Big Data Analytics. Bringing Value out of Volume

Transcription

1 Big Data Analytics Bringing Value out of Volume

2 Big Data Analytics Bringing Value out of Volume

3 Topics About Business Brio Solution Approach Few Case Studies Epidemic Indicators Healthcare Fraud Sales Forecasting Q&A

4 About Us - Who We Are Business Brio offers consulting solutions in the areas of Market Research, Big Data Analytics and Operational Excellence. We equip our clients to understand customer behavior, measure operational performance, design improvements, and deliver results through analytical tools and techniques, and our proven methodology and expertise. We are a team of highly accomplished professionals with rich work experience in Fortune 500 companies across the US, Europe, Middle East and Asia Pacific. Business Brio is a member company of NASSCOM and CII

5 About Us - What We Do TURNING VOLUME INTO VALUE Data Analytics Engine Business Decisions ERP / CRM data Survey data Social Media data Structured and unstructured data Data Analysis Statistical Modeling Algorithm Text Mining Deciding marketing strategies Optimizing supply chain Tracking KPIs Uncovering hidden opportunities Determining runaway costs Improving forecasting 3

6 Analytics - Solution Approach We follow a structured approach. Business Problem Understand the business challenge/opportunity through stakeholder interaction Analytical Problem Analytical Solution Business Solution Develop analytical framework for the business problem Identify key metrics for improvement Up-stream downstream process impact evaluation Expected outcome Augment and Manage data Visualize pattern and trend Segment, Optimize, Validate hypothesis, Model Translate analytics into business insights Prioritize opportunities Feasibility and sustainability assessment

7 Analytics Detects Epidemic Indicators DATA Real-time GIS based AREA Epidemic Indicators Purpose Real time data update with spatial statistics and density distribution on geographical context to detect indicators of epidemic spread. It is also important to identify the hotspots to mobilize necessary resources. Challenge & Method In spatial statistics the ability to visualize data and models superimposed with their basic social landmarks and geographic context is invaluable but not easy. Visualization of spatial data in R using the layered grammar of graphics implementation of ggplot2 in conjunction with the contextual information of static maps from Google Maps, OpenStreetMap, Stamen Maps or CloudMade Maps 7

8 Analytics Detects Epidemic Indicators Technology Mobile Voice + Pic + Data = GIS Survey Every data comes with Lat/Long, Timestamp Authentication via two way - voice and pic No data attrition during data entry Robust framework for audit/quality check Impact Real-time visual monitoring of disease spread and the intensity of spread Identification of affected groups women or men or children Mobilization of resources for affected groups (vaccines, medical professionals, nursing aid etc.) 8

9 Spatial Distribution of Disease Spread Overall Female Children Male 9

10 Sales Forecasting in Retail DATA Static Sales Data AREA Retail Purpose Predicting sales of different varieties of Edible Oil for a 90-days forecast horizon for a retail giant. Method Box-plot to identify the factors influencing sales. Combination of simple regression with dummy variable technique to detect and remove seasonality and sudden fluctuation (due to promotional activities) in sales and ARIMA (Autoregressive Integrated Moving Average) model to explain the residual variation in sales data 10

11 Sales Forecasting in Retail Impact Our forecasting model showed 78.24%, 88.1% and 97.5% accuracy on a daily, weekly and monthly granularity levels respectively. Enabled our client to determine inventory reorder threshold levels and avoid overstocking and stockout situations Identified the factors (e.g., promotional offers, discount rate etc.) impacting sales being significant or not. 11

12 Fraud Detection in Healthcare DATA Semi Real-time Patients claim data AREA Healthcare Purpose Detecting medical cases with high probability of fraudulent claims from providers. Method Box-plot and Kruskal-Wallis test to examine the correlation between patient profile (i.e. age, sex, literacy, employment, type of disease) and UDC (Unit Difference Charges), and interaction plots to find combined effect on fraud. Regression model for finding relation between number of diseases and UDC and ANOVA for detecting specific unusual cases. 12

13 Fraud Detection in Healthcare Impact The model showed that the value of UDC deceases when the number of disease increases. So prophylaxis is critical rather then treatment post detection to reduce the medical costs and hazards Detection of the types of medical cases where there were high probability of fraudulent claims from providers. 13

14 THANK YOU