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Gen Z BI Paradigm

Gen Z BI Paradigm would be a state-of-the-art framework, with customized, interactive , rich visualization integrated with competitive analytics, Natural Language Processing, social media Sentiment Analysis, online news feeds building a collaborative BI framework that is accessible on all devices, whether on the web or mobile.

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Gen Z BI Paradigm

  1. 1. GEN Z BI PARADIGM - A Scalable , hybrid and collaborative Visualization Architecture using Spark, No SQL and RESTFUL API
  2. 2. Agenda ◦ What is Gen Z BI? ◦ Key features of Gen Z BI ◦ How is it structured - Architecture? ◦ How does data flow? ◦ Real-life examples ◦ Challenges in implementation ◦ Key takeaways
  3. 3. What is Gen Z BI? INTEGRATED INTELLIGENCE : BI WITHOUT LIMITATIONS ◦ Operational / Informed Intelligence – Data discovery in Real time with interactive Capabilities ◦ Social Intelligence – Social Media Sentiment Analysis , Online Feeds ◦ Competitive Intelligence – Competitive Strategies by correlation of external data ◦ Machine Intelligence - Search Capabilities powered by NLP and ML ◦ Conversational Intelligence – Personalized Chat bots
  4. 4. Key Features of Gen Z BI ◦ Customized, interactive, Real-Time, Collaborative Rich Visualization framework integrated with competitive analytics, Natural Language Processing, social media Sentiment Analysis, online news feeds ◦ Stand alone plug and play REST FUL APIs to integrate with existing Applications and UI Interfaces ◦ Highly scalable , reliable and flexible hybrid architecture ◦ Multiple caching layers in API, DAO ◦ Available anytime , anywhere - Hybrid model in Web, iPad and Mobile form factors.
  5. 5. How is it Structured? Files Tables Social Media & News Feed DATA LAYER MID - TIER SECURITY LAYER USER INTERFACE LIVE Stream Data SSO DATA INGESTION APP LAYER
  6. 6. How does Data Flow? Interpreter • Detects the Data layer • Converts to Queryable format Optimizer • Runs the Explain plan • Takes the most Optimal Path • Redirects to Cache Cache (May Fly/EH/Mem) • Accepts requests from Optimizer • Based on the Key passed by Optimizer looks up the cache • Returns the output as Response Request JSON ELASTIC SEARCH Not Cached jSON Object (Key: Value Pair) Response JSON Un- cached (Huge Volumes) shards shards shards shards cached shards shards shards shards DATA LAYER TERADATAFILESAPIs ETL Layer KAFKA
  7. 7. Real-Life Examples ◦ In-built Management Information Systems ◦ Showing Monthly/Daily snapshot of the Performance of our company with Top vital KPI’s. ◦ Curated huge terabytes of data with response time < 5ms ◦ Master Product Dashboard ◦ Whole list of Products from an organization ◦ Details about subscriptions, targets and performance ◦ Suggested new avenues to grow ◦ Competitive insights from external data, online feeds etc. ◦ Customer insights ◦ Tailored for different segments and by industry, understanding their growth model ◦ Enabling the company to provide highly customized data spread across the globe ◦ Self Service Monitoring tool ◦ Monitoring all the operational processes inside the organization , SLAs ,tracking effort , performance, budget etc.
  8. 8. Challenges in Implementation ◦ In-Memory Analytics ◦ Batch Vs Data Streaming ◦ Subscriptions ◦ Metadata ◦ Usage Stats ◦ How to overcome: ◦ Choosing apt Data Ingestion / Mid-Tier tools for in-memory analytics and data processing depending upon use cases. ◦ Blending traditional BI Tools with cutting edge technologies to take advantages from either side
  9. 9. Key Takeaways ◦ Gen Z BI will help unleash the power of Big data technologies , NoSQL databases. ◦ Gen Z BI can draw upon the best of existing technologies to provide new ways of visualizing real-time and interactive data in a flexible, scalable and reliable manner. ◦ It is structured as a plug-n-play architecture upon which varied cross functional / cross platform / self service portal can be built on. ◦ The full stack architecture allows for seamless data flow with fast response times.
  10. 10. Q&A