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
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.
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.
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 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.
Think your customers will pay more for data visualizations in your application? Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics. Five years ago they may have. But today, dashboards and visualizations have become table stakes.
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.
This quarter, we continued to build on that foundation by organizing and contributing to events, meetups, and conferences that are pushing the boundaries of what’s possible in Data, AI, and MLOps. In September, we organized the 11th edition of the Analytics Engineering Meetup. As always, the more we share, the more we learn.
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.
Azure Synapse Analytics is Microsofts end-to-give-up information analytics platform that combines massive statistics and facts warehousing abilities, permitting advanced records processing, visualization, and system mastering. What is Azure Synapse Analytics? Why Integrate Key Vault Secrets with Azure Synapse Analytics?
In today’s ambitious business environment, customers want access to an application’s data with the ability to interact with the data in a way that allows them to derive business value. After all, customers rely on your application to help them understand the data that it holds, especially in our increasingly data-savvy world.
growth this year, with data center spending increasing by nearly 35% in 2024 in anticipation of generative AI infrastructure needs. Data center spending will increase again by 15.5% in 2025, but software spending — four times larger than the data center segment — will grow by 14% next year, to $1.24 trillion, Gartner projects.
The value packed into mainframe data is immense. It’s decades of transactional data and history, it’s a comprehensive log of customer engagements, and it’s a view into how the operations of a business have evolved. It’s a decision that maps back to the overarching goals of a business and how they want to leverage their data.
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.
tagging, component/application mapping, key metric collection) and tools incorporated to ensure data can be reported on sufficiently and efficiently without creating an industry in itself! to identify opportunities for optimizations that reduce cost, improve efficiency and ensure scalability.
Speaker: Sam Owens, Product Management Lead, Namely Platform
Sam and Jessica faced a problem that many product managers face: their customers wanted better analytics and reporting, but analytics wasn’t the core function of the SaaS product Sam and Jessica manage. To make things tougher, they needed something flexible, scalable and capable of serving different user types.
If competitors are using advanced dataanalytics to gain deeper customer insights, IT would prioritize developing similar or better capabilities. This process includes establishing core principles such as agility, scalability, security, and customer centricity. Learn more about IDC’s research for technology leaders.
This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.
Israeli startup Firebolt has been taking on Google’s BigQuery, Snowflake and others with a cloud data warehouse solution that it claims can run analytics on large datasets cheaper and faster than its competitors. Big data is at the heart of how a lot of applications, and a lot of business overall, works these days.
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.
As data, analytics, and AI continue to push the boundaries of what’s possible, 2024 has brought forward a new wave of groundbreaking use cases and innovative leaders.
Thats why we view technology through three interconnected lenses: Protect the house Keep our technology and data secure. For example, when we evaluate third-party vendors, we now ask: Does this vendor comply with AI-related data protections? Are they using our proprietary data to train their AI models?
The McKinsey 2023 State of AI Report identifies data management as a major obstacle to AI adoption and scaling. Enterprises generate massive volumes of unstructured data, from legal contracts to customer interactions, yet extracting meaningful insights remains a challenge.
Re-platforming to reduce friction Marsh McLennan had been running several strategic data centers globally, with some workloads on the cloud that had sprung up organically. Several co-location centers host the remainder of the firm’s workloads, and Marsh McLennans big data centers will go away once all the workloads are moved, Beswick says.
Analyzing data generated within the enterprise — for example, sales and purchasing data — can lead to insights that improve operations. But some organizations are struggling to process, store and use their vast amounts of data efficiently. ” Pliops isn’t the first to market with a processor for dataanalytics.
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.
Core42 equips organizations across the UAE and beyond with the infrastructure they need to take advantage of exciting technologies like AI, Machine Learning, and predictive analytics. Technology is seen as critical in ensuring data privacy, particularly as more organizations adopt cloud-based solutions for sensitive workloads.
As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.
Topping the list of executive priorities for 2023—a year heralded by escalating economic woes and climate risks—is the need for data driven insights to propel efficiency, resiliency, and other key initiatives. Many companies have been experimenting with advanced analytics and artificial intelligence (AI) to fill this need.
Everstream Analytics , a supply chain insights and risk analytics startup, today announced that it raised $24 million in a Series A round led by Morgan Stanley Investment Management with participation from Columbia Capital, StepStone Group, and DHL. Plenty of startups claim to do this, including Backbone , Altana , and Craft.
In April 2024, Dataiku and Cognizant surveyed 200 senior analytics and IT leaders from large enterprises worldwide. The results revealed a significant gap between what CIOs aim to achieve with Generative AI (GenAI) and analytics — and what they can realistically deliver.
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. The Internet of Things will also play a transformative role in shaping the regions smart city and infrastructure projects.
In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket.
Re-platforming to reduce friction Marsh McLellan had been running several strategic data centers globally, with some workloads on the cloud that had sprung up organically. Several co-location centers host the remainder of the firm’s workloads, and Marsh McLellan’s big data centers will go away once all the workloads are moved, Beswick says.
American Airlines, the world’s largest airline, is turning to data and analytics to minimize disruptions and streamline operations with the aim of giving travelers a smoother experience. We moved our major data platforms to the cloud and implemented data hubs for Customer and Operations,” Mohan says. Taking to the cloud.
DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?
But, for businesses that want to stay ahead in the data race, centralizing everything inside massive cloud data centers is becoming limiting. In a world of emerging technologies and powerful new analytics models, speed is as critical as accuracy—and in this world, the cloud is going to fall short.
He says, My role evolved beyond IT when leadership recognized that platform scalability, AI-driven matchmaking, personalized recommendations, and data-driven insights were crucial for business success. In a world of data-sharing, operating in silos is never a smart decision. These are her top tips: 1.
Heres the secret to success in todays competitive business world: using advanced expertise and deep data to solve real challenges, make smarter decisions and create lasting value. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
This data highlights the growing recognition of soft skills as a cornerstone of effective leadership. Harnessing Digital Platforms in Executive Search The integration of digital platforms into executive search processes offers unparalleled scalability and efficiency.
During his one hour forty minute-keynote, Thomas Kurian, CEO of Google Cloud showcased updates around most of the companys offerings, including new large language models (LLMs) , a new AI accelerator chip, new open source frameworks around agents, and updates to its dataanalytics, databases, and productivity tools and services among others.
Daloopa closed on a $20 million Series A round, led by Credit Suisse Asset Management’s NEXT Investors, to continue developing its data extraction technology for financial institutions, which is now being expanded globally. Customers can request data sets with a couple of clicks of a button and have it delivered the next day.
German healthcare company Fresenius Medical Care, which specializes in providing kidney dialysis services, is using a combination of near real-time IoT data and clinical data to predict one of the most common complications of the procedure.
Many companies collect a ton of data with some location element tied to it. Carto lets you display that data on interactive maps so that you can more easily compare, optimize, balance and take decisions. A lot of companies have been working on their data strategy to gain some insights. Insight Partners is leading today’s round.
The old stadium, which opened in 1992, provided the business operations team with data, but that data came from disparate sources, many of which were not consistently updated. The new Globe Life Field not only boasts a retractable roof, but it produces data in categories that didn’t even exist in 1992.
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