Leveraging Data Enrichment Pipelines

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Leveraging Data Enrichment Pipelines
Raj Kalluri
Practice Director – Data , Analytics and Search
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2. © Intersys Consulting – Intersys & Client Confidential
Company Summary
▪ Privately held IT services firm specializing in Big Data / Analytics / Search/ Digital Consulting
▪ 200+ Intersys consultants delivering IT solutions across the US and Mexico
▪ Office Locations in Texas, Arizona, and Guadalajara- Mexico
▪ Leverage a local, national and/or global model as appropriate for each customer and
engagement
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▪ Financial Services
▪ Healthcare
▪ High Tech
Manufacturing
▪ Media and Advertising
▪ Real Estate
Overview
Key Industries Core Values Recognition
▪ Be Accountable
▪ Bring Excellence
▪ Be Authentic
▪ Be in Service to Others
Our Key Partners

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Intersys and Elastic Use Case Implementations
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Search
Logging
Data API’s
Analytics
Operations Monitoring
Security Analytics

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Data API Platform(Real Estate)
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▪ High Value Data and Metrics locked in Data Lake
▪ Not possible to provide low latency API access to Data
▪ Expensive Joins needed to present 360 views of Data from multiple platforms
Problem
Solution
▪ Elastic Cloud Enterprise run on AWS for horizontally scalable cluster and different use cases
▪ Built Apache Spark based data pipelines to perform historical and incremental data loads
▪ Low latency data access using flexible elastic query DSL
▪ Kafka acted as middleware to control the loads into elastic and bridge the batch and real time
needs
▪ Data denormalized/Enriched with third party information - purpose fit indices were created
Outcome
▪ Low latency Data API Platform, serving internal and external customers. High value product 360
views now accessible easily.

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Data Science (Healthcare)
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▪ Inability to build comprehensive data profiles due to to variety and volume of data sources
▪ Semi-structured and unstructured data is not mined for value or reviewed manually
▪ Dependency on time-consuming batch data processing and business rules
Problem
Solution
▪ Comprehensive customer 360
▪ Positive , Negative Strength and recency of the sentiment
▪ Unstructured data text, voice, email, comments , social media and other interactions
▪ Conversion of Voice to Text
▪ Use Data Science algorithms to analyze sentiment and intensity of unstructured content
Outcome
▪ Create value from unstructured content, build comprehensive profiles, index key documents

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Enterprise Search(Finance)
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▪ GSA retirement under tight deadlines
▪ Needed replacement for public facing enterprise search
▪ Need to sponsor/promote links
▪ User submitted content in addition to crawls and other indexing
Problem
Solution
Outcome
▪ Replacement for GSA was built on an On-Prem installation of Elasticsearch
▪ Indexing content was enriched with user submitted external content
▪ Tooling was built to be able promote global links or term based links
▪ Persona based searching
▪ Customer was able to transition from GSA based search with equivalent functionality , paving
way for additional features down the lane.

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Reporting and Analytics (Media and Advertising)
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▪ Existing Application was based on older technology that could no longer be effectively
supported
▪ Required install/upgrade at each client location with daily data pushes to 300 locations
▪ Data was tightly integrated with the ratings application and inaccessible by others
▪ New “Big Data” source of television ratings would completely break this existing process
Problem
Solution
▪ Rebuild a single multi-tenant solution on modern technology with a reusable services layer
▪ Elasticsearch was the only viable option to meet the performance, usability, and cost needs for
the mixed reporting and analytics workload from static grid reporting to self-service
▪ Achieved Pricing objectives with recent data held in memory and older data on
inexpensive disk
▪ Elasticsearch was able to serve data for various requests on an average of just under 1
second with the max time around 17 seconds for a complex, high-volume analytical query
Outcome
▪ New integrated ratings data, better customer experience, improved operations and
maintenance