Agile BI

Showing 41 - 50 of 70 pages tagged with: Agile BI

Accelerating Self-Service Analytics with a Unified Semantic Layer

Enabled by Data Virtualization, Unified Semantic Layer Promise Greater Efficiency through Common Definitions.To enable all business users, regardless of technical acumen, to derive value from self-service analytics tools, companies need to be able to provide access to the data through a unified semantic layer, which ensures that no matter the data source, users can access the data using common definitions that are standard, within each particular business culture. However, companies are unable to establish unified semantic layer because of the age-old challenge: Data is spread across multiple...

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Leading Construction Equipment Manufacturer Improves Service Delivery and Revenue Using Data Virtualization

To engage in proactive field maintenance and service delivery, The Company heavily invested in modern tools and technologies for telematics and predictive analytics. In addition, since The Company depends heavily on part data from the field for predictive analytics, it invested in field sensors and big data technology.All this data, captured several times an hour, is then streamed or transferred into a data lake. The company needed an agile data integration and access layer, one that can easily integrate big data with other sources of enterprise or cloud data in real time. Download this case...

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Deploying Data Virtualization at an Enterprise Scale

A Journey Towards an Agile, Data-Driven InfrastructureThe Company, being one of the largest multinational companies, with offices, data centers, and fabrication facilities all over the world, developed a heterogeneous ecosystem of tools and technologies over time, giving rise to a complex, distributed data ecosystem. As its IT culture was not historically suited for reusable information, The Company experienced and egregious misuse of resource time and effort. As challenges became overwhelming, The Company searched for an agile data access solution.Download this case study to learn more about:

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The Need for Speed and Agility in Business

Nagaraj Vijapurkar, CIT Group presents how businesses can move faster than their competitors with the right architecture

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Succeeding in Self-Service BI

Kyle Quass, BI Data Architect, Indiana University presents how to architect a universal semantic model - a common business definition layer that simplifies integration

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Business Needs for a Fast Data Strategy

Gordon Griffin, Chief Architect, Altus Analytics presents how Altus Analytics built a 3 tier modular architecture to tackle particular BI data modelling challenges.

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Data-as-a-Service (DaaS) for Business Decision Makers

Leverage Data Virtualization to Turn Raw Data into Actionable IntelligenceBusiness decision makers are not analysts, but they are often called upon to play the role of analysts by poring over tables of raw data to derive actionable insights. Ultimately, business decision makers need more than raw data; they need clean, curated information that is custom-fi t to their goals, so that when it arrives, it is immediately intelligible. In short, what they need is data-as-a-service (DaaS).Download this solution brief to learn more about:

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Denodo & Looker - Enabling Real-Time Business Decisions with a Modern BI Solution

The combination of Denodo and Looker delivers a modern BI infrastructure built on the foundation of a logical data warehouse. By leveraging templated Looker Blocks for various data sources, getting started with analysis is amazingly fast.Download this solution brief to understand:The benefits of combining data virtualization with data analytics.The data architecture of the Denodo data virtualization platform and the Looker data analytics platform.How Denodo and Looker work together to deliver value to customers such as Ultra Mobile.

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Mergers and Acquisitions Made Easy (And Successful)

Leverage data virtualization for seamless M&A that deliver expected gains.Companies engage in mergers and acquisitions (M&A) to gain new capabilities, reduce operational costs, eliminate competitors, and enter new markets. Unfortunately, most M&A eff orts fail to deliver the results that upper management expects.One reason for this high failure rate is that M&A activities need to be resolved as quickly as possible, since they carry a cost, yet they require complex integrations at the technology level, which take time. Employee and customer data, sales processes, and financial...

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Customer Case Study: Indiana University

In this video, Dan Young, Chief Data Architect at Indiana University, discusses how they used data virtualization to improve their decision-making across the university.

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