The Passagenwerk is an internal Splunk work tracing application that associates the value created by Splunk’s sales organization with individual behaviors, actions and processes from phone calls and email message content parsing (NLP) to optimize revenue growth and lifecycle. Passagenwerk collects phone, email, CRM, and automated marketing system data into a central Splunk cluster. It cross-references the entire workforce of Splunk sales and business development representatives to predict optimal actions to influence quarterly target achievement. Statistics, aggregations, attainment percentages, and predicted outcomes are served to over 500 users daily with point-and-click UIs personalized to individual user IDs and in team pages leveraging insights from the entire data set. I completed the Arcades Project during late 2017 and early 2018. After its implementation, user productivity increased by 96%, and lead conversion jumped over 400% , boosting revenue by millions of dollars. I was awarded the company-wide “Buttercup Award” and the business intelligence unit was funded with over $1 million by executives based upon its success in revenue and pipeline growth.

Languages: JavaScript, SPL, SQL Architecture: Integrated RBAC, Splunk Cluster, RedShift

Splunk Business Flow (SBF) is a customer-facing Splunk product that is currently in beta release. SBF delivers real-time analytics to non-technical users by using Splunk’s proprietary algorithms to correlate event data into sequential journeys. In doing so, SBF inverts the typical inductive analytical framework, visualizing nearly any process automatically and enabling users to perform high speed root cause analysis across millions of events organized as individual or aggregate journeys. I worked with the Splunk product team to design and develop business oriented visualizations and use-cases for the product. In October, 2018 for my work on the project I was invited to speak at Splunk’s user conference “.conf,” which attracted more than 10,000 attendees this year. I continue to work on developing architecture, visualizations, and use-cases for the project.

Skills: SPL, JavaScript Architecture: Druid, Splunk Cluster

Silicon Values

Technology companies can morph from bookstore to cloud service provider and search engine to car manufacturer with a moment’s notice. Defying any specific market category, many tech companies boast public values as their north star, often signaling their age, priorities and products in the values they select. Silicon Values is a crowd-sourced project to map the values of technology companies today. Using a simple webform and the Graphcommons API, participants enter the name of their company and its published values. A graph is generated algorithmically showing the shared relationships and degrees of centrality between different technology companies, allowing for cluster analysis, and associations to be drawn between individual businesses.

Skills: Python

Plateaus Project

Plateaus is an internal project to apply big data analytics and machine learning to marketing automation, CRM, and web tracking systems. The app collects and indexes data from a variety of sources and channels. It then provides several attribution models to determine the appropriate weight and influence of different customer actions and eventual value. The dynamic visualizations then provide predicted outcomes and optimization recommendations using a random forrest algorithm.

Skills: JavaScript, D3, SPL, SQL

Architecture: Splunk Cluster