The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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As organizations strive to be more competitive, they often need real-time insights; no one wants to make decisions based on stale data. TDWI research indicates that real-time data collection is already in the mainstream. Some use cases include inventory management, fulfillment, supply chain, and logistics in which retailers must be able to assess product availability and consumer demand in real time. Forward-looking organizations also want to enrich real-time data with other data types to provide even better analytics.
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Digital transformation means that data drives more of our lives, more than ever. Data analytics is an essential skill for extracting information and meaning from data to support decision-making and power digital systems.
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From micromobility to last-mile delivery, mobility and transportation use cases are inherently geospatial. Whether you’re planning expansion into a new city or territory or optimizing rider / fleet operations, mobility companies are turning to spatial analytics to find greater profitability in a time of rapidly rising costs and fluctuating consumer demand.
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