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Provides enterprise-wide management strategy for decision-making driven by data Self-service analytics platforms give enterprise users the power to improve decision-making and provide on-demand insights. Self-service analytics helps data-driven organizations move beyond traditional reporting and business intelligence tools toward automated data preparation and enhanced analytics capabilities. A self-service solution providing pluggable support for machine learning at the point of data entry can help to make analytics processes even smarter, giving users the visual power to train and build models on the data.
By making a wider variety of data sources accessible more quickly to more people throughout an organization, self-service data ingestion helps improve analytics. Today, providing self-service access to data stored in data lakes means managing open-source software, data operations, infrastructure management, security, and supporting a diversity of ways for different data users across an enterprise to access data. Business leaders and data teams must now think through the many moving parts of providing users with self-service access to data lakes, and consider their data processes in holistic terms.
Business units at Cox Automotive can easily manage an integrated data catalog, helping users see what data they have, access this data, and even perform gaps analyses to see which data is needed. With robust data management and catalog capabilities in place, Cox Automotive business users are spending less time worrying about data precision, and they can focus on analyzing the data to get new insights and better serve customers needs. While the Data Platform Management Console provides catalogue functions, sometimes having a dedicated portal to organize the data elements into a companys taxonomy can provide great value.
Their custom built Data Lake Engine provides user-generated semantic layers with integrated, searchable catalogs indexing all the metadata, so that business users can easily understand all of their data. By using Kyligence, users can run queries directly in their data lake using either standard SQL or Business Intelligence (BI) tools that support SQL queries. Because Kyligence supports all the leading cloud data lakes like Amazon Cloud S3, Azure Data Lake Storage, and Google Cloud Storage, as well as integrations with popular BI tools such as Tableau, Power BI, and MicroStrategy, Kyligence is an ideal choice to create a self-service analytics platform using whatever tools and resources the business is already using, while also providing future-proofing.
You can leverage Power Platform Admin Center self-service options built on top of Azure Data Lake Storage to augment Power Platform telemetry with data from other sources. You can use Microsoft Power Platform self-service analytics to export Power Apps inventory and usage data directly into a Gen2 Data Lake Storage location.
Creating Data Lakes using AWS Lake Formation allows you to build an open, portable data platform, which allows data consumers (users and applications) to discover and extract quality, trusted data from all enterprise data domains. AWS Glue DataBrew, combined with help from machine learning to help better understand and verify data, allows all types of users to create, consume, and publish data into the organizations data lake in a visual, zero-code, or low-code fashion, working within the existing centralised management model.
