Business Intelligence (BI) on the Cloud
Developed Imperva’s first cloud-based data warehouse (DW) in Snowflake to act as the central repository for all major reporting functions (Marketing, Sales, Finance, and Customer Success). To do this we implemented a complete end-to-end cloud infrastructure , as follows:
Talend Cloud (ETL) => Snowflake (Cloud DW) => Tableau Online (Cloud BI)
Utilizing Talend ETL jobs and Tableau prep flows, we were able to migrate data across multiple disparate systems (Salesforce, Netsuite, Zendesk, Google Analytics). Within Snowflake we built reporting views for all functions by translating complex business logic into SQL code to transform data and reduce off-line modifications. The DW quickly reduced time-to-deliver critical reports by more than 70%.
Beyond a Dashboard: Predictive Analytics for a Smarter Factory
Providing “NoDoubt™” product quality is Raytheon’s mission. To do so, we proactively manage materials throughout the entire product lifecycle to minimize quality issues. Using predictive analytics we can utilize the data to warn our Mission Assurance team of potential problems and direct quality engineers where to look for actionable insight. Below is a powerpoint overview Randy Weston and I presented at the SAP TechED 2017 conference in Las Vegas. There were two major aspects of this project, bringing advanced analytics to the factory (examples in the slides), and using SAP HANA as the vehicle and engine for providing an enterprise scale advanced analytics solution.
AAA Insurance Group – Club CSAA
Received 1st Place in SOA Actuarial Research Conference
Provided research on the topic of modeling Insurance Reserves, under the
direction of CSAA. Discovered that time series modeling can also be used
for predicting future losses compared to prior methods like General Linear
Regression. By using Time Series analysis, we were able to conclude time-dependent
correlation factors that would better forecast losses for Home
and Auto insurance.
Data Mining Project
This project ended up being a ‘Version 2.0’ of the CSAA Time Series Analysis research in the above project. Following our research, the reserving group of CSAA asked that we look into finding any possible correlations between the type of ARIMA’s fitted to the individual series by state and/or coverage. Example, Bodily Injury, an
auto insurance coverage that is required in almost all states may experience
similar seasonal components for the frequency of claims for certain states (like high population states/region/coastal). If such a classification exists, the application of this would be useful for understanding what type of reserving trends they should expect for new states they wish to open coverage’s in.