Diagnostic, Descriptive, Predictive and Prescriptive Analytics with Geospatial Data |
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| © 2021 by IJCTT Journal | ||
| Volume-69 Issue-1 |
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| Year of Publication : 2021 | ||
| Authors : Prashant Tyagi | ||
| https://doi.org/10.14445/22312803/IJCTT- V69I1P104 | ||
How to Cite?
Prashant Tyagi, "Diagnostic, Descriptive, Predictive and Prescriptive Analytics with Geospatial Data," International Journal of Computer Trends and Technology, vol. 69, no. 1, pp. 18-22, 2021. Crossref, 10.14445/22312803/IJCTT-V69I1P104
Abstract
This article discusses how companies can build a data lake foundation or a massively parallel processing data warehousing solution that they can leverage for addressing some of their ever-changing business climate needs through Diagnostic, Descriptive, Predictive, and Prescriptive Analytics. This article will discuss an overview of how to collate the data residing in silos and prepare the data for deeper structured analysis that will empower organizations to enhance the speed and quality of their decision-making process by converting data into some of the quick key actionable business insights. This article also discusses how certain Python Libraries on geospatial data can be leveraged to answer some of the most challenging questions faced by transportation, logistic and environmental service industries to address some of the critical issues such as Service Verification (missed customers), Route Compliance/adherence and real-time analytics for the estimated time of arrival at the customer’s location.
Reference
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Keywords
Predictive Analytics, Descriptive Analytics, Prescriptive Analytics, Diagnostic Analytics, Geospatial Data, Data Lake, Data Warehouse, Massive Parallel Processing, Hybrid Cloud, Data Access, Data Integration, Business Intelligence, Business Challenges, IoT data, Customer Service Verification.