Technical Debt is Killing Your Business: How a PLM Strategy Helps
Many organizations implicitly tolerate technical debt as a necessary
investment to adapt to changing circumstances or swiftly seizing new
opportunities. Successful businesses stress the importance of managing
technical debt through acceptance, measurement and proactive strategies,
including the adoption of open standards, abstraction and incremental changes.
... Defining and adopting an effective PLM strategy is instrumental in
managing technical debt comprehensively. A 2020 McKinsey study titled “Tech
Debt: Reclaiming Tech Equity” highlighted the importance of strategic
alignment, stating that, “A degree of technical debt is an unavoidable cost of
doing business, and it needs to be managed appropriately to ensure an
organization’s long-term viability." Furthermore, the study emphasized that
“the goal is not to reach zero technical debt. That would involve devoting all
resources to remediation rather than building points of competitive
differentiation. It would also make it difficult to expedite IT development
when strategic or risk considerations require it. Rather, companies should
work to size, value and control their technical debt and regularly communicate
it to the business.”
Improving the case for waste from data centers
The challenge originally stems from the practical complexities of collecting
and harnessing residual heat from data centers. Planning authorities actively
encourage heat reclamation, but the lack of existing infrastructure poses a
significant obstacle. While planning conditions that mandate developers to
allow for connections to ‘future’ heating networks is a positive move, this
becomes futile where there is no corresponding plan for heat network
development. Developers comply with the condition out of an obligation to meet
regulatory requirements rather than in genuine expectation of the
infrastructure ever being used. From the perspective of data center operators,
investing in the infrastructure only makes sense when it generates Operational
Expenditure (OpEx) savings through the reduced power and water consumption.
However, the misalignment in load profiles complicates this matter. As the
heating network’s demands peak in winter whilst reducing in summer, the data
center operates the opposite way, as it can take advantage of ‘free cooling’
during the colder months. This misalignment in load profiles also impacts the
ESCos.
The rise of observability and why it matters to your business
Automation is a two-edged sword. It’s one of those alluring concepts, but
there’s real caution around trusting machines to judge what actions should and
shouldn’t be taken and when. So given the sensitive nature of change
management, we would expect this trend to continue to lean toward AI-led
automation, but it will take some time before humans are mostly out of the
loop. Moreover, while many vendors claim to have AI, there’s a wide spectrum
of capabilities, and customers should be very cautious about vendor claims in
this regard. Now, not surprisingly, the regulated industries of financial
services, healthcare and government see a much lower tendency to be mostly-AI
led in this context over the next year (well under 5% say mostly AI-led in
this chart), whereas industries such as energy and high tech are much more
likely to adopt AI aggressively in this space. Interestingly, the data show
that senior managements are more likely to push for AI adoption whereas the
practitioners, who literally have their jobs on the line – that is, machines
replacing humans or getting fired for implementing rogue automation – are much
less optimistic.
Innovate to elevate: Blueprint for business excellence in 2024 and beyond
The upcoming year promises an exciting development in the form of GenAI, which
will be integrated into everyday applications such as search engines, office
software, design tools, and communication platforms. This integration will
reveal its full potential as a super-smart hyper-automation engine. With the
ability to take over routine tasks, including information retrieval,
scheduling, compliance management, and project organization, individuals will
be able to boost their productivity and efficiency. As per a report,
hyper-automation, combined with other technologies, can automate work
activities that currently occupy 60-70% of employees’ time by 2024. This
development offers immense value to sectors such as software engineering,
R&D, customer operations, marketing, and sales, making it an indispensable
part of the IT industry. In this rapidly evolving world, organizations are
constantly searching for ways to enhance customer service and drive growth.
One of the most promising ways to achieve this is by embracing
hyper-automation technologies such as AI-powered tools, Natural Language
Processing (NLP), chatbots, and virtual assistants.
4 ways robotics, AI will transform industry in 2024
The future of manufacturing is intricately linked to IT/OT integration as data
will underpin innovation and efficiency. Research shows that the manufacturing
industry has been at the forefront of adopting cloud-based software services
and we are already seeing some customers use these to enhance quality, cost
efficiency, and predictability. That makes me confident that 2024 will see the
growth of data-driven logistics and manufacturing systems. Many still have an
outdated view of the cloud as merely being a data collector and backup
function, as we know it from our private lives. But the real potential and
power don’t lie in storing data or even in linking machines. The real
transformative leap comes when cloud-based software services connect humans
and machines and help manufacturers simplify complex processes and make
smarter decisions. The benefits of this digital evolution are significant.
Remote access to manufacturing data enables quick responses to issues and
continuous automation improvement. With dynamic systems now essential, trusted
cloud technologies offer the latest in security and state-of-the-art services.
Proper Data Management Drives Business Success
Organizations across industries are excited about generative artificial
intelligence (AI) and large language models (LLMs), and for good reason. Tools
like Chat GPT-4 have the potential to transform business and revolutionize how
employees do their jobs, so it’s no surprise that many people are enthusiastic
about implementing them within their organizations. However, LLMs are only as
good as the data on which they are trained. If an organization’s data isn’t
properly sorted, tagged, and secured, the addition of LLMs will not be nearly
as transformative as business leaders hope. Nearly half (45%) of IT leaders
admitted that ineffective and inefficient Data Management means they can’t
leverage emerging technology such as generative AI, which can put them at a
competitive disadvantage. IT leaders must holistically assess the state of
their data practices before implementing generative AI. Only 13% of
respondents reported that Data Management initiatives are their number one
priority, so it’s unsurprising that 77% of the average U.S. company’s data is
redundant, obsolete, or trivial (ROT) or dark data.
Understanding the NSA’s latest guidance on managing OSS and SBOMs
In an effort to provide context and prioritization to downstream product and
software consumers, the guidance recommends suppliers and developers adopt
Vulnerability Exploitability eXchange (VEX) documents to help consumers and
customers know which components are actually impacted by a vulnerability,
which have been resolved, and what should potentially be addressed via
compensating controls. The NSA also recommends suppliers and vendors adopt
attestation processes to demonstrate the secure development of a product
throughout the building, scanning, and packaging of product development and
distribution. This of course is being led by industry efforts such as in-toto
and SSDF and self-attestations when machine-readable artifacts are not
generated and used. This helps provide assurance of not just the components of
an end product but the security of the development process as well. To address
vulnerabilities the NSA recommends using not just CVE and NVD but also other
vulnerability databases such as OSV as well as vulnerability intelligence
sources such as the CISA Known Exploited Vulnerability (KEV) catalog and
Exploit Prediction Scoring System (EPSS).
5 Common Data Science Challenges and Effective Solutions
The upskilling and reskilling of existing data science experts aren’t limited
to technical skills. Data science experts also need enhanced problem-solving
and communication skills. With the massive amount of data now available come
new challenges and problems that need to be addressed. The solutions to these
problems need to be properly communicated to team members and management, who
may or may not have the expertise to interpret data on their own. We’ll
explore this in more detail later. To address the challenge of a smaller pool
of data scientists relative to demand, you just need to stand out as a
potential employer and attract some of those professionals who are part of
that pool. So, offer competitive salaries and benefits. The average base pay
for data scientists in the US is $146,422, according to Glassdoor, and if you
can offer more, better. Whether you hire data scientists or already have data
professionals as employees, you need to invest in data science workshops and
training. These can help ensure your team’s data science skills are attuned to
the times and consider current practices and standards in the data science
industry.
How Observability Strengthens Your Company Culture
Observability breaks down silos and makes collaboration easier across
different clouds, databases, and dashboards seamlessly. For example, an issue
that the DevOps team discovers through observability might lead them to
collaborate with the design team in a way they may never have before. Leaders
should aim to do the same for their teams by fostering greater collaboration
across the entire organization. A lack of effective collaboration and
communication is the top cause of workplace failures, according to 86 percent
of employees and executives. Just as observability is a step up from
monitoring, collaboration is the output that evolves from transparent
communication. Your head of accounting probably knows precisely where each
decimal point needs to be within a spreadsheet and why it needs to be there.
Can they say the same about the IT team’s technology stack or the sales team’s
go-to-market plan? With a culture underpinned by collaboration, employees
won’t just learn how to get along. They’ll understand why each cog in your
machine functions the way it does, as well as the effect of their work on
their fellow employees, the end product, and the business as a whole.
The Third-Party Threat for Financial Organisations
DORA requires financial entities to have robust contracts in place with ICT
service providers. Financial organisations must also maintain a register of
service providers and report on this to the competent authority every year.
The key here is to manage risks. This includes managing the risk of having too
many critical or important functions supported by a small number of service
providers. In addition, DORA requires that financial entities only contract
with providers that “comply with appropriate information security standards”.
Where the ICT service provider supports critical or important functions, the
financial entity must ensure the standards are “the most up-to-date and
highest quality”. ... Unlike the GDPR (General Data Protection Regulation),
DORA does not require that these standards be identified by a specific
authority, so it’s reasonable to assume that ISO 27001 – since it sets the
international benchmark for information security management – would qualify as
such a standard. As Alan mentioned, certifications like ISO 22301 and
Europrivacy™/® add further assurance, as do due diligence checks on suppliers’
resilience, particularly for critical suppliers.
Quote for the day:
"Innovation is taking two things that
already exist and putting them together in a new way." --
Tom Freston
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