Rancho Biosciences Consulting
We help R&D organizations turn fragmented data into a connected foundation, from strategy to outcomes, so scientists can move faster and bring better treatments to patients who are waiting.
Meeting the Questions That Shape Modern R&D
Leaders across research, development, and CMC are asking the same questions about how to put data to work. We meet those questions with a connected set of consulting capabilities.
The Questions Leaders Are Asking
How We Help: Our Consulting Capabilities
Data Strategy Definition
It's critical to align strategic goals to a broader enterprise strategy. This typically occurs through establishing a north star, understanding the value implications, prioritizing focus areas, planning proof-of-value experiments, and developing a near and long-term roadmap for execution and future data enablement.
See CredentialOperating Model & Governance Definition
The future of talent is evolving and for successful use of data at scale, it's imperative to define and align on future roles and accountabilities across the business, R&D IT, and enterprise IT.
See CredentialBusiness & Data Journey Mapping
Defining the future state process to address critical questions is important to improve the future experience leading to accelerated productivity through broader data adoption and use.
See CredentialBusiness / Value Case Definition
Understanding the value and productivity implications of business or technical change is important for planning, go / no go decisions, and improved operational efficiency.
Vendor Assessment & Planning
The rise in the amount of technology vendors continues to grow, assessing capabilities to your specific needs are critical.
Data Quality Assessment
Identifying and assessment data for FAIR readiness tied to business value and tangible use cases is critical for near and long-term value realization.
MVP / Implementation Planning
Defining and prioritizing requirements through an agile approach tied to personas is important to realize value quickly and ensure the right features are developed.
Proven Partnership, From Strategy to Outcomes
Three representative engagements with pharmaceutical organizations, each showing the business context, the questions we addressed, our approach, and the value delivered.
Data Strategy Definition
Business Context
To maintain a competitive edge and enable insight generation and operational efficiency through data, a pharmaceutical organization embarked on a journey to use artificial intelligence to accelerate research, development, and CMC capabilities.
Questions to Address
- What data and technology foundation should be put in place to drive data-as-a-service?
- Do we understand the connection between use cases and the future data needs tied to an overall data strategy to unlock the most value in the near and long term?
- What external data is available to supplement my internal data for our use cases across research, preclinical, CMC, development, and commercial?
- What immediately needs to occur to enable near term value and what is required to scale long term?
Our Partnership Approach
Data Use Case Strategy
Inventory or define use cases, develop a framework for assessment and future prioritization inclusive of business value and the external data landscape to drive the strategy.
R&D Data Assessment
Identification of priority datasets, quality gaps within and across R&D, and mechanism to make data AI-ready tied to business value.
Future Architecture Definition
Definition of the future architecture factoring in internal systems used across R&D to develop a modern data infrastructure.
Data Strategy Roadmap
Immediate (next 60 days), near term (next 1 year), and long term (next 3 years) plan across people, process, and data / technology.
Value Delivered
Maximized value realization, avoided siloed activities, and ensured data readiness across R&D.
Established mechanism to scale AI faster, reduce cost and complexity, and enable sustainable growth and focus.
Operating Model & Governance Definition
Business Context
To prepare for AI use across the enterprise, a pharmaceutical organization embarked on a journey to establish an operating model with clear accountabilities across business and technical stakeholders to drive new ways-of-working.
Questions to Address
- What are the critical roles for success, what are their core competencies, and how will they work in the new model?
- What are the optimal organizational constructs to drive data-as-a-service across research, development, and commercial domains?
- What are the key capabilities required to enable the democratization of data, and which capabilities should be centralized or decentralized?
- How do we start small and expand to enable data-as-a-service across the organization?
Our Partnership Approach
Data-as-a-Service Operating Model
Define overall data goals, assess current resource responsibilities and organizational capabilities, and align on the future organizational construct (e.g., decentralized).
Data Governance
Establish process and hierarchy for decision ownership across capabilities.
Resource RACI and Process
Define future responsibilities tied to required capabilities, map current to future changes, and establish future ways-of-working.
Operating Model Roadmap
Develop near and long term plan for organizational change and upskilling / hiring of new critical roles for success.
Value Delivered
Improved R&D decision speed and operational efficiency across R&D teams.
Enabled faster time-to-insight in the near term while establishing a scalable model for data governance and management.
Business & Data Journey Mapping
Business Context
To accelerate and improve data for use / reuse across R&D to ultimately bring better therapeutics to patients, a pharmaceutical organization embarked on a journey of operational efficiency across R&D to optimize the end-to-end drug development process.
Questions to Address
- How do we connect our business ways-of-working and data / technology to promote a shared understanding?
- How do we best utilize our data and understand what is required to accelerate operational efficiency?
- How do we establish a common data domains for the creation of data products?
Our Partnership Approach
Business Outcome Alignment
Current state and future state process definition, user journey definition by persona, and KPI development for future tracking aligned to value streams.
Data Journey Mapping
Capture input / output of data and technology use aligned to business processes including the identification of gaps and data recommendations for downstream use.
Data Domain Definition
Development of data domains based on business need to establish a common understanding and consistent use of data for multiple use cases.
Value Delivered
Accelerated cycle times and improved business / technical ways of working across internal functions and geographies.
Established process and data foundation for data product creation reducing burden on teams.
From Fragmented Data to Confident Decisions
Every dataset we touch helps scientists see more clearly, move faster, and bring better treatments to patients who are waiting. Let's talk about where your data journey goes next.
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