Advanced Analytics with Amazon Quicksight

Democratising Analytics with Amazon QuickSight

Organisations increasingly strive to become more data driven in their decisions and need to improve data literacy for all their employee’s, reducing dependency on IT and data scientists. They also look to gain efficiency by moving to the cloud, increasing ability to provide self-service analytics in a secure, scalable, and cost-efficient way. ​

Many customers looking to modernise their advanced analytics, self-service and reporting needs in a cost-effective manner, have already migrated to Amazon QuickSight - a scalable, embeddable, ML-powered business intelligence (BI) service for the cloud that is constantly evolving with new features.​

A number of latest innovations and improvements make Amazon QuickSight an even more attractive option for organisations of all sizes. These include natural language query, fostering democratisation of analytics, embedded analytics with ML capabilities enabling rich data visualizations within customer applications, seamless scaling, security and governance.

So, what is Amazon QuickSight?

QuickSight is a fully managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards, and share these with tens of thousands of users, either within QuickSight itself, or embedded in software as a service (SAAS) application.

Benefits


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    Easy data insights for every user​

    Easy data access and insights for every user, business and technical, quick and easy to set up, with QuickSight enabling natural language queries fostering self- service and democritisation of analytics

  • Embedded Analytics and Collaboration​

    A multi-tenant embedded dashboard for hundreds of thousands of users enables sharing dashboards without access to QuickSight or a need to duplicate and manage users scaling and governance

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    Cost Effective Power Analytics at Scale ​

    Only pay for what you use; scale quickly with embedded Governance & Security – there is no need for any infrastructure overheads​

  • Innovation & Time to value​

    - Embedded ML capabilities facilitate a move from descriptive to predictive analytics and insight - e.g., churn, anomaly detection, fraud use cases

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Our Approach

As a specialist in Data, Analytics and AI/ML on AWS technology, Data Reply works with customers to help them with actionable data driven insights, with driven business transformation.

Reply has the AWS QuickSight Service Delivery Competency and is part of the AWS QuickSight Business Impact initiative helping customers to better use QuickSight in building data driven business functions and processes. Examples include Contact Centre Analytics, Supply Chain Visualisation for Risk & ESG, etc. ​

We developed a number of accelerators to help customers with QuickSight implementations for a particular use case, business function, process or with the QuickSight migration and modernisation of their analytics. These include architectures, QuickSight dashboards, data models, sprint /migration planning and other reusable QuickSight technical content to help reduce time to value. ​

For QuickSight Migration projects we typically adopt a 3-stage approach:

  • Business Case, TCO calculator, Rapid Discovery etc.

  • Implementing QuickSight Discovery and Planning, Migration path etc.​

  • Includes modernisation​

Case Studies


Case Study

FastWeb

A major engine manufacturer wanted to improve maintenance and assistance services, leveraging mixed reality applications, 5G technologies and the AWS cloud. Reply built a solution to support hands-free training for maintenance activities, by showing a holographic preview of the operations to be performed. QuickSight was used to show management some statistics about maintenance activities (e.g. maintenance information for specific engines, average time to perform maintenance operations). The solution allowed a quicker identification of errors and an overall reduction of errors during maintenance procedures.

Case Study

Bottero

A glass manufacturing company wanted to get a better visibility of machine productivity using AWS. IoT components directly integrated with machines' hardware allow to collect data in real-time and process it through a highly-scalable serverless back-end, then store it in the cloud. QuickSight is then used to visualise the status of production lines, so that Bottero are able to evaluate actual utilisation patterns and thus define ways to bring improvements. The solution also provides proactive alarms to save time previously spent doing manual checks.

Case Study

B2B SaaS company

The client wanted to migrate numerous marketing & sales dashboards from their existing visualisation platform to QuickSight. Reply started with an assessment phase, analysing all dashboards to find all opportunities for optimisation. The mobilisation and migration phases then allowed to establish patterns and best practices and to incrementally migrate all dashboards. The business has now access to dashboards that are much faster to render and are functionally and graphically equivalent to the original ones, which makes it much easier for stakeholders to accept this change.

Why Data Reply?


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    Data Reply is a Reply Group company, a premier AWS partner, offering a broad range of advanced analytics, AI ML and data processing services. We operate across different industries and business functions, enabling our customers to achieve meaningful business outcomes through effective use of data, accelerating innovation and time to value. ​

  • In addition to the AWS QuickSight Service Delivery Competency, Reply holds multiple AWS Competencies, including Machine Learning, Data & Analytics, and can support clients end-to-end in their data & AI journey on AWS.

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    DATA REPLY

    Data Reply is the Reply group company offering a broad range of advanced analytics and AI-powered data services. We operate across different industries and business functions, enabling them to achieve meaningful outcomes through effective use of data. We have strong competences in Big Data Engineering, Data Science and IPA; we build Big Data platforms and implement ML and AI models in a manner that is repeatable, efficient, scalable, simple and yet secure. We supports companies in combinatorial optimization processes with Quantum Computing techniques that enable an engine with high computational performances.