1. Business Analysis for Data Science Teams
Susan M. Meyer

2. Data Science & Big Data
Yet another Gold Rush?

3. 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

4. St. Louis: A traditional IT powerhouse

5. 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

6. 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

7. 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

8. “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

9. 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:

10. 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

11. 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)

12. 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

13. #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:

14. 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)

15. #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:

16. #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:

17. 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:

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