For years, Accenture and MongoDB have been committed to enabling organizations to leverage the power of data to gain a competitive edge. Today, we are excited to announce the availability of MongoDB Enterprise Server as part of the Accenture Insights Platform (AIP).
AIP is an analytics-as-a-service solution designed to simplify analytics and support a data-native culture. It delivers actionable insights that unlock business value trapped in data and create new value for clients. Offered in a pay-as-you-use service model, the platform includes a suite of pre-built analytics apps and tools to support a multitude of industries and business functions.
The addition of MongoDB to AIP not only enables customers to leverage data at any scale, but also allows the AIP platform to address a wider range of use cases. For instance, Accenture's PV First – a cloud-based platform for pharmacovigilance – is leveraging MongoDB to help three of the world's largest biotech companies make drugs safer through machine learning.
Building such sophisticated analytics capabilities involves thinking through the three broad phases of turning data into actions – gathering data, analyzing data and serving the insights. As a key component of the AIP stack, MongoDB provides a modern data platform to simplify and accelerate efforts at each of these phases.
The first phase entails how easily can one gather and manage data. As organizations are becoming more data driven, they are collecting more data, from different sources, in different formats. The volume and variety of data has been growing at an unfathomable pace. The AIP team chose MongoDB for its flexible data model and horizontal scalability, allowing customers to analyze any kind of data, at scale.
While thinking through the second phase – ‘building capabilities to analyze the data’ – one must choose a set of technologies that helps one build and deliver at speed. AIP provides a comprehensive set of preconfigured tools for data ingestion, data prep, advanced analytics and data visualization) and frameworks (such as Apache Spark and Apache Kafka) to perform sophisticated analysis, generate reports, and automate operational apps. MongoDB enables developers and data scientists to move faster and get even more out of the AIP toolset. It readily integrates with other components of the AIP toolset, even tools that expect data in relational structure, and offers powerful native analytics capabilities. Developers can query and analyze the data in multiple ways – by single keys, ranges, search with faceted navigation, graph traversal, and geospatial queries. Using MongoDB’s aggregation framework, users can run complex analytics and move the complexity to database layer, allowing developers to build richer, responsive applications. Moreover, with choice of over a dozen native language drivers, MongoDB enables developers to build naturally in their preferred language, including R.
The third phase deals with ensuring that the analytics capability provides reliable access to insights. Batch processing can be the appropriate approach for some use cases, but access to around the clock, real time insights can prove to be a big competitive advantage. Real- time, mission-critical analytics workloads need a high performance, highly available data platform. MongoDB’s distributed architecture coupled with a high performance storage engine ensures that the toughest of the SLAs can be met. In some cases, the same data set might serve multiple workloads. MongoDB can provision replicas of a data set to serve as dedicated analytics nodes. This allows users to simultaneously run real-time analytics and reporting queries against live data, without impacting nodes servicing the operational application.
Whether building powerful business intelligence capabilities, performing machine learning or developing real-time smart applications, AIP with MongoDB helps address a wide spectrum of analytics scenarios by providing a scalable, flexible, and high performance analytics-as-a-service platform.
About the Author
Alan Chhabra is responsible for Worldwide Partners and APAC Sales at MongoDB. Before joining MongoDB, Alan was responsible for WW Cloud & Data Center Automation Sales at BMC Software, where he managed $200M annual revenue business unit that touched over 1000 customers. Alan has also held senior sales, services, engineering & IT positions at Egenera (a cloud pioneer), Ernst & Young consulting, and the Charles Stark Draper Laboratory. Alan is a graduate of the Massachusetts Institute of Technology, where he earned his B.S. in mechanical engineering and his Masters in aerospace engineering.