Solving the tech debt problem while staying competitive and secure
Technical debt often stems from the costs of running and maintaining legacy
technology services, especially older applications. It typically arises when
organizations make short-term sacrifices or use quick fixes to address immediate
needs without ever returning to resolve those temporary solutions. For CIOs,
balancing technical debt with other strategic priorities is a constant
challenge. They must decide whether to invest resources in high-profile areas
like AI and security or to prioritize reducing technical debt. ... CIOs should
invest in robust cybersecurity measures, including advanced threat detection,
response capabilities, and employee training. Maintaining software updates and
implementing multifactor authentication (MFA) and encryption will further
strengthen an organization’s defenses. However, technical debt can significantly
undermine these cybersecurity efforts. Legacy systems and outdated software can
have vulnerabilities waiting to be exploited. Additionally, technical debt is
often represented by multiple, disparate tools acquired over time, which can
hinder the implementation of a cohesive security strategy and increase
cybersecurity risk.
How to Create a Data-Driven Culture for Your Business
With businesses collecting more data than ever, for data analysts it can be more
like scrounging through the bins than panning for gold. “Hiring data scientists
is outside the reach of most organizations but that doesn't mean you can’t use
the expertise of an AI agent,” Callens says. Once a business has a handle on
which metrics really matter, the rest falls into place, organizations can define
objectives and then optimize data sources. As the quality of the data improves
the decisions are better informed and the outcomes can be monitored more
effectively. Rather than each decision acting in isolation it becomes a positive
feedback loop where data and decisions are inextricably linked: At that point
the organization is truly data driven. Subramanian explains that changing the
culture to become more data-driven requires top-down focus. When making
decisions stakeholders should be asked to provide data justification for their
choices and managers should be asked to track and report on data metrics in
their organizations. “Have you established tracking of historical data metrics
and some trend analysis?” she says. “Prioritizing data in decision making will
help drive a more data-driven culture.”
How Prompt Engineering Can Support Successful AI Projects
Central to the technology is the concept of foundation models, which are rapidly
broadening the functionality of AI. While earlier AI platforms were trained on
specific data sets to produce a focused but limited output, the new approach
throws the doors wide open. In simple — and somewhat unsettling — terms, a
foundation model can learn new tricks from unrelated data. “What makes these new
systems foundation models is that they, as the name suggests, can be the
foundation for many applications of the AI model,” says IBM. “Using
self-supervised learning and transfer learning, the model can apply information
it’s learnt about one situation to another.” Given the massive amounts of data
fed into AI models, it isn’t surprising that they need guidance to produce
usable output. ... AI models benefit from clear parameters. One of the most
basic is length. OpenAI offers some advice: “The targeted output length can be
specified in terms of the count of words, sentences, paragraphs, bullet points,
etc. Note however that instructing the model to generate a specific number of
words does not work with high precision. The model can more reliably generate
outputs with a specific number of paragraphs or bullet points.”
Effective Strategies To Strengthen Your API Security
To secure your organisation, you have to figure out where your APIs are, who’s
using them and how they are being accessed. This information is important as API
deployment increases your organisation’s attack surface making it more
vulnerable to threats. The more exposed they are, the greater the chance a
sneaky attacker might find a vulnerable spot in your system. Once you’ve
pinpointed your APIs and have full visibility of potential points of access, you
can start to include them in your vulnerability management processes. By
proactively identifying vulnerabilities, you can take immediate action against
potential threats. Skipping this step is like leaving the front door wide open.
APIs give businesses the power to automate the process and boost operational
efficiency. But here’s the thing: with great convenience comes potential
vulnerabilities that malicious actors could exploit. If your APIs are
internet-facing, then it’s important to put in place rate-limiting to control
requests and enforce authentication for every API interaction. This helps take
the guesswork out of who gets access to what data through your APIs. Another key
measure is using the cryptographic signing of requests.
The Time is Now for Network-as-a-Service (NaaS)
As the world’s networking infrastructure has evolved, there is now far more
private backbone bandwidth available. Like all cloud solutions, NaaS also
benefits from significant ongoing price/performance improvements in commercial
hardware. Combined with the growing number of carrier-neutral colocation
facilities, NaaS providers simply have many more building blocks to assemble
reliable, affordable, any-to-any connectivity for practically any location. The
biggest changes derive from the advanced networking and security approaches that
today’s NaaS solutions employ. Modern NaaS solutions fully disaggregate control
and data planes, hosting control functions in the cloud. As a result, they
benefit from practically unlimited (and inexpensive) cloud computing capacity to
keep costs low, even as they maintain privacy and guaranteed performance. Even
more importantly, the most sophisticated NaaS providers use novel metadata-based
routing techniques and maintain end-to-end encryption. These providers have no
visibility into enterprise traffic; all encryption/decryption happens only under
the business’ direct control.
Criticality in Data Stream Processing and a Few Effective Approaches
With the advancement of stream processing engines like Apache Flink, Spark,
etc., we can aggregate and process data streams in real time, as they handle
low-latency data ingestion while supporting fault tolerance and data processing
at scale. Finally, we can ingest the processed data into streaming databases
like Apache Druid, RisingWave, and Apache Pinot for querying and analysis.
Additionally, we can integrate visualization tools like Grafana, Superset, etc.,
for dashboards, graphs, and more. This is the overall high-level data streaming
processing life cycle to derive business value and enhance decision-making
capabilities from streams of data. Even with its strength and speed, stream
processing has drawbacks of its own. A couple of them from a bird's eye view are
confirming data consistency, scalability, maintaining fault-tolerance, managing
event ordering, etc. Even though we have event/data stream ingestion frameworks
like Kafka, processing engines like Spark, Flink, etc, and streaming databases
like Druid, RisingWave, etc., we encounter a few other challenges if we drill
down more
Understanding the Impact of AI on Cloud Spending and How to Harness AI for Enhanced Cloud Efficiency
The real magic happens when AI unlocks advanced capabilities in cloud services.
By crunching real-time data, AI transforms how businesses operate, making them
more agile and strategic in their approaches. Businesses can gain better
scalability, run operations more efficiently, and make smarter, data-driven
decisions – all thanks to AI. One of the biggest advantages of AI in the cloud
is how it helps companies scale up smoothly. By using AI-driven solutions,
businesses can predict future demands and optimise resource allocation
accordingly. This means they can handle increased workloads without massive
infrastructure overhauls, which is crucial for staying nimble and competitive.
Scaling AI in cloud computing isn’t without its challenges, though. It requires
strategic approaches like getting leadership buy-in, establishing clear ROI
metrics, and using responsible AI algorithms. These steps ensure that AI
integration not only scales operations but also does so efficiently and with
minimal disruption. AI algorithms continuously monitor workload patterns and can
make recommendations on adjusting resource allocations accordingly.
Blockchain Technology and Modern Banking Systems
“Zumo's innovative approach to integrating digital assets into traditional
banking systems leverages APIs to simplify the process.” As Nick Jones explains,
its Crypto Invest solution offers a digital asset custody and exchange service
that can be seamlessly incorporated into a bank's existing IT infrastructure.
“This provides consumer-facing retail banks with a compliance-focused route to
offer their customers the option to invest in digital assets,” says Nick. By
doing so, banks can generate new revenue streams, enabling customers to buy,
hold and sell crypto within the familiar confines of their own banking platform.
Recognising the regulatory and operational challenges faced by banks, Nick Jones
believes in developing a sustainable and long-term approach, with a focus on
delivering the necessary infrastructure. For banks to confidently integrate
digital asset propositions into their business models, they must address the
financial, operational and environmental sustainability of the project.
Similarly, Kurt Wuckert highlights the feasibility of a hybrid approach for
banks, where blockchain solutions are introduced gradually alongside existing
systems.
The transformation fallacy
Describing the migration process so far, Jordaan says that they started with
some of the very critical systems. “One of which was the e-commerce system that
runs 50 percent of our revenue,” he says. “That was significant, and provided
scalability, because we could add more countries into it, and there are events
such as airlines that cancel flights and so our customers would suddenly be
looking for bookings.” After that, it was a long-running program of lifting and
shifting workloads depending on their priority. The remaining data centers are
either “just really complicated” to decommission, or are in the process of being
shut down. By the end of next year, Jordaan expects TUI to have just one or two
data centers. One of the more unique areas of TUI’s business from an IT
perspective is that of the cruise ships. “Cruise ships actually have a whole
data center on board,” Jordaan says. “It has completely separate networks for
the onboard systems, navigation systems, and everything else, because you're in
the middle of the sea. You need all the compute, storage, and networks to run
from a data center.” These systems are being transformed, too. Ships are
deploying satellite connectivity to bring greater Internet connectivity on
board.
AI and Design Thinking: The Dynamic Duo of Product Development
When designing products that incorporate generative AI, it may feel that you are
tipping in the direction of being too technology-focused. You might be tempted
to forego human intuition in order to develop products that embrace AI’s
innovation. Or, you may have a more difficult time discerning what is meant to
be human and what is meant to be purely technical, because AI is such a new and
dynamic field that changes almost weekly. The human/machine duality is precisely
why combining human-centric Design Thinking with the power of Generative AI is
so effective for product development. Design Thinking isn’t merely a method;
it’s a mindset focusing on user needs, iterative learning, and cross-functional
teamwork—all of which are essential for pioneering AI-driven products. ... One
might say that focusing on a solution to a problem, instead of the problem
itself, is quite an empathetic way to approach a problem. Empathy, a cornerstone
of Design Thinking, allows developers to understand their users deeply. ...
While AI is a powerful tool, it’s crucial to maintain ethical standards and
monitor for biases. Generative AI should not be considered a replacement for
human ethics and critical thinking. Instead, use it as a collaborative component
for enhancing creativity and efficiency.
Quote for the day:
"The litmus test for our success as
Leaders is not how many people we are leading, but how many we are
transforming into leaders" -- Kayode Fayemi
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