API Mocking Is Essential to Effective Change Management
A constant baseline is essential when managing API updates. Without it, teams
risk diverging from the API’s intended design, resulting in more drift and
potentially disruptive breaking changes. API mocks serve as a baseline by
accurately simulating the API’s intended behavior and data formats. This
enables development and quality assurance teams to compare proposed changes to
a standardized benchmark, ensuring that new features or upgrades adhere to the
API’s specified architecture before deployment. ... A centralized mocking
environment is helpful for teams who have to manage changes over time and
monitor API versions. Teams create a transparent, trusted source of truth from
a centralized environment where all stakeholders may access the mock API,
which forms the basis of version control and change tracking. By making every
team operate from the same baseline in keeping with the desired API behavior
and structure, this centralized approach helps reduce drift. ... Teams that
want to properly use API mocking in change management must include mocking
techniques in their daily development processes. These techniques ensure that
the API’s documented specifications, implementation and testing environments
remain in line, lowering the risk of drift and supporting consistent, open
updates.How Open-Source BI Tools Are Transforming DevOps Pipelines
BI tools automate the tracking of all DevOps processes so one can easily
visualize, analyze, and interpret the key metrics. Rather than manually
monitoring the metrics, such as the percentage ratio of successfully deployed
applications or the time taken to deploy an application, one is now able to
simply rely on BI to spot such trends in the first place. This gives one the
ability to operationalize insights which saves time and ensures that pipelines
are well managed. ... If you are looking for an easy-to-use tool, Metabase is
the best option available. It allows you to build dashboards and query
databases without the need to write elaborate codes. It also allows the user
to retrieve data from a variety of systems, which, from a business
perspective, allows a user to measure KPIs, for example, deployment frequency
or the occurrence of system-related problems. ... If you have big resources
that need monitoring, Superset is perfect. Superset was designed with the
concept of big data loads in mind, offering advanced visualization and
projection technology for different data storage devices. Businesses with
medium-complexity operational structures optimize the usage of Superset thanks
to its state-of-the-art data manipulation abilities.
Reflecting on the disconnect between IT and end users, Dyer says that there
will “always be a disparity between the two classes of employees”. “IT is a
core fundamental dependency to allow end users to perform their roles to the
best of their ability – delivered as a service for which they consume as
customers,” he says. “Users wish to achieve and excel in their employment, and
restrictions of IT can be a negative detractor in doing so. He adds that users
are seldom consciously trying to compromise the security of an organisation,
and that the incompetence in security hygiene is due to a lack of investment,
awareness, engagement or reinforcement. “It is the job of IT leaders to bridge
that gap [and] partner with their respective peers to build a positive
security awareness culture where employees feel empowered to speak up if
something doesn’t look right and to believe in the mission of effectively
securing the organisation from the evolving world of outside and inside
threats.” And to build that culture, Dyer has some advice, such as making
policies clearly defined and user-friendly, allowing employees to do their
jobs using tech to the best of their ability (with an understanding of the
guardrails they have) and instructing them on what to do should something
suspicious happen.
/articles/responsible-ai-fintech/en/smallimage/navigating-responsible-ai-logo-small-1732088592946.jpg)
Cross-functional collaboration is critical to successful, responsible AI
implementation. This requires the engagement of multiple departments,
including security, compliance, legal, and AI governance teams, to
collectively reassess and reinforce risk management strategies within the AI
landscape. Bringing together these diverse teams allows for a more
comprehensive understanding of risks and safeguards across departments,
contributing to a well-rounded approach to AI governance. A practical way to
ensure effective oversight and foster this collaboration is by establishing an
AI review board composed of representatives from each key function. This board
would serve as a centralized body for overseeing AI policy adherence,
compliance, and ethical considerations, ensuring that all aspects of AI risk
are addressed cohesively and transparently. Organizations should also focus on
creating realistic and streamlined processes for responsible AI use, balancing
regulatory requirements with operational feasibility. While it may be tempting
to establish one consistent process, for instance, where conformity
assessments would be generated for every AI system, this would lead to a
significant delay in time to value. Instead, companies should carefully
evaluate the value vs. effort of the systems, including any regulatory
documentation, before proceeding toward production.
IT leaders aren’t just tech wizards, but savvy data merchants. Imagine
yourself as a store owner, but instead of shelves stocked with physical goods,
your inventory consists of valuable data, insights, and AI/ML products. To
succeed, they need to make their data products appealing by understanding
customer needs, ensuring products are current, of a high-quality, and
organized. Offering value-added services on top of data, like analysis and
consulting, can further enhance the appeal. By adopting this mindset and
applying business principles, IT leaders can unlock new revenue streams. ...
With AI becoming more pervasive, the ethical and responsible use of it is
paramount. Leaders must ensure that data governance policies are in place to
mitigate risks of bias or discrimination, especially when AI models are
trained on biased datasets. Transparency is key in AI, as it builds trust and
empowers stakeholders to understand and challenge AI-generated insights. By
building a program on the existing foundation of culture, structure, and
governance, IT leaders can navigate the complexities of AI while upholding
ethical standards and fostering innovation. ... IT leaders need to maintain a
balance of intellectual (IQ) and emotional (EQ) intelligence to manage an
AI-infused workplace.
Inside threats: How can companies improve their cyber hygiene?
Reflecting on the disconnect between IT and end users, Dyer says that there
will “always be a disparity between the two classes of employees”. “IT is a
core fundamental dependency to allow end users to perform their roles to the
best of their ability – delivered as a service for which they consume as
customers,” he says. “Users wish to achieve and excel in their employment, and
restrictions of IT can be a negative detractor in doing so. He adds that users
are seldom consciously trying to compromise the security of an organisation,
and that the incompetence in security hygiene is due to a lack of investment,
awareness, engagement or reinforcement. “It is the job of IT leaders to bridge
that gap [and] partner with their respective peers to build a positive
security awareness culture where employees feel empowered to speak up if
something doesn’t look right and to believe in the mission of effectively
securing the organisation from the evolving world of outside and inside
threats.” And to build that culture, Dyer has some advice, such as making
policies clearly defined and user-friendly, allowing employees to do their
jobs using tech to the best of their ability (with an understanding of the
guardrails they have) and instructing them on what to do should something
suspicious happen.Navigating Responsible AI in the FinTech Landscape
Cross-functional collaboration is critical to successful, responsible AI
implementation. This requires the engagement of multiple departments,
including security, compliance, legal, and AI governance teams, to
collectively reassess and reinforce risk management strategies within the AI
landscape. Bringing together these diverse teams allows for a more
comprehensive understanding of risks and safeguards across departments,
contributing to a well-rounded approach to AI governance. A practical way to
ensure effective oversight and foster this collaboration is by establishing an
AI review board composed of representatives from each key function. This board
would serve as a centralized body for overseeing AI policy adherence,
compliance, and ethical considerations, ensuring that all aspects of AI risk
are addressed cohesively and transparently. Organizations should also focus on
creating realistic and streamlined processes for responsible AI use, balancing
regulatory requirements with operational feasibility. While it may be tempting
to establish one consistent process, for instance, where conformity
assessments would be generated for every AI system, this would lead to a
significant delay in time to value. Instead, companies should carefully
evaluate the value vs. effort of the systems, including any regulatory
documentation, before proceeding toward production.The Future Of IT Leadership: Lessons From INTERPOL
Cyber threats never keep still. The same can be said of the challenges IT leaders face. Historically, IT functions were reactive—fixing problems as they arose. Today, that approach is no longer sufficient. IT leaders must anticipate challenges before they materialise. This proactive stance involves harnessing the power of data, artificial intelligence (AI), and predictive analytics. It is by analysing trends and identifying vulnerabilities that IT leaders can prevent disruptions and position their organisations to respond effectively to emerging risks. This shift from reactive to predictive leadership is essential for navigating the complexities of digital transformation. ... Cybercrime doesn’t respect boundaries, and neither should IT leadership. Successful cybersecurity efforts often rely on partnerships—between businesses, governments, and international organisations. INTERPOL’s Africa Cyber Surge operations demonstrate the power of collaboration in tackling threats at scale. An IT leader needs to adopt a similar mindset by building networks of trust across industries, government agencies, and even with and through competitors. It can help create shared defences against common threats. Besides, collaboration isn’t limited to external partnerships.4 prerequisites for IT leaders to navigate today’s era of disruption
IT leaders aren’t just tech wizards, but savvy data merchants. Imagine
yourself as a store owner, but instead of shelves stocked with physical goods,
your inventory consists of valuable data, insights, and AI/ML products. To
succeed, they need to make their data products appealing by understanding
customer needs, ensuring products are current, of a high-quality, and
organized. Offering value-added services on top of data, like analysis and
consulting, can further enhance the appeal. By adopting this mindset and
applying business principles, IT leaders can unlock new revenue streams. ...
With AI becoming more pervasive, the ethical and responsible use of it is
paramount. Leaders must ensure that data governance policies are in place to
mitigate risks of bias or discrimination, especially when AI models are
trained on biased datasets. Transparency is key in AI, as it builds trust and
empowers stakeholders to understand and challenge AI-generated insights. By
building a program on the existing foundation of culture, structure, and
governance, IT leaders can navigate the complexities of AI while upholding
ethical standards and fostering innovation. ... IT leaders need to maintain a
balance of intellectual (IQ) and emotional (EQ) intelligence to manage an
AI-infused workplace. How to Build a Strong and Resilient IT Bench
Since talent is likely to be short in new technology areas and in older tech
areas that must still be supported, CIOs should consider a two-pronged approach
that develops bench strength talent for new technologies while also ensuring
that older infrastructure technologies have talent waiting in the wings. ...
Companies that partner with universities and community colleges in their local
areas have found a natural synergy with these institutions, which want to ensure
that what they teach is relevant to the workplace. This synergy consists of
companies offering input for computer science and IT courses and also providing
guest lecturers for classes. Those companies bring “real world” IT problems into
student labs and offer internships for course credit that enable students to
work in company IT departments with an IT staff mentor. ... It’s great to send
people to seminars and certification programs, but unless they immediately apply
what they learned to an IT project, they’ll soon forget it. Mindful of this, we
immediately placed newly trained staff on actual IT projects so they could apply
what they learned. Sometimes a more experienced staff member had to mentor them,
but it was worth it. Confidence and competence built quickly.
The Growing Quantum Threat to Enterprise Data: What Next?
One of the most significant implications of quantum computing for cybersecurity
is its potential to break widely used encryption algorithms. Many of the
encryption systems that safeguard sensitive enterprise data today rely on the
computational difficulty of certain mathematical problems, such as factoring
large numbers or solving discrete logarithms. Classical computers would take an
impractical amount of time to crack these encryption schemes, but quantum
computers could theoretically solve these problems in a matter of seconds,
rendering many of today's security protocols obsolete. ... Recognizing the
urgent need to address the quantum threat, the National Institute of Standards
and Technology launched a multi-phase effort to develop post-quantum
cryptographic standards. After eight years of rigorous research and relentless
effort, NIST released the first set of finalized post-quantum encryption
standards on Aug. 13. These standards aim to provide a clear and practical
framework for organizations seeking to transition to quantum-safe cryptography.
The final selection included algorithms for both public-key encryption and
digital signatures, two of the most critical components of modern cybersecurity
systems.
Are we worse at cloud computing than 10 years ago?
Rapid advancements in cloud technologies combined with mounting pressures for
digital transformation have led organizations to hastily adopt cloud solutions
without establishing the necessary foundations for success. This is especially
common if companies migrate to infrastructure as a service without adequate
modernization, which can increase costs and technical debt. ... The growing
pressure to adopt AI and generative AI technologies further complicates the
situation and adds another layer of complexity. Organizations are caught between
the need to move quickly and the requirement for careful, strategic
implementation. ... Include thorough application assessment, dependency mapping,
and detailed modeling of the total cost of ownership before migration begins.
Success metrics must be clearly defined from the outset. ... When it comes to
modernization, organizations must consider the appropriate refactoring and
cloud-native development based on business value rather than novelty. The
overarching goal is to approach cloud adoption as a strategic transformation. We
must stop looking at this as a migration from one type of technology to another.
Cloud computing and AI will work best when business objectives drive technology
decisions rather than the other way around.
A well-structured Data Operating Model integrates data efforts within business
units, ensuring alignment with actual business needs. I’ve seen how a "Hub and
Spoke" model, which places central governance at the core while embedding data
professionals in individual business units, can break down silos. This
alignment ensures that data solutions are built to drive specific business
outcomes rather than operating in isolation. ... Data leaders must ruthlessly
prioritize initiatives that deliver tangible business outcomes. It’s easy to
get caught up in hype cycles—whether it’s the latest AI model or a
cutting-edge data governance framework—but real success lies in identifying
the use cases that have a direct line of sight to revenue or cost savings. ...
A common mistake I’ve seen in organizations is focusing too much on static
reports or dashboards. The real value comes when data becomes actionable —
when it’s integrated into decision-making processes and products. ... Being
"data-driven" has become a dangerous buzzword. Overemphasizing data can lead
to analysis paralysis. The true measure of success is not how much data you
have or how many dashboards you create but the value you deliver to the
business.
Quote for the day:
"Efficiency is doing the thing right. Effectiveness is doing the right thing." -- Peter F. Drucker
Quote for the day:
"Efficiency is doing the thing right. Effectiveness is doing the right thing." -- Peter F. Drucker
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