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Data Science Capabilities
| wecan TECHNOSOFT CORPORATION Data Science Capabilities
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Agenda Data Sciences Services Data Visualization Project Profiles
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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)
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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
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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
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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
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Data Visualization
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Data Visualization in multiple platforms
On a PC/Mac On Phone
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Network Availability/Performance
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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
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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
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Project Profiles
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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
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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
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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
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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
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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
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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
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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
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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)
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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 © Copyright 2016, Technosoft. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Technosoft. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.
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