The Business Case for a Multi-Persona Data Science Platform

What it Does

A data science platform enables users with varying levels of expertise to utilize data science, bridging the gap between technical and business competencies. It empowers everyone in the organization to perform predictive analysis and obtain data-driven business insights.

Benefits

• Halve decision-making time by leveraging data science-driven insights across the organization.
• Achieve labor cost reductions of 20% to 30% by enabling business stakeholders to perform analysis previously done only by data scientists.
• Increase top-line revenue by 5% to 15%, in a sales use case, through broader adoption of data science across the organization, due to more intelligent and timely decision making.

Urgency

Moderate: With data scientists and engineers in short supply, accessible data science platforms empower business users to benefit from these tools. Adoption of multi-persona data science platforms is strategically important to business success.

Risk Level

Moderate: As with all analytics-related initiatives, data security and protection vulnerabilities pose risks. Poor model transparency, inadequate training and change management, and deployment struggles with business user-created solutions are risks.

30/60/90 Plan

• 30 days: Identify use cases and resources, plan a roadmap, and identify data to be used. Prioritize use cases on impact and feasibility based on availability of required data.
• 60 days: Set business goals and evaluate solutions before adopting a platform.
• 90 days: Deliver initial proof-of-concept and then scale across use cases. Monitor and optimize ML models.

Time to Value

Initial value of integrating a multi-persona data science platform can be realized within three to six months. Further value will accrue at the one- and two-year milestones.