Business Analysis for Data Science Teams Susan M. Meyer Data Strategy Lead, Supply Chain Quality Testing Monsanto

data science teams: Fluid & Flexible Roles Primary skill sets of blended data science teams: Domain expertise Predictive analytics Information technology Primary deliverables: Commercially useful predictive/prescriptive models Analytics solutions required to deploy the models Customer support services including risk / compliance Coordinating the involvement of experts in multiple disciplines--as well as business stakeholders—often requires the skills of a specialized business analyst… The Data Science BA© Source: Harlan Harris, Data Community DC

Data Science teams: Where are we Apr. 2016 McKinsey Survey Feb. 2018 HBR: Data Science Translators ATTRACT RETAIN Analytics Translator Skills: Business analysis Domain-specific use case expertise Data and information expertise Structured analysis methods Project management skills Innovation orientation McKinsey Global Institute predicts the US market will need 2-4 million data science BA’s by 2026 N=519

IIBA: International institute of Business analysis Founded in 2004 in Toronto, Canada with 37 members Now a global network of 29,166 members, 120 Chapters, 15 branches, 51 Academic Members and 303 Global Corporate Program Members Certifications include: ECBA™ – Entry Certificate in Business Analysis CCBA™ – Certification of Capability in Business Analysis CBAP™ – Certified Business Analysis Professional CBATL™ – Certified Business Analysis Thought Leader

http://www.iiba.org/Careers/Becoming-a-Business-Analyst/BaBA/IIBA_BA_Career_Road_Map_Int_Sr.aspx

Requirements Life Cycle Management Predictive Model Development Process 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 Understanding is a critical initial phase that should be led by a Data Science BA© Data Understanding & Data Prep must include domain experts, and may also be supported by data analysts or data engineers Modeling, Evaluation & Deployment phases may evolve into separate projects or run concurrently; results should be evaluated by the Data Science BA© *Serving data science since 1998 Source: http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html

agile data science roles Requirements Life Cycle Management agile data science roles Product features driven by data science can include: Model-driven scores and ranking algorithms Data transformation services (API’s) Decision support services (business rules, decision tables) Due to the “unknown unknowns” in data assets, data science projects are often run as Agile or scrum projects As model development becomes productionalized, organizations that prefer traditional project methodologies can develop standardized project plans

Six Best Practices for Data Science BA’s Identify business value drivers & metrics Elicit requirements through data discovery Enable the lifecycle for model development Develop a data strategy Develop a decision model Define the analytics solution 1 2 3 4 5 6 BABOK v3 (2015)

Define Business value drivers & metrics Business Analysis Planning Define Business value drivers & metrics Business requirements in the early planning phase may take the form of strategy documents such as: Policy whitepapers Current vs. target state business architecture Value chain analysis Legal / risk analysis Be prepared to define scope using data assets available to the team: Operational data stores Analytic data stores Early definition of the key business metrics that the model must predict or prescribe: Detection Classification Automation Data Utilization Competitive Response Technology Adoption Customer Demand Resource Utilization

Identify the business analytics use cases Business Analysis Planning Identify the business analytics use cases Ag Tech Seed Testing Breeding Decisions Image Analytics Supply Chain Supply / Demand Planning Route Optimization Logistics Financial Credit Scoring Risk Analysis Asset Optimi-zation Security Fraud Detection Network Threat Detection Marketing Campaign Planning Marketing Mix Offer Optimi-zation 1950’s – 2000’s 1980’s – 1990’s 2000’s Sources: Chambers & Dinsmore, Modern Analytics Methodologies, p. 107 (2015) Sanders, Big Data Driven Supply Chain Management (2014)

The Data value chain: From Commodity to Value Data Suppliers Data Inputs Process Activities Data Outputs Data Consumers Manual to Automated Standardize Simplify Curate Manual -> The SIPOC (or COPIS) model used by Supply Chain Stewardship team identifies the economic value of automating data management activities that are currently manual: Begin with key data outputs required by customers / stakeholders Estimate the value of shifting or eliminating resources (labor, inventory, process time) in the value chain

Flip the Triangle: Begin with the data Elicitation & Collaboration Flip the Triangle: Begin with the data Data science teams tend to focus primarily on data features, rather than end-user behavior Build information maps & conceptual models to link the concerns of subject matter experts with data scientists & enable a common language BA’s with data access skills such as SQL may have an advantage in the data understanding phase of the project, but data discovery tools are widely available Share insights from data profiling with business stakeholders to illuminate data features in the target domain Capabilities Information Maps Data Profiles Pro Tip: Develop concept models (BABOK 10.11) to facilitate cross-functional team deliverables

The Data Science BA: Begin the Journey Business analysis & program level support is critical to the success of data science teams Delivering innovation Role clarity Scoped projects Productive data scientists Engaged stakeholders Source: http://www.scaledagileframework.com