This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.
As far as many C-suite business and IT executives are concerned, their company data is in great shape, capable of fueling data-driven decision-making and delivering AI-powered solutions. That emphasis can erode an organizations data foundation over time. Teams tend to prioritize short-term wins over a long-term outlook.
Business leaders may be confident that their organizations data is ready for AI, but IT workers tell a much different story, with most spending hours each day massaging the data into shape. Theres a perspective that well just throw a bunch of data at the AI, and itll solve all of our problems, he says.
We’ll cover: ✅ Data Management Best Practices: Streamline operations and reduce manual tasks with centralized, connected systems. Dive into the strategies and innovations transforming accounting practices. 🚀 Future Trends in Accounting Technology: Learn about technologies that help attract and retain tech-savvy talent.
While many organizations have already run a small number of successful proofs of concept to demonstrate the value of gen AI , scaling up those PoCs and applying the new technology to other parts of the business will never work until producing AI-ready data becomes standard practice. This tends to put the brakes on their AI aspirations.
In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.
Deepak Jain, CEO of a Maryland-based IT services firm, has been indicted for fraud and making false statements after allegedly falsifying a Tier 4 data center certification to secure a $10.7 The Tier 4 data center certificates are awarded by Uptime Institute and not “Uptime Council.”
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. Take, for example, a recent case with one of our clients.
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. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.
In the race to build the smartest LLM, the rallying cry has been more data! As businesses hurry to harness AI to gain a competitive edge, finding and using as much company data as possible may feel like the most reasonable approach. After all, if more data leads to better LLMs , shouldnt the same be true for AI business solutions?
Scaled Solutions grew out of the company’s own needs for data annotation, testing, and localization, and is now ready to offer those services to enterprises in retail, automotive and autonomous vehicles, social media, consumer apps, generative AI, manufacturing, and customer support. This kind of business process outsourcing (BPO) isn’t new.
Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. 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. Cost, by comparison, ranks a distant 10th.
An organization’s data is copied for many reasons, namely ingesting datasets into data warehouses, creating performance-optimized copies, and building BI extracts for analysis. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.
The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.
In 2025, insurers face a data deluge driven by expanding third-party integrations and partnerships. Many still rely on legacy platforms , such as on-premises warehouses or siloed data systems. Step 1: Data ingestion Identify your data sources. First, list out all the insurance data sources.
Data warehousing, business intelligence, data analytics, and AI services are all coming together under one roof at Amazon Web Services. It combines SQL analytics, data processing, AI development, data streaming, business intelligence, and search analytics.
However, trade along the Silk Road was not just a matter of distance; it was shaped by numerous constraints much like todays data movement in cloud environments. Merchants had to navigate complex toll systems imposed by regional rulers, much as cloud providers impose egress fees that make it costly to move data between platforms.
Getting off of Hadoop is a critical objective for organizations, with data executives well aware of the significant benefits of doing so. By migrating to the data lakehouse, you can get immediate benefits from day one using Dremio’s phased migration approach.
In 2025, data management is no longer a backend operation. 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.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Data is the heart of our business, and its centralization has been fundamental for the group,” says Emmelibri CIO Luca Paleari.
Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose. In fact, a data framework is critical first step for AI success. There is, however, another barrier standing in the way of their ambitions: data readiness. AI thrives on clean, contextualised, and accessible data.
In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
An interactive guide filled with the tools to turn your data into a competitive advantage. They rely on data to power products, business insights, and marketing strategy. We’ve created this interactive playbook to help you use your data to provide actionable insights that will lead to better business decisions and customer outcomes.
Data is the lifeblood of the modern insurance business. Yet, despite the huge role it plays and the massive amount of data that is collected each day, most insurers struggle when it comes to accessing, analyzing, and driving business decisions from that data. There are lots of reasons for this.
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. Initially, I worked as a researcher in academia, specializing in data analysis. This initiative is about creating a unified data platform.
It demands a robust foundation of consistent, high-quality data across all retail channels and systems. AI has the power to revolutionise retail, but success hinges on the quality of the foundation it is built upon: data. The Data Consistency Challenge However, this AI revolution brings its own set of challenges.
Leading brands and local businesses alike are tapping into varied business and consumer data to power their products and meet consumers’ ever-evolving needs. But companies need to remember that a product can only be as good as the data that powers it. The criteria you should use to vet available data sources.
The products that Klein particularly emphasized at this roundtable were SAP Business Data Cloud and Joule. Business Data Cloud, released in February , is designed to integrate and manage SAP data and external data not stored in SAP to enhance AI and advanced analytics.
The European Data Protection Board (EDPB) issued a wide-ranging report on Wednesday exploring the many complexities and intricacies of modern AI model development. This reflects the reality that training data does not necessarily translate into the information eventually delivered to end users.
At issue is how third-party software is allowed access to data within SAP systems. The reason: Sharing data from the SAP system with third-party solutions is subject to excessive fees. The reason: Sharing data from the SAP system with third-party solutions is subject to excessive fees. But SAP and its customers benefited.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics.
Cloud data lake engines aspire to deliver performance and efficiency breakthroughs that make the data lake a viable new home for many mainstream BI workloads. Key takeaways from the guide include: Why you should use a cloud data lake engine. What workloads are suitable for cloud data lake engines.
As CIO at NTT DATA North America, Barry Shurkey is responsible for digital transformation and optimizing the IT roadmap to support the company and its clients.
For us, its about driving growth, innovation and engagement through data and technology while keeping our eyes firmly on the business outcomes. What does it mean to be data-forward? Being data-forward is the next level of maturity for a business like ours. Being data-forward isnt just about technology. It wasnt easy.
A huge majority of data center customers and operators worry about the environmental impact of their IT decisions, but only a tiny number put their money where their mouths are. The lack of action on sustainability also applies to AI decisions, according to the recent State of Data Infrastructure Global Report from Hitachi Vantara.
AI’s ability to automate repetitive tasks leads to significant time savings on processes related to content creation, data analysis, and customer experience, freeing employees to work on more complex, creative issues. Building a strong, modern, foundation But what goes into a modern data architecture?
With data stored in vendor-agnostic files and table formats like Apache Iceberg, the open lakehouse is the best architecture to enable data democratization. By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience.
Do we have the data, talent, and governance in place to succeed beyond the sandbox? These, of course, tend to be in a sandbox environment with curated data and a crackerjack team. They need to have the data, talent, and governance in place to scale AI across the organization, he says. How confident are we in our data?
Many Kyndryl customers seem to be thinking about how to merge the mission-critical data on their mainframes with AI tools, she says. In addition to using AI with modernization efforts, almost half of those surveyed plan to use generative AI to unlock critical mainframe data and transform it into actionable insights.
The road ahead for IT leaders in turning the promise of generative AI into business value remains steep and daunting, but the key components of the gen AI roadmap — data, platform, and skills — are evolving and becoming better defined. But that’s only structured data, she emphasized. MIT event, moderated by Lan Guan, CAIO at Accenture.
With the core architectural backbone of the airlines gen AI roadmap in place, including United Data Hub and an AI and ML platform dubbed Mars, Birnbaum has released a handful of models into production use for employees and customers alike. CIO Jason Birnbaum has ambitious plans for generative AI at United Airlines.
Speaker: Kate Owens and Megan Bubley, SpotHero, Diana Smith, Segment, and Erin Franz, Looker
As your sources of data increase, so do the complexities of unifying the data in a meaningful way. Join our webinar on October 17th with Segment and Looker to hear how they have solved these complex data issues. In this webinar, you'll learn: Why SpotHero decided to unify their data across departments.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content