Business Analysis for Data Science Teams Susan M. Meyer

Data Science & Big Data Yet another Gold Rush?

In a Word: No Big data left the Gartner Hype Cycle in 2015, but there’s no shortage of data-driven services to take its place: Internet of Things AdTech FinTech RegTech http://www.gartner.com/smarterwithgartner/3-trends-appear-in-the-gartner-hype-cycle-for-emerging-technologies-2016/

St. Louis: A traditional IT powerhouse

St. Louis: A top metro area for startups A strong startup scene: 4,876 new businesses in 2014, representing 9.7% of total businesses—a 3 point change in share Source: FiveThirtyEight.com

Average Base Salary Guide: 2016 St. Louis Job Market Starting salaries tend to be higher for specialized roles such as: Certified project managers Business intelligence specialists Information security analysts Information security engineers UMSL led the way in creating Business Intelligence and Cybersecurity programs Source: Modis 2016 Salary Guide for IT Professionals

A Business analytics Career Path Minimum Viable: Are you a Power User of Excel? Can you read SQL? VP Business Owner Agile Product Owner Product Developer Product Analyst Bonus Points: MOOC credentials A/B testing & experimental design Business Rules Analyst Business Analyst

“You Be You”: Design Your Own Role We don’t need to be mathematicians or statisticians to contribute to data science teams …prepare to lead data integration …and to define key business metrics Source: Harlan Harris, Data Community DC

CRISP-DM: Cross-Industry Standard Process for Data Mining* Research by KD Nuggets confirms that data science teams (43%) still rely on IBM’s CRISP-DM as their primary methodology for analytics: Business & Data Understanding are critical to the success of data science teams Solution Evaluation may evolve into its own product support role Deployment Design may evolve into an independent project or run concurrently *Serving data science since 1998 Source: http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html

You had me at “iterative”: Six Ways we Add Value to data science teams Know your business domain Leverage an Agile approach Know your business model Elicit requirements through data Select analytics architecture Build feedback loops

Network Threat Detection Business Analysis Planning #1: Know your verticals Marketing Campaign Planning Marketing Mix Offer Optimi-zation Cyber Fraud Detection Network Threat Detection Financials Credit Scoring Risk Analysis Asset Optimi-zation Pro Tip: Do document analysis (BABOK 10.18) on industry standards (ISO) Sources: Chambers & Dinsmore, Modern Analytics Methodologies, p. 107 (2015)

Requirements Life Cycle Management #2: Go Agile Product features driven by data science can include: Model-driven scores Data transformation services Customer support deliverables Data quality monitoring services Pro Tip: Check out Data Mining (BABOK 10.14) for modeling features, uses, & risks

#3: Know the business model Strategy Analysis Strategy Analysis #3: Know the business model As BA’s we hold this as a self-evident truth, but a new data science team may not fully understand the domain Shilpa Aggarwal and Nimal Manuel (McKinsey) Pro Tip: Work through the Business Model Canvas (BABOK 10.8) Source: http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/big-data-analytics-should-be-driven-by-business-needs-not-technology

Elicitation & Collaboration #4: elicit through data Rather than focusing on data reporting at the end of the project, the data science team may spend up to 80% of the project on the initial Data Understanding phase Pro Tip: Use a Decision Model (BABOK 10.17) to isolate key decision points Source: James Taylor, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics (2011)

#5: Partner on architecture Business Analysis Planning #5: Partner on architecture IT Projects driven by data science can include: Build-vs.-buy analysis A data mart An analytics environment System enhancements to deploy data services Pro Tip: Use Data Flow Diagrams (BABOK 10.13) to capture the horizontal view Source: http://www.slideshare.net/RevolutionAnalytics/revolution-r-enterprise-100-r-and-more-webinar-presentation

#6: build the feedback loop Solution Evaluation #6: build the feedback loop Track the metrics used to measure & manage data-driven services: Product maintenance Customer support Regulatory compliance Pro Tip: Use Financial Analysis tools (BABOK 10.20) to demonstrate product ROI Source: https://www.google.com/analytics/analytics/#?modal_active=none

Data Science BA’s ARE In Demand Using these six tools, a capable DSBA can impact the 80/20 rule in data science projects: Reduce the 80% time spent on data & business understanding Improve the 20% time spent on in-depth modeling & testing Less expense. Higher quality. Happier customers. Productive data scientists. Source:https://upscored.com/blogpage/24/

We are Data Science BA’s: and we will rock this gold rush