Best Practices for Managing Hybrid Cloud Data Governance
Kausik Chaudhuri, CIO of Lemongrass, explains monitoring in hybrid-cloud
environments requires a holistic approach that combines strategies, tools, and
expertise. “To start, a unified monitoring platform that integrates data from
on-premises and multiple cloud environments is essential for seamless
visibility,” he says. End-to-end observability enables teams to understand the
interactions between applications, infrastructure, and user experience, making
troubleshooting more efficient. ... Integrating legacy systems with modern data
governance solutions involves several steps. Modern data governance systems,
such as data catalogs, work best when fueled with metadata provided by a range
of systems. “However, this metadata is often absent or limited in scope within
legacy systems,” says Elsberry. Therefore, an effort needs to be made to create
and provide the necessary metadata in legacy systems to incorporate them into
data catalogs. Elsberry notes a common blocking issue is the lack of REST API
integration. Modern data governance and management solutions typically have an
API-first approach, so enabling REST API capabilities in legacy systems can
facilitate integration. “Gradually updating legacy systems to support modern
data governance requirements is also essential,” he says.
These Founders Are Using AI to Expose and Eliminate Security Risks in Smart Contracts
The vulnerabilities lurking in smart contracts are well-known but often
underestimated. “Some of the most common issues include Hidden Mint functions,
where attackers inflate token supply, or Hidden Balance Updates, which allow
arbitrary adjustments to user balances,” O’Connor says. These aren’t isolated
risks—they happen far too frequently across the ecosystem. ... “AI allows us to
analyze huge datasets, identify patterns, and catch anomalies that might
indicate vulnerabilities,” O’Connor explains. Machine learning models, for
instance, can flag issues like reentrancy attacks, unchecked external calls, or
manipulation of minting functions—and they do it in real-time. “What sets AI
apart is its ability to work with bytecode,” he adds. “Almost all smart
contracts are deployed as bytecode, not human-readable code. Without advanced
tools, you’re essentially flying blind.” ... As blockchain matures, smart
contract security is no longer the sole concern of developers. It’s an
industry-wide challenge that impacts everyone, from individual users to large
enterprises. DeFi platforms increasingly rely on automated tools to monitor
contracts and secure user funds. Centralized exchanges like Binance and Coinbase
assess token safety before listing new assets.
Three best change management practices to take on board in 2025
For change management to truly succeed, companies need to move from being
change-resistant to change-ready. This means building up "change muscles" --
helping teams become adaptable and comfortable with change over the long term.
For Mel Burke, VP of US operations at Grayce, the key to successful change is
speaking to both the "head" and the "heart" of your stakeholders. Involve
employees in the change process by giving them a voice and the ability to
shape it as it happens. ... Change management works best when you focus on the
biggest risks first and reduce the chance of major disruptions. Dedman calls
this strategy "change enablement," where change initiatives are evaluated and
scored on critical factors like team expertise, system dependencies, and
potential customer impact. High-scorers get marked red for immediate
attention, while lower-risk ones stay green for routine monitoring to keep the
process focused and efficient. ... Peter Wood, CTO of Spectrum Search, swears
by creating a "success signals framework" that combines data-driven metrics
with culture-focused indicators. "System uptime and user adoption rates are
crucial," he notes, "but so are team satisfaction surveys and employee
retention 12-18 months post-change."
Corporate Data Governance: The Cornerstone of Successful Digital Transformation
While traditional data governance focuses on the continuous and tactical
management of data assets – ensuring data quality, consistency, and security –
corporate data governance elevates this practice by integrating it with the
organization’s overall governance framework and strategic objectives. It
ensures that data management practices are not operating in silos but are
harmoniously aligned and integrated with business goals, regulatory
requirements, and ethical standards. In essence, corporate data governance
acts as a bridge between data management and corporate strategy, ensuring that
every data-related activity contributes to the organization’s mission and
objectives. ... In the digital age, data is a critical asset that can drive
innovation, efficiency, and competitive advantage. However, without proper
governance, data initiatives can become disjointed, risky, and misaligned with
corporate goals. Corporate data governance ensures that data management
practices are strategically integrated with the organization’s mission,
enabling businesses to leverage data confidently and effectively. By focusing
on alignment, organizations can make better decisions, respond swiftly to
market changes, and build stronger relationships with customers.
What is an IT consultant? Roles, types, salaries, and how to become one
Because technology is continuously changing, IT consultants can provide
clients with the latest information about new technologies as they become
available, recommending implementation strategies based on their clients’
needs. As a result, for IT consultants, keeping the pulse of the technology
market is essential. “Being a successful IT consultant requires knowing how to
walk in the shoes of your IT clients and their business leaders,” says Scott
Buchholz, CTO of the government and public services sector practice at
consulting firm Deloitte. A consultant’s job is to assess the whole situation,
the challenges, and the opportunities at an organization, Buchholz says. As an
outsider, the consultant can see things clients can’t. ... “We’re seeing the
most in-demand types of consultants being those who specialize in
cybersecurity and digital transformation, largely due to increased reliance on
remote work and increased risk of cyberattacks,” he says. In addition,
consultants with program management skills are valuable for supporting
technology projects, assessing technology strategies, and helping
organizations compare and make informed decisions about their technology
investments, Farnsworth says.
Blockchain + AI: Decentralized Machine Learning Platforms Changing the Game
Tech giants with vast computing resources and proprietary datasets have long
dominated traditional AI development. Companies like Google, Amazon, and
Microsoft have maintained a virtual monopoly on advanced AI capabilities,
creating a significant barrier to entry for smaller players and independent
researchers. However, the introduction of blockchain technology and
cryptocurrency incentives is rapidly changing this paradigm. Decentralized
machine learning platforms leverage blockchain's distributed nature to create
vast networks of computing power. These networks function like a global
supercomputer, where participants can contribute their unused computing
resources in exchange for cryptocurrency tokens. ... The technical
architecture of these platforms typically consists of several key components.
Smart contracts manage the distribution of computational tasks and token
rewards, ensuring transparent and automatic execution of agreements between
parties. Distributed storage solutions like IPFS (InterPlanetary File System)
handle the massive datasets required for AI training, while blockchain
networks maintain an immutable record of transactions and model provenance.
DDoS Attacks Surge as Africa Expands Its Digital Footprint
A larger attack surface, however, is not the only reason for the increased
DDoS activity in Africa and the Middle East, Hummel says. "Geopolitical
tensions in these regions are also fueling a surge in hacktivist activity as
real-world political disputes spill over into the digital world," he says.
"Unfortunately, hacktivists often target critical infrastructure like
government services, utilities, and banks to cause maximum disruption." And
DDoS attacks are by no means the only manifestation of the new threats that
organizations in Africa are having to contend with as they broaden their
digital footprint. ... Attacks on critical infrastructure and financially
motived attacks by organized crime are other looming concerns. In the center's
assessment, Africa's government networks and networks belonging to the
military, banking, and telecom sectors are all vulnerable to disruptive
cyberattacks. Exacerbating the concern is the relatively high potential for
cyber incidents resulting from negligence and accidents. Organized crime gangs
— the scourge of organizations in the US, Europe, and other parts of the
world, present an emerging threat to organizations in Africa, the Center has
assessed.
Optimizing AI Workflows for Hybrid IT Environments
Hybrid IT offers flexibility by combining the scalability of the cloud with
the control of on-premises resources, allowing companies to allocate their
resources more precisely. However, this setup also introduces complexity.
Managing data flow, ensuring security, and maintaining operational efficiency
across such a blended environment can become an overwhelming task if not
addressed strategically. To manage AI workflows effectively in this kind of
setup, businesses must focus on harmonizing infrastructure and resources. ...
Performance optimization is crucial when running AI workloads across hybrid
environments. This requires real-time monitoring of both on-premises and cloud
systems to identify bottlenecks and inefficiencies. Implementing performance
management tools allows for end-to-end visibility of AI workflows, enabling
teams to proactively address performance issues before they escalate. ...
Scalability also supports agility, which is crucial for businesses that need
to grow and iterate on AI models frequently. Cloud-based services, in
particular, allow teams to experiment and test AI models without being
constrained by on-premises hardware limitations. This flexibility is essential
for staying competitive in fields where AI innovation happens rapidly.
The Cloud Back-Flip
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Cloud repatriation is driven by various factors, including high cloud bills,
hidden costs, complexity, data sovereignty, and the need for greater data
control. In markets like India—and globally—these factors are all relevant
today, points out Vishal Kamani – Cloud Business Head, Kyndryl India.
“Currently, rising cloud costs and complexity are part of the ‘learning curve’
for enterprises transitioning to cloud operations.” ... While cloud
repatriation is not an alien concept anymore, such reverse migration back to
on-premises data centres is seen happening only in organisations that are
technology-driven and have deep tech expertise, observes Gaurang Pandya,
Director, Deloitte India. “This involves them focusing back on the basics of
IT infrastructure which does need a high number of skilled employees. The
major driver for such reverse migration is increasing cloud prices and
performance requirements. In an era of edge computing and 5G, each end system
has now been equipped with much more computing resources than it ever had.
This increases their expectations from various service providers.” Money is a
big reason too- especially when you don’t know where is it going.
Why Great Programmers fail at Engineering
Being a good programmer is about mastering the details — syntax, algorithms,
and efficiency. But being a great engineer? That’s about seeing the bigger
picture: understanding systems, designing for scale, collaborating with teams,
and ultimately creating software that not only works but excels in the messy,
ever-changing real world. ... Good programmers focus on mastering their tools
— languages, libraries, and frameworks — and take pride in crafting solutions
that are both functional and beautiful. They are the “builders” who bring
ideas to life one line of code at a time. ... Software engineering requires a
keen understanding of design principles and system architecture. Great code in
a poorly designed system is like building a solid wall in a crumbling house —
it doesn’t matter how good it looks if the foundation is flawed. Many
programmers struggle to:Design systems for scalability and maintainability.
Think in terms of trade-offs, such as performance vs. development speed. Plan
for edge cases and future growth. Software engineering is as much about people
as it is about code. Great engineers collaborate with teams, communicate ideas
clearly, and balance stakeholder expectations. ... Programming success is
often measured by how well the code runs, but engineering success is about how
well the system solves a real-world problem.
Quote for the day:
"Ambition is the path to success.
Persistence is the vehicle you arrive in." -- Bill Bradley
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