CIO CIO

“A couple of years ago, we looked at women in tech at Trade Me and listened to their experiences and started delving into the data to see where the problems to solve were. And as a result of that, we kicked off a program. Over a couple of years, we’ve done 28 different things in that program – mentoring, sponsorship, allyship training, unconscious bias training, outreach events and networking events to name a few. We’ve standardised our hiring processes. We’ve made sure that we have 50 per cent women interviewed for key roles where we find there is inequity which is in the technical leadership roles.”  Along with those measures, Morris says that one of the most impactful things they’ve done is taking a gender lens in their performance and pay review cycle to make sure that they are making fair decisions. However despite dozens of initiatives, the company hasn’t moved the needle much on the number of women in technical leadership roles. TradeMe has around 240 people in its tech team and overall the number of women lines up with the industry average at 27 per cent. When it comes to women in tech leadership roles, the organisation had just 11 per cent and through the 28 programs it grew only marginally to 13 per cent. source

Deep understanding of how to monetize data assets 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. Focus on data governance and ethics 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. Ability to embrace both smarts and heart IT leaders need to maintain a balance of intellectual (IQ) and emotional (EQ) intelligence to manage an AI-infused workplace. On the IQ side, leaders need to have a vision for the AI-first world in their organizations and know where it can be used to free up employees so they can spend more time on other complex tasks and enhance productivity. But more importantly, EQ and people-centric skills are critical to evangelize positive impacts, keep people engaged, address anxiety around the changing workforce, and help people reskill to focus on new ways of working and thinking. In fact, with advanced analytics producing vast amounts of data beyond comprehension, softer management skills will be more important than deep subject expertise or raw intelligence. source

We take pride in our heritage at NetApp. Our heritage is rooted in developing innovative solutions that address the challenges of storing, managing, and protecting data in a complex IT environment. From pioneering the NetApp® WAFL® file system to providing a first-party cloud-native storage service with NetApp Cloud Volumes and NetApp Astra™ Trident—one of the first open-source storage orchestrators for containers and Kubernetes distributions—our heritage is built on innovation. We’re leaders in providing cutting-edge solutions that empower companies to transform their businesses. Although generative AI (genAI) is the new kid on the block, businesses have been using predictive AI for years to improve financial forecasting, detect financial fraud by identifying anomalies, and find the most successful treatments for high-risk patients in healthcare. We know because we’ve been helping customers achieve these AI-driven outcomes. However, genAI goes beyond mere pattern recognition, employing advanced modeling techniques to produce fresh, original content. Regardless of whether you believe genAI is overhyped or underhyped, it has the potential to reshape industries, revolutionize how businesses operate, and enhance our lives. With the right tools, individuals can unleash their creativity. Marketing professionals can use genAI to create high-quality video content from simple text; AI-powered music enables artists to craft full audio tracks from prompts like genre, lyrics, or inspirational artists. The applications of AI are vast, diverse, and still growing. All these applications are made possible by one thing: data. Current innovations are rapidly improving these foundation models by using customers’ private data to provide better context or to fine-tune an existing model and make better decisions. But when adopting AI, organizations can struggle to effectively manage and extract value from their ever-growing volumes of data that are scattered and unstructured. At NetApp, we tackle this challenge head-on with an intelligent data infrastructure.  The NetApp intelligent data infrastructure gives you the ability to access any data from any location, maintaining data security, protection, and governance. Our adaptive operations aim to maximize the performance and efficiency of both infrastructure and applications while focusing on cost-effectiveness and sustainability. Together, these capabilities can enhance the productivity of AI workers and help you achieve more successful outcomes for your business. AI projects need an intelligent data infrastructure In the recent Scaling AI Initiatives Responsibly survey, commissioned by NetApp, IDC identified these top reasons for AI initiatives failing: Inability to access data Insufficient data to train models Privacy, compliance, and data governance concerns or requirements Data engineering complexity Untrustworthy or poor-quality data sources AI transformation is often hindered by the inability to access scattered data in siloed storage infrastructure—so it’s harder for AI engineers to train and develop models. NetApp helps companies by supporting the movement, transformation, and preparation of data across hybrid cloud environments using block, file, and object storage solutions. This seamless integration means that data sources for machine learning and AI are readily available, regardless of their location. We understand that AI isn’t just about algorithms and models; it’s about trust, transparency, and ethical use of data. We’re committed to helping you build responsible AI systems that prioritize accountability and privacy. With our data governance and compliance capabilities, your AI initiatives can adhere to regulatory requirements and ethical standards, building stakeholder trust and confidence. NetApp’s classification service automatically tags data to support streamlined data cleansing for both the ingest and inferencing phases of the data pipeline, so the right data is used for queries, and sensitive data is not exposed to the model out of policy. Furthermore, NetApp offers revolutionary cyber-resilience capabilities, including AI/ML embedded in storage to combat ransomware. With our systems, you can detect and mitigate sophisticated cyber threats with increased accuracy and performance. Our Python library of tools makes it easy for developers, data scientists, and data engineers to manage data. For example, teams can create an auditable NetApp Snapshot™ copy of a development workspace for traceability and error detection, or an instant clone of a vector index store to make relevant data instantly available for different queries, without affecting production. However, our dedication to AI extends beyond technology. We believe in the power of partnership to drive innovation and accelerate AI adoption across industries. We collaborate closely with industry leaders like NVIDIA and Google to co-create AI solutions that tackle real-world challenges and deliver tangible business value. For instance, with our genAI toolkit for retrieval-augmented generation (RAG) operations, you can implement RAG operations by connecting proprietary data stored on NetApp volumes with Google’s Vertex AI for faster access to unique insights. Our heritage: Being at the forefront of technology advancements The potential of AI is limitless, and at NetApp, we’re excited to be at the forefront of this transformative journey. By combining our intelligent data management expertise with the power of AI, we empower organizations to unlock new possibilities, drive growth, and shape a better, more intelligent future for all. To explore further, visit our NetApp AI Solutions page. Read more about NetApp AI thought leadership perspectives. Read about improving NetApp engineering with GenAI here. If you missed out on our webinar where we talked through the survey results of IDC’s AI maturity model white paper, you can watch it here. source

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data—have begun exploring AI. However, there’s a significant difference between those experimenting with AI and those fully integrating it into their operations. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes. AI enhances organizational efficiency by automating repetitive tasks, allowing employees to focus on more strategic and creative responsibilities. Today, enterprises are leveraging various types of AI to achieve their goals. Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models. To fully benefit from AI, organizations must take bold steps to accelerate the time to value for these applications. This is where Operational AI comes into play. Operational AI involves applying AI in real-world business operations, enabling end-to-end execution of AI use cases. It integrates AI into business processes, processes real-time data, and provides actionable insights to automate tasks, improve efficiency, and make data-driven decisions. Ultimately, it simplifies the creation of AI models, empowers more employees outside the IT department to use AI, and scales AI projects effectively. Adopting Operational AI Organizations looking to adopt Operational AI must consider three core implementation pillars: people, process, and technology. People: To implement a successful Operational AI strategy, an organization needs a dedicated ML platform team to manage the tools and processes required to operationalize AI models. This team serves as the primary point of contact when issues arise with models—the go-to experts when something isn’t working. The team should be structured similarly to traditional IT or data engineering teams. Just as DevOps has become an effective model for organizing application teams, a similar approach can be applied here through machine learning operations, or “MLOps,” which automates machine learning workflows and deployments. Process: To build confidence in the reliability of an organization’s AI implementation, it’s essential to standardize the processes and best practices for deploying models into production. For example, there should be a clear, consistent procedure for monitoring and retraining models once they are running (this connects with the People element mentioned above). As organizations integrate more AI into their operations and expand their use cases, standardizing these practices helps maintain a high level of confidence in both the methods and the models. Technology: The workloads a system supports when training models differ from those in the implementation phase. While in the experimentation phase, speed is a priority, the implementation phase requires more attention to resiliency, availability, and compatibility with other tools. For this reason, organizations looking to leverage Operational AI need an Operational AI platform that specifically supports the requirements for operationalizing, managing and monitoring models in production.  Operational AI offers organizations significant benefits, including time and cost savings, and critical competitive advantages in today’s business landscape. Key benefits of Operational AI include: Increased efficiency through task automation Improved service delivery Reduced time to market for new AI models Lower operational costs Enhanced decision-making capabilities Additionally, Operational AI provides greater oversight of AI models, which is crucial for regulated industries that must diligently manage risk. However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture. These advanced architectures offer the flexibility and visibility needed to simplify data access across the organization, break down silos, and make data more understandable and actionable. They support the integration of diverse data sources and formats, creating a cohesive and efficient framework for data operations. Ensuring effective and secure AI implementations demands continuous adaptation and investment in robust, scalable data infrastructures. Bringing Operational AI to Enterprises In an effort to address the biggest hurdles in AI deployments by enabling organizations to effectively build, operationalize, monitor, secure, and scale models across the enterprise, Cloudera acquired Verta’s Operational AI Platform and team, deepening its intellectual property and adding even more talent to better serve its customers with unmatched expertise and innovative solutions. In leveraging Verta’s platform, Cloudera is now equipped to simplify the process of bolstering customers’ private datasets to build custom retrieval-augmented generation (RAG) and fine-tuning ​applications​. As a result, ​developers — regardless of their expertise in machine learning — will be able to develop and optimize business-ready large language models (LLMs). The Verta Operational AI platform supports production AI-ML workloads in the most complex IT environments. The Verta Model Catalog, Model Operations, and GenAI Workbench have helped customers ranging from AI startups to Fortune 100 enterprises seamlessly manage, run, and govern AI-ML models on-prem and in the cloud.  Adopting an Operational AI mindset helps organizations fully leverage AI benefits across their companies. It’s the difference between a handful of AI success stories and reaching the point where the whole enterprise is running on intelligence. Learn more about how Cloudera can support your enterprise AI journey here. source

Cultivating sustainability Wagh Bakri is leveraging technology to promote sustainable practices, both in cultivation and packaging. Tarun elucidates, “At Wagh Bakri, the majority of our tea is sourced from certified gardens only. During production, sustainability practices are adhered to as a compliance practice, with all plastics that are consumed in packaging and production being recycled using environment-friendly methods. We have also set up huge solar plants so that the majority of the electricity we consume is sourced from renewable sources only, which is a huge win in our sustainability efforts.” Change management with technology The era when a CDIO’s role was only that of a technological leader is long past. In an organization like Wagh Bakri, where IT practices need to be as sound as the business itself, Tarun and his team work to provide value addition through competent change management within the organization. The technology revolution within the organization is gaining a lot of support from internal stakeholders, confirms Tarun. His efforts are oriented towards business alignment, not just technology in a silo. “Ultimately, there is no replacement for the human X-factor, the creativity and intelligence that a person can bring,” concludes Tarun Vijh. source

IT modernization is a necessity for organizations aiming to stay competitive. However, the journey toward modernization has significant hurdles. Understanding these barriers and implementing effective solutions is crucial for the journey. Here, we explore the key factors impeding IT modernization and provide recommendations to overcome them (with real-world illustrations of strategies). Legacy systems and technical debt Barrier: Legacy systems, often deeply embedded in an organization’s operations, pose a significant challenge to IT modernization. These outdated systems are not only costly to maintain but also hinder the integration of new technologies, agility, and business value delivery. Solution: A phased approach to modernization can mitigate the risks associated with legacy systems. For instance, Capital One successfully transitioned from mainframe systems to a cloud-first strategy by gradually migrating critical applications to Amazon Web Services (AWS). It adopted a microservices architecture to decouple legacy components, allowing for incremental updates without disrupting the entire system. Additionally, leveraging cloud-based solutions reduced the burden of maintaining on-premises infrastructure. Budget constraints Barrier: IT modernization requires substantial investment, and budget constraints are a common hurdle. Organizations often struggle to justify the upfront costs of modernization projects, especially when the ROI is not immediately apparent. Solution: To address budget constraints, organizations should adopt a strategic approach to funding IT modernization. For example, some clients explore alternative funding models such as opex through cloud services (rather than traditional capital expensing), which spread costs over time. Also communicating clearly how the debt impacts business strategy and goals in an executive-friendly fashion is key. When trying to rationalize investing, IT often makes the mistake of using language that doesn’t resonate with business peers, which makes it difficult to get consensus on funding. Skill gaps and workforce resistance Barrier: The rapid evolution of technology has created a significant skills gap in the IT workforce. Many organizations lack the expertise required to implement and manage modern IT solutions. Furthermore, resistance to change among employees can impede modernization efforts. Solution: Invest in continuous learning and development programs to upskill the existing workforce. For instance, AT&T launched a comprehensive reskilling initiative called “Future Ready” to train employees in emerging technologies such as cloud computing, cybersecurity, and data analytics. The compay fostered a culture of innovation by involving employees in the modernization process and addressing their concerns. Change management strategies, including clear communication and incentives, helped overcome resistance and drive adoption. Security and compliance concerns Barrier: Modernizing IT systems often involves handling sensitive data and integrating with external platforms, raising security and compliance concerns. Organizations fear that new technologies may introduce vulnerabilities and complicate regulatory compliance. Solution: Implement a robust security framework that includes regular risk assessments, threat modeling, and continuous monitoring. For example, a financial services firm adopted a zero trust security model to ensure that every access request is authenticated and authorized. It collaborated with compliance experts to ensure that modernization efforts adhered to industry regulations and standards and leveraged automated compliance tools to streamline the process and reduce the risk of human error. Integration challenges Barrier: Integrating new technologies with existing systems is a complex task that can lead to compatibility issues and operational disruptions. The lack of standardized protocols and interfaces further complicates integration efforts. Solution: Embrace API-driven integration to facilitate seamless communication between disparate systems. For instance, an entertainment-driven technology organization used middleware solutions to bridge the gap between legacy and modern applications, enabling it to scale its services globally. Besides enabling scaling, the middleware also allowed the company to “pick off” the legacy services rather than forcing a riskier, big-bang approach to modernizing them. Finally, adopting a modular approach to system design enhanced flexibility and simplified future integrations. Cultural barriers and process fossilization Barrier: Organizational culture and structure significantly impact the success of IT modernization initiatives. The technology is relatively easy when compared with shifting the habits and relationships among staff. Siloed departments, lack of collaboration, and resistance to change are common cultural barriers. As are outdated or inefficient processes being shoehorned into the modernized environment, because of a “this is how we do business here,” mentality. This leads to an old consulting joke. Q: What do you get when you add new technology to bad processes? A: Expensive, bad processes. Solution: Modernization is not simply updating technology; to be successful, it must change work habits and structures. Promote a culture of collaboration and innovation by breaking down silos and encouraging cross-functional teams. Also, reexamine current practices and processes. For example, Microsoft restructured its organization to foster a more collaborative environment, which was crucial for its successful transition to a cloud-first strategy. Establishing clear governance structures to oversee modernization efforts ensured alignment with business objectives. Leadership played a crucial role in driving cultural change, with executives championing modernization initiatives and leading by example. Conclusion IT modernization is complex but, if done mindfully and addressing the key barriers — legacy systems, budget constraints, skill gaps, security concerns, integration challenges, and cultural resistance and process fossilization — organizations can pave the way for a successful transformation. Learn more about IDC’s research for technology leaders OR subscribe today to receive industry-leading research directly to your inbox. International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and sourcing intelligence data from the industry’s most experienced advisors. Contact us today to learn more. Daniel Saroff is group vice president of consulting and research at IDC, where he is a senior practitioner in the end-user consulting practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization’s information technology. IDC’s end-user consulting practice utilizes IDC’s extensive

The introduction of Oracle Alloy into the UAE’s public sector is part of Du Tech’s broader strategy to address the growing demand for sovereign AI services. The potential benefits of sovereign AI for the UAE’s public sector are substantial. In particular, AI’s integration into government services will streamline and improve efficiencies across multiple sectors. Al Awadi points to several key areas where AI will have an immediate impact, such as client-facing channels like chatbots, call center analytics, and other AI-powered tools that will enhance customer service and public interaction. These technologies are expected to improve service delivery and ensure that citizens receive faster, more personalized assistance from government entities. However, with the rapid adoption of AI and cloud technologies, concerns over security and data privacy are paramount. Du Tech has made it clear that security is their top priority, particularly when dealing with government data. As Al Awadi explained, Du Tech operates its own data centers, giving the company full control over its cloud infrastructure and ensuring it complies with all UAE national laws regarding data sovereignty. “We are getting all the necessary approvals to provide a secured infraestructure,” he added. Looking toward the future, Al Awadi sees AI playing a pivotal role in the continued evolution of the UAE’s public sector. He believes that AI’s integration will not only improve existing services but also pave the way for new innovations that will reshape how the government interacts with its citizens. “AI is big in the UAE. The leadership has embraced AI, and it’s reflected in the initiatives that are being worked on across various federal and governmental entities,” Al Awadi said. source

The difficulties of finding and holding onto tech talent are multiplying, especially with AI in play. It can help reduce turnover and be integrated into a strategy based on smart working, continuous training, innovation, and welfare measures. But keeping AI in check is a discipline unto itself. source

Standardize tagging to unify reporting As mentioned, FinOps teams are often small; as such, improving how teams access data asynchronously and communicate with a common lexicon is vital. One area worth focusing on is your tagging taxonomy, Cloudbolt’s Campos says. To truly collaborate, security and FinOps teams must come into alignment on taxonomy standardization down to the cloud workload, he says. This standardization lets both teams view the same reports, alerts, and response patterns. In Campos’ experience, organization silos first manifest in data structures, then leak into behaviors and lack of communication, often resulting in work that overlaps without knowing it. Moreover, security tooling often provides earlier detection of issues compared to FinOps tooling, which often delays data visibility longer, Campos says. All the more reasons to get security and FinOps teams on the same page, with the same lexicon, to ensure they can leverage each other’s work and tools to the benefit of the enterprise as a whole. source