The Semantic Layer’s Critical Role in Modern Data Architectures

The Semantic Layer’s Critical Role in Modern Data Architectures
Many of the most exciting innovations and advancements in data management today are occurring within the semantic layer of data architectures. For example, we’re witnessing new or improved approaches to semantic modeling, data cataloging, data lineage, and more. Even older forms of semantics—such as metadata and virtualization—are being infused with new techniques for augmentation and automation, including intelligent tool algorithms driven by machine learning and the use of graph analytics to generate data maps and automatically document data elements found via graph.
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Unleash the Power of Your Finance Data

In today’s corporate finance departments, the pressure to do more with less is greater than ever. The most successful finance teams partner with other lines of business to drive growth through data-driven insights, using advanced analytics and predictive models. But translating insights into real-time actions requires real-time analytics that can’t be achieved with traditional business intelligence solutions and dashboards. To unleash the full power of your finance data, you need a new approach.
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Overcoming the Obstacles for Data Lake Success

Business users have a tremendous appetite for data. The “single version of the truth” was a rallying cry to deliver business data in data warehouses for years. Users were able to digest and analyze large volumes of corporate data. They reviewed trends, identified anomalies, and supported decision-making because they had the detailed data to support action. As the business/data environment matured, the need for more diverse detail and increased delivery speed only grew. The data lake became a successful mechanism for delivering data from diverse systems in a timely manner.
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3 ways to scale your data insights

How do you scale your analytics strategy more broadly across your business units and your customers? Join us on January 26 as we reveal brand new features in our latest cloud release that help you reach that elusive last mile of users. Learn how you can:
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Realizing the Value of Big Data through Data Quality

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Third, we argue that to advance theories on big data value realization, the IS field .For example, in a research facility, policies on information governance
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