Trends for Modernizing Analytics and Data Warehousing in 2019

Brand new research published from Dresner Advisory Services digs deeply into the trends in 2018 around big data analytics. Where are organizations heading in 2019? How are analytic and data warehouse architectures evolving to enable faster and deeper self-service analytics and BI for organizations looking to create a competitive edge? How is public, private and hybrid clouds factoring into deployment decisions? What are the hottest open source projects from Apache Spark to Kudu, Kafka, Hadoop, and beyond?
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Data extraction from documents: template-based or AI-based

In companies today, numerous documents are in circulation. Yet the data inside needs to be captured so it can be used for further processing. Just think about how the Finance team needs access to data in invoices. Or how the market analytics department needs access to utility bill data to perform market research. Brokers who need to retrieve data from purchase orders, IT departments that require data extraction as a part of a paperless workflow. The list goes on.
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How to Gain a Crisis Advantage with Real-Time Information

To reduce the impact of an unfolding crisis, companies need a clear, accurate and early line of sight into emerging risks. Leaders can then act with confidence and seize opportunities to preserve brand reputation while driving operational excellence. To demonstrate how real-time information can help companies gain advanced warn
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Does More Data Mean More Effective Control Charts The Surprising Truth

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When you need to monitor and control variation in your process, a control chart will help you to separate special causes from common cause variation or random variation. Yet how do todays types of data, including full inspection, impact this tool's effectiveness? The control chart was created in the 1920s, a time when measurement, data capture and calculation were done manually, making sampling essential. Today, how data is collected has all changed.
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Gain Context, Insight and Value from Big Content

OpenText

The Internet of Things, social media and enterprise content all have one thing in common: they generate vast quantities of data — ever-faster. So much so that picking out relevant information and assembling it into meaningful patterns is a tall order. These new data sources also present another challenge. They generate unstructured data - such as text and emojis - that don’t fit neatly into traditional database structures. This data can however hold valuable insights on important and hard-to-quantify concepts such as social sentiment.
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