CIO CIO

On gen AI trends: A lot of the things we’re looking at are practical examples of where generative AI can help. One simple example is, within our industry, you have lab notebooks where you have to track everything. Historically they were done on paper, so we’ve identified some paper lab books with a wealth of information, but it’s not in an easily accessible format. So where we’re using generative AI is to scan and interpret those and read them, and it’s dealing with handwritten notes, pictures, and sketches, but it’s presenting it in a format now that we can expose to many others and capture it. And while the generative AI maybe doesn’t understand at all, it’s able to tag it in a way that others will be able to look at it and see if there’s value. On cloud to manage data: Not everyone might want to hear this, but it takes a long time to rework infrastructure to transition to cloud. We took a native cloud approach and moved probably 60 to 70% of everything we do to cloud. But we did that very thoughtfully. We identified what made sense to stay on premise. And then in the move to cloud, we also refactored and redesigned it to make sure we took the benefits. So once you get it into the cloud, you suddenly realize you can deal with much bigger data sets, and this idea of connected data comes into play. So the approach we took was we’ve got to have a data platform that’s going to deal with all the ingestion with the quality issues. We’ve got to present the data in the right formats to different groups. Some of them work with research data, others with clinical data or manufacturing with regulatory reform. But it’s about taking a very thoughtful approach to understanding what the process is, how it’s being used, and who’s using it. The other aspect is to be prepared to re-engineer because the technology is moving and processes move so fast. For example, we had an award for high performance computer in the cloud, and since then, we’ve made adjustments to it, but it looks nothing like what it looked like three years ago. So you got to keep revisiting and looking at how people use it and decide if cloud is still the answer, or does it make sense to bring it closer and bring it back in-house. So it’s continually evaluated. On creating educational partnerships: I personally went out and became OECD certified. Part of it was I wanted to think about what the pressures are there because if you think of what’s happened in the last few years, with Covid-19, for example, the SEC started putting more rigor into things like cybersecurity. So the board had to lean into it. And now you have AI, so just being able to understand how they’re thinking about it helps me shape how I message things. All the boards are slightly different and I mostly interact with the audit committee, and they’re very concerned about cybersecurity. So we do briefings on it all the time. They’re also very interested in AI and we use it a lot. I think sometimes with generative AI, people see that it’s only two years old, but it’s actually been around a long time. But there’s a lot of interest, so we’re doing more presentations and more updates. But as a CIO, you have to put yourself in their shoes and the risks to board members has increased, too. So providing that oversight can be really challenging in the dynamic world that we’re in today. source

00:00 Hi, everybody. Welcome to DEMO, the show where companies come in and showcase their latest products and services. Today, I’m joined by Conor Jensen. He is the Global Field CDO at Dataiku. Welcome to the show, Conor. 00:10Thanks. I’m really happy to be here, Keith. 00:11And CDO—I’m going to guess that stands for Chief Data Officer? 00:13It does. Chief Data Officer. My background is in the data science space. Sometimes people think it means Chief Digital Officer. 00:21Now, Dataiku has several different pronunciations. Can you explain the company name and then tell us what you’re going to show us today? 00:28Sure. It’s really just a portmanteau of data and haiku. There’s nothing more to it than that. But because it’s a French company—founded in France about 12 years ago—the French tend to pronounce it Da-tie-koo with a very soft “H.” Americans usually say Data-IKU. Since we’re a global company, I’ve probably heard 20 to 30 different pronunciations. We welcome them all. 00:53At first, I thought it was a Japanese company because it reminded me of an anime show. 00:56Totally! I can see why. The Japanese pronounce it differently as well. So, yeah, there’s a wide range of ways people say it. 01:03All right. So give us an overview of what you’re going to show today and the whole purpose behind it. 01:07Dataiku, our core platform, allows people—regardless of their skill set, whether they’re coders, non-coders, or anywhere in between—to access data wherever they need it, whether in the cloud, on-premises, or elsewhere. It supports the full spectrum of analytics, from basic data analysis to machine learning and generative AI, all from a single UI with built-in governance. Today, we’re going to walk through a use case that demonstrates what working with Dataiku looks like in practice. 01:35A lot of companies that come on this show build products and platforms tailored to specific job roles within an enterprise. It feels like Dataiku is designed for a wide range of users across different roles. Would you say that’s the case? 01:49Absolutely. It’s for anyone who needs to work with data as part of their job. We see users from data engineering and data science, as well as analytics professionals in fields like sales and marketing. It’s a highly versatile platform with broad use across the enterprise. 02:04Got it. I’ve also had a lot of companies come on here that focus on data access—pulling data from multiple sources, compiling it, and generating insights. What makes Dataiku different from those other platforms? 02:14The key difference is that we don’t move the data. Dataiku operates as a UI layer on top of your existing data sources. Your data stays where it is. We do pull a small sample locally so users can interact with the data in real time—similar to working in Excel—but the actual data remains in its original source. When you finalize your work, the computations are pushed down to where the data resides, whether that’s a SQL database, a machine learning environment, or in-memory processing. This means you can use Dataiku to access data from local SQL servers, S3 buckets, or Kubernetes clusters without physically moving large datasets around. 02:58That makes sense. My last question before we jump into the demo—what would companies be doing if they didn’t have this product? Would they be using another platform, or would they be accessing data manually? 03:13Right, they’d be doing it manually—or at least in a more fragmented way. The reality is that these activities already exist, but they’re happening in siloed environments. For example, analysts might be using Excel, Alteryx, or point solutions. Engineers might be working in Tableau Data Prep or writing SQL scripts. Data scientists could be using notebooks, Databricks, or other tools. The problem is that these teams can’t easily collaborate. They often end up emailing Excel files back and forth, which is time-consuming and error-prone. Dataiku brings all of this together into a single platform where everyone—analysts, engineers, and data scientists—can work collaboratively. 03:59All right, let’s get to the demo. Since Dataiku is used across multiple job roles, I believe I asked you to come up with a use case scenario that highlights the platform in action. 04:10Exactly. We’ll walk through a use case that demonstrates how different users interact with the platform. When you log into Dataiku, it runs entirely in a browser. It’s hosted in the cloud, in a VPC, or on-premises. When you enter, you land on your homepage. For this demo, I’ll take on the role of a financial analyst in an FP&A (Financial Planning & Analysis) team. My job is to generate reports quickly and accurately. I have a project built out, but as an analyst, I don’t want to deal with the complexities of the backend—I just need my report. Here’s an example: I select a business unit, say The Americas without the USA, click a button, and instantly get a generated report. This report was drafted using an LLM (large language model), which pulls real-time data from structured databases. It’s not hallucinating numbers—it’s querying SQL databases and forecasts to provide a real-world summary of sales performance, what’s working, what’s not, and projected trends. 08:36Do companies need to know where all their data sources are, or does Dataiku help them discover that? 08:43Great question. Users can search for data within Dataiku’s catalog. Any data asset available to them will appear, making it easier to discover and access data without knowing exactly where it resides. If a user doesn’t have access to certain datasets, they’ll still see that the data exists and can request permissions from security teams. 09:13Because some companies struggle with simply knowing where all their data is, right? 09:21Exactly. That’s often the first challenge. Dataiku helps solve this by centralizing access while respecting existing security controls. 10:10I got you off track—let’s go back to the demo. 10:14No problem! So, we’ve looked at how an analyst interacts with the platform. Now, let’s look at the data engineer’s role. Behind the scenes,

“I’m aware that, even as we implement new products for the digital evolution of the studio, people need to be able to work without penalizing interruptions,” he says. “It’s also important to keep people updated on what’s being done and how technology will improve everyone’s work. I insist on the value of communication and involvement.” A focus on AI The experience of Stefano Bombara, IT manager of technical systems services at Crédit Agricole Vita, isn’t dissimilar. For Crédit Agricole Insurance and Crédit Agricole Life, companies of the Crédit Agricole Group, digital transformation began a few years ago, involving both internal processes and those used externally by intermediaries and customers. Until now, digitalization has focused on streamlining and automating processes, but AI now requires a strong IT-business synergy and a structured training intervention to accompany innovation. “This year we intend to introduce AI to digitalize the claims management process in the non-life branch,” Bombara says. “Even more than other technologies, AI is an innovation introduced not by IT alone, but together with the business — in this case, the claims department — ​​to identify the processes in which to apply it, or the use cases where we can produce real benefits of optimization and simplification for core activities. The entire project is accompanied by training on the methodology and the new cultural approach. This is very important because it allows us to align the expectations of the various business areas. Without common training, we can’t understand the potential or the constraints of AI.” source

1. Develop business acumen and user sensitivity The more IT’s business analysts and developers learn the end business, the better prepared they will be to deliver applications that fit the forms and functions of business processes, and integrate seamlessly into these processes. Part of IT engagement with the business involves understanding business goals and how the business operates, but it’s equally important to understand the skill levels of the employees who will be using the apps. For example, if you’re developing a sales application for front-line tellers at a bank because you want them to pitch credit cards and CDs to customers when customers come in, you should also take into account that turnover rates for bank tellers are extremely high. That means there will likely be a perpetual need for employee training on the app, and also that the app should be designed to be as simple and easy to use as possible, with base-level troubleshooting help baked in. source

As businesses increasingly rely on multi-cloud environments, building cloud resilience becomes critical. In this article, we will explore the key components of cloud resilience and its importance in recovering from outages such as ransomware attacks. We will delve into essential elements of cloud resilience such as cloud infrastructure backup, dual-vault cloud time machine, and recovery-as-code. By adopting these cloud-native disaster recovery practices across AWS, Azure, Google Cloud, and Kubernetes platforms, businesses can effectively safeguard their infrastructure, minimize downtime, and recover from ransomware attacks. Understanding Cloud Resilience Cloud resilience refers to an organization’s ability to withstand and rapidly recover from outages, assuring the continuity of its business operations. In a multi-cloud environment, resilience is achieved through a combination of robust infrastructure, efficient and comprehensive cloud infrastructure and data backup solutions, and well-defined disaster recovery strategies across multiple platforms. This is particularly crucial in the context of recovering from ransomware attacks, where the ability to quickly restore systems and data is vital to minimizing the impact of such security incidents. Importance of Cloud Resilience in Ransomware Recovery Ransomware attacks have become a significant threat to businesses, with devastating consequences for organizations that fall victim to them. These malicious attacks encrypt critical data and systems, holding them hostage until a ransom is paid. Cloud resilience plays a crucial role in recovering from ransomware attacks. By implementing robust backup and recovery mechanisms, a business’s data remains protected and recoverable even in the face of ransomware encryption. Cloud infrastructure backup solutions enable organizations to create regular snapshots of their systems and data, providing a clean and uncorrupted copy to restore from after an attack. Additionally, the ability to automate recovery processes through recovery-as-code practices allows businesses to swiftly recover entire environments, minimizing downtime and disruption to operations. By embracing cloud resilience strategies across multiple platforms, organizations can effectively mitigate the impact of ransomware attacks and allow for continuous business. As businesses embrace multi-cloud environments, the need for cloud resilience becomes paramount, especially in the context of recovering from ransomware attacks. By implementing robust backup solutions, leveraging automation through recovery-as-code practices, and adopting cloud-native disaster recovery strategies, organizations can effectively protect their data, systems, and applications. Cloud resilience not only minimizes downtime but also provides tools and processes that are necessary to recover from ransomware attacks and maintain continuous business. In an increasingly interconnected and threat-prone digital landscape, embracing cloud resilience is essential to safeguarding critical assets and maintaining the long-term success of businesses in multi-cloud environments. Learn more about how Commvault Cloud Rewind can help you rapidly rewind, and recover critical data, cloud applications, and configurations to a clean state and swiftly rebuild your cloud environments after cyber incidents. Get more tips to strengthen your cloud resilience with the following resources: ·         Simplifying Cloud Resilience and Cloud Recovery ·         Modern Cloud-Native Software Shines in Latest Analyst Reports ·         Build Cloud App Resilience with Commvault Cloud Rewind source

“T200 was founded with a bold vision: to unite and elevate at least 200 women C-level technology executives from large-cap companies,” says Mamatha Chamarthi, co-founder and chair of T200 and chief digital officer at Goodyear. “Achieving that goal only reinforced a deeper challenge — the urgent need to strengthen the pipeline of women rising into executive leadership roles.” To help address this, T200Lift was launched in 2021, expanding the mission beyond the C-suite to support women at all leadership levels, particularly those aspiring to their first C-level role. “T200Lift is about fostering growth through mentorship, networking, and professional development — creating an ecosystem where women in technology can learn, connect, and accelerate their paths to leadership,” Chamarthi explains. “By building a stronger, more inclusive leadership pipeline, we are not only advancing individual careers but also shaping the future of technology leadership as a whole.” I recently had a chance to catch up with four remarkable women who participated in the TechLX program through scholarships designated for T200: Lavanya Bobba, product owner at The Hartford; Gela Guiuo, digital and ecommerce program leader at Abbott; Emily (Pineiro) Hurff, vulnerability management service lead and senior manager at Zoetis; and Corrine Ptacek, SMO-ITSM catalog service manager at McDonald’s. They discussed their leadership aspirations and how connecting with nonprofits and gaining training opportunities have impacted their career journeys. source

The Foundation is also responding to requests from practitioners to extend its framework to cover other areas of IT, such as private cloud, SaaS, licensing, and on-prem data centers. “We’ve had to make some quick changes to the FinOps framework,” Storment says. “FinOps is starting to become this reporting backbone across variable technology spends.” That interest is one reason why the FinOps Foundation community doubled to 60,000 participants last year. CarMax, in addition, built a cross-functional team, reporting to the CIO, that uses many of the principles put forth by the FinOps Foundation. But his organization developed its own model, says Mohammad. While it’s not formally following the framework, “as it matures, we’re looking at it as a benchmark,” he says. And its FinOps principles aren’t limited to public cloud or SaaS. “Most FinOps principles have been applied to technology financials as a whole,” he adds. Why the interest in extending FinOps? “A big spender on AWS might have billions of tiny charges in a single month,” Storment says. “After managing that volume of data for the cloud, it’s easy to add other types of spend.” That’s one reason why, in the FinOps Foundation’s 2025 State of FinOps survey of 861 of its members, 65% said their FinOps practices are also being asked to optimize spend for SaaS, as well as licensing (49%), private cloud (39%), data centers (36%), and AI (63%). In addition, according to the report, 97% of respondents are investing in multiple infrastructure areas for AI, which reinforces and accelerates how managing an organization’s AI spend will require taking on new areas of spend for FinOps practitioners. source

Today’s CIOs are facing a digital paradox – the more technology they implement, the more complex it becomes to manage. So while many CIOs have successfully shifted enterprise workloads from manual and legacy systems to new, cloud-based applications in recent years, these breakthroughs have also caused them fresh headaches. “CIOs are now at the heart of the business like never before,” observes Dino Trevisani, SVP and Head of Americas region at Tata Communications. “Their organization’s digital fabric – the intricate tapestry of platforms, tools, solutions, and expertise that powers modern enterprises – has become the cornerstone of its success.” The flipside is that, without a streamlined digital foundation, innovation will stall, security risks rise and overall business agility will inevitably suffer. The urgent challenge for CIOs is to turn the digital fabric of their organization from a potential liability into a powerful enabler of success. A complex web For Shane Guthrie, VP Operations at application delivery and security company F5, digital complexity is only speeding up: “In the past, technology innovation came along in 10-year or five-year blocks, so you had the time to cycle through solutions and plan better for the future. But now we just don’t have that kind of time.” New tools now emerge “year over year, even in some cases month to month now.” In addition, today’s business ecosystems are ‘hyperconnected’, with always-on, real-time interactions between multiple audiences of employees, partners, suppliers and customers. And that’s not even counting the 32bn IoT devices forecast to be connected by 2030. This accumulation and fragmentation of technologies has created a complex web that many organizations struggle to manage effectively. Far from speeding up their work, it can create silos that slow them down, and introduce vulnerabilities that put data revenue at risk. The costs of inaction The rise of AI brings further complications. While 92% of companies say they’re investing in AI today, according to McKinsey[1]only 25% have a clear AI strategy. The result for too many organizations is inertia, waste and enormous opportunity cost. These factors might explain why, according to a recent Gartner study, more than half of CEOs think digital initiatives take too long to complete or to realize value. The message to CIOs is clear: speed and efficiency are no longer optional. IT leaders must rapidly optimize their organization’s digital fabric to help, not hinder, their business success. Tata Communications has developed a comprehensive approach to addressing this challenge. Through a three-part process – integrate, uncomplicate, and innovate – it helps CIOs get their digital fabric into shape and provide the foundation for the next stage of their organizations’ growth. Integrate, uncomplicate and innovate The process starts with integrating disparate technology stacks across their network infrastructure, cloud strategy, connected IoT devices, and enterprise applications and APIs. That requires an in-depth understanding of organizational objectives, as well as a thorough assessment of their existing systems. Bringing these different technologies together creates a solid digital foundation for the enterprise. Next comes simplification. This involves streamlining the IT estate by consolidating platforms, eliminating redundant systems, and optimizing workflows. A typical global enterprise’s network, for example, might involve anywhere from five to 30 different vendors, all working with different SLAs, regulations and architectures. Untangling this helps improve agility, resilience and performance, while critically reducing risk. “Complication in their digital infrastructure is frankly killing a lot of IT teams,” says Raj Purkayastha, New Jersey-based VP Head of Pre Sales and Strategy, Americas at Tata Communications. “By uncomplicating it for them, we can help drive cost efficiencies and generate operational excellence that’s really transformational.” With an integrated and simplified digital fabric in place, organizations can then focus on innovation. Greater visibility and control allow them to derive actionable insights more quickly and power new ideas. That can be the difference between winning and losing in today’s fast-paced business environment. For modern CIOs, optimizing their organization’s digital fabric is arguably their number one challenge, as well as their number one route to value. The strength and flexibility of their digital foundations will determine their ability to thrive in an increasingly competitive global environment. With the right approach and expert support, CIOs can create a fabric that underpins success for years to come. To learn more, visit Tata Communications’ website. ____________________________________________________________________________________________________________________________________________________________ [1] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work source

There are several advantages to using Kubernetes: it ensures a high degree of flexibility when it comes to selecting the right cloud for the respective application. And it increases the availability and reliability of services. For example, Kubernetes can automatically redirect workloads to other providers if a provider fails or the connection is poor — or to make optimal use of flat-rate data volumes. Terraform Terraform, an open-source tool for Infrastructure as Code (IaC), is recommended for building an infrastructure for application environments. It allows you to define and manage resources such as virtual machines, networks and databases using declarative configuration files. Instead of manually creating and managing infrastructure resources, the IT or cloud architects merely describe the desired end state of their infrastructure and save it as configuration files. The configuration language HashiCorp Configuration Language (HCL) is used for the description. Terraform then independently generates the desired state by creating, modifying or deleting the necessary resources. The whole thing can be set up as often as you like. A short command is all it takes to automatically copy an environment once it has been created. This is useful, for example, when setting up staging environments that are needed at different stages in the software development process. It is useful, for example, when developing cloud applications in highly regulated industries such as banking and insurance, aerospace, utilities and automotive. source

Deneen DeFiore, VP and CISO, United Airlines Deneen DeFiore / United Airlines DeFiore and CIO Jason Birnbaum got a head start on their relationship dynamics working at General Electric, where they didn’t interact as colleagues, but still gained exposure to a shared set of experiences, core values, and business language. That mutual understanding was pivotal when it came time to sketch out the contours of their working partnership at United. DeFiore and Birnbaum built on their common foundation, prioritizing open communications and transparency, developing a shared vision and set of outcomes, and aligning messaging to help break down barriers and misperceptions. Their playbook helps position security requirements at the center of new initiatives without bogging down timelines or becoming a gating factor for innovation. Case in point: United’s “Every Flight Has a Story” offering, a generative AI-fueled flight-status service released last year designed to bring more transparency and context to flight delays and updates. Jason Birnbaum, CIO, United Airlines Jason Birnbaum / United Airlines Working as a team, DeFiore and Birnbaum recognized the game-changing potential for generative AI, and together with their organizations created a framework around responsible use of the technology. The flight-status service was one of the first external-facing use cases for gen AI, and there are about 90 others in the pipeline, she says. “We were able to iterate on that quickly together and manage the risks associated with using emerging technology,” she explains. source