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Enter Gen AI, a transformative force reshaping digital experience analytics (DXA). Gen AI as a catalyst for actionable insights One of the biggest challenges in digital analytics isn’t just understanding what’s happening, but why it’s happening—and doing so at scale, and quickly. That’s where Gen AI comes in.
And the Global AI Assessment (AIA) 2024 report from Kearney found that only 4% of the 1,000-plus executives it surveyed would qualify as leaders in AI and analytics. To counter such statistics, CIOs say they and their C-suite colleagues are devising more thoughtful strategies. Is our AI strategy enterprise-wide?
In today’s rapidly evolving technological landscape, the role of the CIO has transcended simply managing IT infrastructure to becoming a pivotal player in enabling business strategy. This article delves into the six steps of delivering a successful IT strategy.
In a world where business, strategy and technology must be tightly interconnected, the enterprise architect must take on multiple personas to address a wide range of concerns. In todays rapidly evolving business landscape, the role of the enterprise architect has become more crucial than ever, beyond the usual bridge between business and IT.
But do you have a strategy? Strategy and goals are different. It's your strategy that allows you to make decisions that help you meet your goals in the first place. If you want to have an articulated strategy that you can use to make decisions, stay on-track, and meet your product goals, this is the webinar for you!
But the more analytic support we have, the better,” Gonzalo Gortázar CEO of CaixaBank, told IBM. A client once shared how predictive analytics allowed them to spot a rising trend in customer preferences early on. Decision-making based on intuition, common sense, and knowledge is very good and should never be lost.
In our fast-changing digital world, it’s essential to sync IT strategies with business objectives for lasting success. Effective IT strategy requires not just technical expertise but a focus on adaptability and customer-centricity, enabling organizations to stay ahead in a fast-changing marketplace.
A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.
In response, traders formed alliances, hired guards and even developed new paths to bypass high-risk areas just as modern enterprises must invest in cybersecurity strategies, encryption and redundancy to protect their valuable data from breaches and cyberattacks. Theft and counterfeiting also played a role.
Speaker: Megan Brown, Director, Data Literacy at Starbucks; Mariska Veenhof-Bulten, Business Intelligence Lead at bol.com; and Jennifer Wheeler, Director, IT Data and Analytics at Cardinal Health
Join data & analytics leaders from Starbucks, Cardinal Health, and bol.com for a webinar panel discussion on scaling data literacy skills across your organization with a clear strategy, a pragmatic roadmap, and executive buy-in. You’re invited! Unlocking enhanced levels of value and insight from data using a semantic layer.
Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose. Strong data strategies de-risk AI adoption, removing barriers to performance. Despite the ambitions many leaders harbour, they face a series of challenges that must be overcome to realise the true value of AI investments.
How do we CISOs adapt our strategies today? While AI-driven analytics and automation hold the promise of enhancing threat detection and response capabilities, they also introduce new attack vectors and vulnerabilities.
To attract and retain top-tier talent in a competitive market, organizations must adopt innovative strategies that help identify the right candidates and create a cultural environment where they can thrive. Leveraging Technology for Smarter Hiring Embracing technology is imperative for optimizing talent acquisition strategies.
The successful execution of AerCaps growth through acquisition strategy involved many moving parts, among them merging two IT departments, a process that has plagued other high profile M&A projects in the past. Business strategy must drive IT decision making Business-first pragmatism is the key to understanding what makes Koletzki tick.
Speaker: Eric Feinstein, Professional Services Manager, Looker
How to evaluate embedded analytic solutions as strategy to greatly reduce initial and on-going engineering effort. He will discuss working through personas, data types, reporting needs analysis and ultimately how this comes together to form a roadmap for reporting functionality and interface.
To fully leverage AI and analytics for achieving key business objectives and maximizing return on investment (ROI), modern data management is essential. The faster data is processed, the quicker actionable insights can be generated.” “It’s impossible,” says Shadi Shahin, Vice President of Product Strategy at SAS.
As new technologies and strategies emerge, modern mainframes need to be flexible and resilient enough to support those changes. At the same time, many organizations have been pushing to adopt cloud-based approaches to their IT infrastructure, opting to tap into the speed, flexibility, and analytical power that comes along with it.
Data strategies in the balance In addition to a data visibility gap between levels of IT management, quality problems often come from piecemeal IT infrastructure, with many companies using multiple IT vendors products to achieve desired functionality, says Anant Agarwal, co-founder and CTO at Aidora, developer of AI-powered HR software.
Our job is to think about technology and introduce technology, but were really here to solve business challenges, says Mills, executive vice president and chief technology, digital, and corporate strategy officer for Tractor Supply. We need to train the organization to leverage AI to solve business problems, not just to create something new.
Speaker: Yoav Yechiam, Founder and Head Instructor, productMBA
Analytics are highly important for product managers - and yet, analytic implementations often fail to actually help us. Analytics are there to answer important product questions, not just to collect data. How can we be mindful of our analytics so they enable us, rather than confine us?
Paul Beswick, CIO of Marsh McLennan, served as a general strategy consultant for most of his 23 years at the firm but was tapped in 2019 to relaunch the risk, insurance, and consulting services powerhouse’s global digital practice. Simultaneously, major decisions were made to unify the company’s data and analytics platform.
Well continue to need data engineering and analytics, data science, and prompt engineering. With gen AI, we have a wow factor that makes driving adoption less challenging. What skills are you developing to continue this gen AI momentum?
The CDO’s mandate extends beyond mere technology implementation; it encompasses the development of comprehensive digital strategies and the cultivation of a culture that embraces continuous innovation. This holistic strategy should encompass all business areas, including operations, finance, marketing, and customer service.
The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. The choice of vendors should align with the broader cloud or on-premises strategy.
As a product or technology leader, you likely know there’s a tremendous value that can be gained from predictive analytics. That said, successful implementation of predictive analytics can feel unpredictable. There are risks that need to be consciously addressed, and successful implementation requires the right strategy.
For some, it might be implementing a custom chatbot, or personalized recommendations built on advanced analytics and pushed out through a mobile app to customers. Ensuring these elements are at the forefront of your data strategy is essential to harnessing AI’s power responsibly and sustainably.
As organizations adopt a cloud-first infrastructure strategy, they must weigh a number of factors to determine whether or not a workload belongs in the cloud. Cloudera is committed to providing the most optimal architecture for data processing, advanced analytics, and AI while advancing our customers’ cloud journeys.
Allow me, then, to make five predictions on how emerging technology, including AI, and data and analytics advancements will help businesses meet their top challenges in 2025 particularly how their technology investments will drive future growth. Prediction #5: There will be a new wave of Data and Analytics DIY.
Successful digital chiefs combine a nuanced understanding of emerging technologies with strong commercial instincts, aligning sophisticated digital strategies with core enterprise objectives to outpace market shifts and capture new opportunities. This leaders influence also extends into talent strategy.
Computing surveyed 150 individuals representing companies from a wide variety of industries that are actively involved in using, testing, evaluating, or procuring data analytics tools at their organization. Download now to learn: The state of data analytics in end-user organizations.
The evolution of cloud-first strategies, real-time integration and AI-driven automation has set a new benchmark for data systems and heightened concerns over data privacy, regulatory compliance and ethical AI governance demand advanced solutions that are both robust and adaptive.
They understand that their strategies, capabilities, resources, and management systems should be configured to support the enterprise’s overarching purpose and goals. Recognize IT and business are inseparable IT and business strategies are now fully intertwined, observes Jay Upchurch, EVP and CIO at analytics vendor SAS.
research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Nutanix commissioned U.K.
Think surgical vs. brute force, and ground decisions as much on growth and strategy as on tech stack considerations,” he says. Related: Generative AI’s killer enterprise app just might be ERP ] The firm was using Deltek Vision, which Stanton says is “not well-suited for that — it’s a transactional system, not a data analytics system.”
As PMs, we all know the importance of building a successful product-led growth strategy. Zoom, Stripe, and Airtable are all examples of software companies with strong PLG strategies. What features do their strategies have that allow them to see continued success in this ever-changing market? But what else do they have in common?
Meanwhile, AI can also help companies modernize their mainframe strategies, whether it be assisting with moving workloads to the cloud, converting old mainframe code, or training workers in mainframe-related technologies, Goude says. I believe you’re going to see both.” The survey is cementing the fact that the IT world is hybrid,” she says.
Paul Beswick, CIO of Marsh McLellan, served as a general strategy consultant for most of his 23 years at the firm but was tapped in 2019 to relaunch the risk, insurance, and consulting services powerhouse’s global digital practice. Simultaneously, major decisions were made to unify the company’s data and analytics platform.
While data and analytics were not entirely new to the company, there was no enterprise-wide approach. As a result, we embarked on this journey to create a cohesive enterprise data strategy. These individuals play a pivotal role in driving change and implementing new strategies.
Aligning your culture, processes and technology strategy ensures you can adapt to a rapidly changing landscape while staying true to your core purpose. In this role, she empowers and enables the adoption of data, analytics and AI across the enterprise to achieve business outcomes and drive growth.
Speaker: Azmat Tanauli, Senior Director of Product Strategy at Birst
By creating innovative analytics products and expanding into new markets, more and more companies are discovering new potential revenue streams. Join Azmat Tanauli, Senior Director of Product Strategy at Birst, as he walks you through how the data that you're likely already collecting can be transformed into revenue!
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. With the right investments, policies, and strategies in place, the region is on track to become a global leader in digital transformation.
CMOs are now at the forefront of crafting holistic customer experiences, leveraging data analytics to gain insights into consumer behavior, and developing strategies that drive engagement across multiple channels. Enhancing decision-making comes from combining insights from marketing analytics and digital data to make informed choices.
GenAI is also helping to improve risk assessment via predictive analytics. In one example, BNY Mellon is deploying NVIDIAs DGX SuperPOD AI supercomputer to enable AI-enabled applications, including deposit forecasting, payment automation, predictive trade analytics, and end-of-day cash balances.
Here, we explore the key factors impeding IT modernization and provide recommendations to overcome them (with real-world illustrations of strategies). For instance, Capital One successfully transitioned from mainframe systems to a cloud-first strategy by gradually migrating critical applications to Amazon Web Services (AWS).
Speaker: Ian Thompson, Head of Business Intelligence at King, and Zara Wells, Strategic Customer Success Manager at Looker
King uses almost a competitive launch strategy for new games, as each game has a series of KPIs that it needs to meet. King’s product managers rely heavily on analyzing product features using analytics data and visualization to improve outcomes. The key is the strategy and tools for accessing product data at the level that you'd like.
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