1. Data Science Capabilities
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TECHNOSOFT
CORPORATION
Data Science
Capabilities

2. Agenda
Data Sciences Services
Data Visualization
Project Profiles

3. Data Sciences Services
Big Data analytics - BI/traditional analytics
Big Data use
Traditional BI suites and online
analytical processing
e-Commerce Search
social gaming
End-user
applications
Visualization
Basic Visualisation applications
Advanced Visualization Applications - Geospatial Mapping, Dendrograms, Graphs Heatmaps, Parallel Coordinates etc
Visualization
Tools
Consulting, Project management, and System integration services
IT Services
Traditional analytics
(BI, data mining)
Advanced analytics (predictive modelling, descriptive modelling and optimisation)
In-memory computing
Analytics
Big Data
Traditional relational database
NoSQL databases
Parallel relational database
Hadoop
Data modelling Data integration and processing Data storage and management
Data
Management
systems
Convectional file systems
Hadoop Distributed File System (HDFS)

4. Data Science Services and Capabilities @ Technosoft
Need
Services
Description
Application areas*
Store large quantities of unstructured
data
Data Architecture
Big Data architecture, RDBMS
Website click streams
Tweets and Facebook likes
Sensor data s
Faster data access, storage and analysis
Data Integration
Big Data Integration Services, Data Migration, Data Management
Real-time embedded systems
Algorithmic trading
E-Commerce
Social networking
Real-time analysis of high volumes of data
Data Mining
Text Mining, Taxonomy, Classification, Dynamic Neural Networks, Time Series Modeling, Event Sequence Analysis, …
Risk management
Customer intelligence
Revenue optimisation
Assortment
Merchandise planning
Gain actionable insights from analytics and respond to issues instantly
Data Visualization
QlikView, Tableau, SpotFire, Shiny R, D3 JS, …
Energy management
SEO optimisation
Real-time traffic congestion detection using GPS data

5. Technosoft’s “Service-Product” Framework
Outputs from Algorithms
Interfacing
Which Network equipment will fail
When it is going to fail
Visualization
Where equipment is going to fail
What action to take
Predictive Modelling
Actionable Dashboard
Analytical Data Set
Analytical Processing
Intermediate Data Set
Parsing
Integration
Building Taxonomy
Event log
Usage log
Product Configuration

6. Predictive Analytics As A Service @ Technosoft
RAW DATA SOURCES
ANALYTICAL DATA SETS
Integrated and Parsed data
Data without missing values
Data free from outliers
Normalized data fields
Derived fields in the data
Preprocessing
Integrated Enterprise Datawarehouse
External Data
Unstructured Data
Validating
Data Mining
ACTIONABLE RESULTS
OPTIMIZED MODELS
Model outputs
Interpretations abstracted
Top action items
ROI estimations and validations
Trained and Tested Models
Performance - optimized models
Interpreting

7. Data Visualization

8. Data Visualization in multiple platforms
On a PC/Mac
On Phone

9. Network Availability/Performance

10. Infrastructure Services Helpdesk Performance
This dashboard was developed to analyze tickets
One can easily do analysis of tickets by category and status
Size of pie depicts the no. of tickets and size of slice depicts the no. of tickets for each category
This dashboard was created using Tableau

11. Tools Expertise Data Extraction, Processing and Loading OLAP
Informatica
SSIS
DataStage
Social Media API - Facebook, Twitter
IBM Streams
Hadoop MapReduce, PIG, SQOOP
MS SQL Server Analysis Services
Cognos
MicroStrategy
Data Storage
Visualization
Oracle
MS SQL Server
IBM DB2
Teradata
Netezza
Hadoop HDFS / HIVE / No-SQL/Columnar DB
TIBCO Spotfire
SSRS
Cognos
MicroStrategy
QlikView
Tableau
Shiny R
JasperSoft
Google, BING , Visual Crossing maps
Custom Development
Data Mining and Predictive Analytics
Net
Java
Native Language within ETL, DB or Reporting Tool (Eg. Iron Python in Spotfire)
SAS R
TIBCO Spotfire S+
MicroStrategy

12. Project Profiles

13. Barr Pharma (MDM) Business Challenge Solution Overview
Client wanted Technosoft to design, support the design and configure their ECC 6.0 Master Data Management
Solution Overview
Translation of new business requirements into technical design of the global SAP Source to Settle Solutions and support the design and configuration of the ECC 6.0 Master Data Management
Go-Live stabilization for 4.6C roll out
Post Production support on FICO, MM, SAP Security, APO
Process validation, customization
CPC integration with SAP
Technical upgrade from 4.6C to ECC 6.0
Integration with Pliva’s system (company acquired by Barr last year)
Testing
Business Benefits
Enhanced business insight to support decision making and implementation of company strategy
Streamlined processes to comply with regulatory requirements
More efficient partnering with suppliers and providers
Increased nimbleness and flexibility with a more efficient and comprehensive ERP system
Automate Barr Lab’s transactions

14. Enhancing QoS using Event Sequence Analysis
Business Challenge
Telecom network failures - due to router, switch, server, ... faults
QoS issues could lead to customer churn & high service/repair costs
Objective
Enhance the quality of service of telecom network using predictive analytics
Solution Approach
Use standard telecom equipment log files
Extracted the unique keywords - events (faults or normal) before failure
Extracted three types of failures from the logs (Server, Firewall and Router)
Created a table with the sequence of events occurred prior to failure
Frequent Subsequence Pattern Mining and Event Sequence Analysis to identify frequently occurring events prior to failure
Technology
Open Source R Software
Machine Learning Approaches/Algorithms Applied -
Event Sequence
Clustering (Euclidian Distance)
Tools - R
Spotfire

15. Conversation Mining for Service Contract Process
Problem
Detect topics of the conversations from chat, s, and call notes from other messages
Derive the root cause of issue from the flow and other signatures of conversations
Objective
Build an approach to derive the root cause of an issue from the conversation
Predict future occurrence of issues based on patterns discovered from data
Allocate resources optimally or improve process to preempt issues and rework
Sample Approach: Part of Overall Conversation Mining Framework
Conversation Sequence Analysis
Create ‘sequenced’ bag of words from the conversation
Analyze term sequences to identify root cause of issue
Conversation Topic Detection, Association Mining and Tracking
Group records by unique product level combinations
Create bag of words for each chat for all items in the unique group (clique)
Find associations between cliques and product level classification and find anomalies if any
Technology
Open Source R Software
Machine Learning Approaches/Algorithms Applied -
Latent Dirichlet allocation
Lexicalization, Sentence Boundary detection ….
Clustering (Proprietary Distance)
Tools - R
Python
Opensource Library OpenNLP

16. The problem and opportunity in Churn
Business Problem
Loss of revenue
Increased customer acquisition cost
Reduced Market Share
Opportunities
Create innovative services (including bundling)
Understand customers better
Create targeted mediation strategy
Technology
Open Source R Software
Machine Learning Approaches/Algorithms Applied -
Classification - SVM with boosting
Tools R

17. Competitive Intelligence: Handset+WirelessPlans
Business Problem
How to competitively price bundles: Mobile Handsets plus Wireless Plans
Solution Approach
Created a perceptual map of the competitive landscape
Of different combinations of mobile handsets plus wireless plans
Created a feature comparison binary matrix for different mobile handsets
Converted binary matrix to dissimilarity matrix using Hamming distance
Created a feature comparison matrix for wireless plans of different service providers
Found composite score of dissimilarity matrices
Scaled down to two dimensional matrix using multidimensional scaling algorithm
Visualized perceptual map of combination of handsets plus wireless plans
Technology
Open Source R Software
Machine Learning Approaches/Algorithms Applied -
Multi-dimensional scaling (MDS)
Tools R

18. Differential Diagnosis Engine
Business Problem
Design and develop a symptom checker web application that recursively and algorithmically generates a differential diagnosis.
Solution Approach
Develop a natural language processing system that can convert the verbal description of symptoms to a systematically coded list of symptoms (based on ICD classifications)
Create a differential diagnosis engine that takes type, acuity of the symptom and demographics of patient to generate differential diagnosis.
Created a feature comparison binary matrix for different mobile handsets
Converted binary matrix to dissimilarity matrix using Hamming distance
Created a feature comparison matrix for wireless plans of different service providers
Found composite score of dissimilarity matrices
Scaled down to two dimensional matrix using multidimensional scaling algorithm
Visualized perceptual map of combination of handsets plus wireless plans
Technology
R for NLP and statistical analysis
SAS for NHAMCS data manipulation
Microsoft Access
HTML 5, Tableau
Causes, Treatment Options
Differential Diagnosis Generator
Natural Language Processor
System, Type, Acuity
Demography
Chief Complaints
Machine Learning Approaches/Algorithms Applied –
Asssociation Mining
Text Mining
Tools
R, SQL

19. Health and Condition Monitoring
Business Problem
With increasing number of incoming patients it becomes difficult to analyse each and every report
Response to interventions for risk profile may vary from individual to individual
Business Objective
To interpret the disease or combination of the diseases based on patients’ clinical report, demographic information and their life style and assess the risk of disease.
Tailor the wellness program to improve effectiveness and increase adoption of the program
Solution Approach
Classification Model to use disease history and other factors to assess health risk
Machine Learning Approaches/Algorithms Applied
Classification - Decision Tree
Tools R

20. Reduce Revenue Leakage by Minimizing Missing Codes
Business Problem
Loss of Revenue due to missing codes/charges (HCC 2009 report puts the loss figure at $100B annually)
Improper coding may also lead to claim denials
Business Solution
Predict the missing charge codes for individual visits by comparing with
Similar patients
Similar treatment/procedure
Historical claim sequences
Increase revenue by optimizing reimbursements through more accurate coding
Technical Solution Approach
Used patient historical data and filtered the required variables
Grouped the CPT codes based on the Visit Sequence Number
Created a transaction for each Visit Sequence Number with the associated CPT Codes
Extracted rule-sets using association rule mining to the
Considered only the rules with the Denied status
Machine Learning Approaches/Algorithms Applied
Association Mining
Clustering (Euclidian Distance )
Classification (Support Vector Machines, Decision Trees)

21. CONTACT US 28411 Northwestern Hwy Suite 640 Southfield, MI 48034
Tel: (248)
Fax: (248)
719 Griswold Street
Suite 120, Detroit,
MI 48226
nd PL NE
Suite 107
Bellevue, WA 98007
79 W. Monroe Street,
#819, Chicago,
IL 60603
9393 West 110th Street,
51 Corporate Woods,
Suite 500,
Overland Park, KS 66210
Technosoft Pune
Panchashil Tech Park,
6th Floor, Plot No 43/1, 43/2 & 44/2, Viman Nagar, Pune
4th Floor, Unit 1,
International Tech Park
CSIR Road, Taramani,
Chennai
Tel:
Fax:
4th Floor, Block 5,
DLF IT SEZ,
1/124 Shivaji Gardens, Ramapuram,
Chennai
A1, Block 4th Floor,
Shriram The Gateway SEZ,
16, GST Road, Perangalathur,
Chennai
Embassy Tech Square,
Signet building, West wing,
3rd floor, Kadubeesanahalli,
Sarjapur Marathalli Outer Ring Road,
Bangalore
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