric that seamlessly unifies these
data sources so data consumers can use the data without knowing the details
about where and how it is stored. In the case of generative AI, where an LLM is
the “consumer,” the LLM can simply leverage the available data, regardless of
its storage characteristics, so the model can do its job. Another advantage of a
data fabric is that because the data is universally accessible, it can also be
universally governed and secured.
Developers don’t need performance reviews
Software development is commonly called a “team sport.” Assessing individual
contributions in isolation can breed unhealthy competition, undermine teamwork,
and incentivize behavior that, while technically hitting the mark, can be
detrimental to good coding and good software. The pressure of performance
evaluations can deter developers from innovative pursuits, pushing them towards
safer paths. And developers shouldn’t be steering towards safer paths. The
development environment is rapidly changing, and developers should be encouraged
to experiment, try new things, and seek out innovative solutions. Worrying about
hitting specific metrics squelches the impulse to try something new. Finally, a
one-size-fits-all approach to performance reviews doesn’t take into account the
unique nature of software development. Using the same system to evaluate
developers and members of the marketing team won’t capture the unique skills
found among developers. Some software developers thrive fixing bugs. Others
love writing greenfield code. Some are fast but less accurate. Others are slower
but highly accurate.
Quote for the day:
''Perseverance is failing nineteen times
and succeeding the twentieth.'' -- Julie Andrews
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