The architecture of a robust modern Analytics Platform has to address the following needs.
- A cost effective well governed and well protected data acquisition platform
- A data scientist friendly data consumption interface for batch, ad-hoc and stream analytics
- A robust extensible data science toolkit
- A business user friendly data consumption interface for the latest ad-hoc data insight tools with robust visualization
- A robust collaborative decision support enabling workflow and user interaction application framework
- A robust science models for domain specific problem solving
- A fast data scalable and reliable data stream infrastructure
- An extensible stream analytics, monitoring and alerting mechanism
- An extensible semantic context library for data platform with runtime extensibility
- A mature governance, security and privacy model that spans the heterogeneous infrastructure
Profitics has built, tested and deployed various best practice architectures for a wide variety of analytics needs with different levels of emphasis on different aspects of the above mentioned needs.
The architecture of a robust modern data platform has to address the following needs.
- A data acquisition platform that is inherently extensible and covers the following four different price points
- minimal cost archival and DR needs
- very low cost large heterogeneous data and content storage
- Cost effective query available very large unstructured and semi structured data sets
- Real time and high performance query available structured and extensible structure data sets
- Real time high transaction volume supporting data platforms with extensible and robust integrity validation frameworks
- The data platform should be inherently amenable to security, privacy and governance
- Modern data platforms are also expected to support various streaming and in-process extensible analytics frameworks
- Modern data platforms need extensible semantic context library for data platform with run-time extensibility
- Big-Data, Fast-Data, Data-Streams, cost effective persistence, distributed computing that supports runtime extensibility define our modern data platforms
- A robust microservices framework would be needed to provide the necessary data and analytics consumption infrastructure
Profitics has designed, built and deployed various best practice architectures to meet various complex data infrastructure needs. Ask us about the platforms we built on AWS, Azure, IBM Cloud, using Snowflake,Redshift, BigQuery, Teradata, HDFS, MapR, Hive, Neo4J, MongoDB, Cassandra, CouchDB, HANA, hadoop, Spark, Solr, Tensorflow, R, Python, Kafka, akka, RabbitMQ, Nifi, MQSeries, Lambda ..etc.,
Data Science is the backbone for the new intelligent enterprise. A modern data science platform will address the following needs:
- Intelligent and learning systems that provide real-time and interactive personalized user interactions.
- Solvers for complex structured planning problems with computational complexity
- Stochastic and predictive models for various forecasting and simulation analytics
- Real-time and intelligent event monitoring and alerting systems
- Frameworks for knowledge modeling, knowledge extraction, knowledge tuning and insight development
Profitics has designed, built and deployed various cutting edge models using the latest tools. Ask us about our expertise with R, SPSS, SAS, Azure ML,Python, Amazon Personalize, DeepLens, DeepRazor, CPLEX, Matlab, Tensorflow, DL4J, Theano .. etc.,
The goal of an analytics platform is to deliver intelligent and continuously improving enterprise. The platform should be expected to monitor risks and deliver decision support in a rapidly changing world. Modern tools have made this objective cost-effective. Technology projects can now be truly responsive to business initiatives. Well designed analytics initiatives need a robust business strategy driving it. A robust business strategy needs a thorough and deep understanding of what is available (data,content, knowledge) and possible (models, tool capabilities and associated risks). Profitics excels at providing a robust business driven technology strategy for your analytics initiatives.
Internet scale data sets, syndicated data, user-interaction streams, machine/sensor generated signals make modern machine learning and deep learning possible. Modern in-memory decision models, AI frameworks, graph networks now solve a broad range of real-time problems in conjunction with machine learning. Microservices, componentization, containerization and real-time orchestration makes deploying rapidly evolving smart engines cost effective. Ask Profitics about our expertise in risk monitoring and alerting. Provide intelligent customer/user engagement with our stream analytics.
Visualitics™ is a customizable reporting and display tool that creates a variety of interactive business intelligence dashboards for business users. Visualitics™ uses existing planning, operational and sales data to create displays used for project planning and scheduling, geographic and heat map displays, dials and gauges, 3D and radar charts, treemap and pivot charts, calendar displays, and organizational charts. Create an intuitive, insightful and actionable view of your real time operational data with VisualiticsTM.
- Process raw data into inputs that create polished, readable reports in real-time
- Synthesize historical and current data to identify trends and make decisions
- Manipulate dashboards to view from different time periods, geographic regions, and business units
- Create custom dashboards to fit the unique needs of any business