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.

1. GEN Z BI PARADIGM
- A Scalable , hybrid and collaborative
Visualization Architecture using Spark, No
SQL and RESTFUL API

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. 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. 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. 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. 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. 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. 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. 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. Q&A