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What is data architecture? A framework to manage data

CIO

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.

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When is data too clean to be useful for enterprise AI?

CIO

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.

Data 211
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CIOs must mind their own data confidence gap

CIO

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.

Data 169
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AI data readiness: C-suite fantasy, big IT problem

CIO

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.

Data 201
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Maximizing Profit and Productivity: The New Era of AI-Powered Accounting

Speaker: Yohan Lobo and Dennis Street

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.

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3 steps to get your data AI ready

CIO

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.

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The future of data: A 5-pillar approach to modern data management

CIO

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.

Data 167
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Data Value Scorecard Report

This report examines the quantitative research of data leaders on data value and return on investment.

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The Unexpected Cost of Data Copies

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.

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From Hadoop to Data Lakehouse

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.

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Put Your Data to Work: The Complete Playbook

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.

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5 Key Elements for Building a Successful Data-Driven Product

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.

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Ultimate Guide to the Cloud Data Lake Engine

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.

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Build Your Open Data Lakehouse on Apache Iceberg

Speaker: Veena Vasudevan and Jason Hughes

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.

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Harness Your Product Data: Better Understanding User Behavior Across Channels and Devices

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.