Maturing Decision Quality for Pharma R&D
“A great practice in portfolio decision quality strategically manages uncertainty and offers decision makers more reliable foundations to make hard decisions.”
Progressing to enterprise decision quality is a journey. Although independent decision modeling may feel quicker and more agile, the value of an integrated approach yields significant transparency and value across the organization. Enterprise decision quality enables a far more strategic outlook for future decision making and promotes far greater decision agility across the enterprise.
The Case for Enterprise Decision Quality in the Pharmaceutical Industry
Within biopharmaceutical drug development, R&D investment decisions scale from millions to billions of dollars of potential value for a company. As such, it is understandable that making such investment decisions is a complex, difficult, and risky undertaking. A basis for the hardship lies in the uncertain nature of making predictions about the future. New, ambitious R&D projects present excitement, innovation, and commercial opportunity for the enterprise. These however carry significant investment volatility that, if considered independently, could result in outcomes that can range from a blockbuster success to a catastrophic failure. Methodically assessing the risk and uncertainty surrounding individual R&D investment decisions, while assessing those investments in the context of the company’s broader investment portfolio, enables a company to progress individual project investments with confidence that the aggregate outcome of individual project failures and successes will net a positive return on investment for the portfolio.
Establishing a thoughtful, efficient, and responsive enterprise practice for major R&D investments will ensure that an organization can grow the value of its R&D investment portfolio, confidently progress risky innovative investments, and stabilize its long-term growth projections.
The Enterprise Decision Quality Framework
An enterprise decision quality framework will formalize key steps in the decision process as well as the required engagement with key decision stakeholders. Establishing such a framework for R&D portfolio investment decisions allows for the construction of robust and achievable decision options that aim to achieve the company’s strategic objectives. To build this framework, one must collect meaningful information in a way that allows investment alternatives to be evaluated against each other with consistently calculated values and trade off considerations. These key metrics and evaluation criteria must also be applied consistently across portfolio investments so that they may be appropriately evaluated within a portfolio context. Also, through the formulation of the decision framework, decision influencers will understand how they contribute to the decision, why the decision options are being considered, and how the options are being evaluated. By and large, enterprise decision quality is about getting decision makers and decision influencers to align in their understanding of the multiple dimensions of the decision and to ensure that the organization can confidently commit to action.
Implementing a Decision Maturity Model
An organization’s readiness to mature is essential to effectively implement a maturity model. A Wikipedia description of a maturity model states the following:
“Maturity is a measurement of the ability of an organization for continuous improvement in a particular discipline. The higher the maturity, the higher will be the chances that incidents or errors will lead to improvements either in the quality or in the use of the resources of the discipline.”
From a decision quality perspective, the maturity assessment will require evaluating behaviors, methodologies, and outcomes of past individual decisions to enable the organization to adapt to these learnings. Furthermore, the ability to overcome incidents and errors that lead to quality concerns and unpredicted outcomes requires an organization to broaden its capacity to account for and deal with uncertainty. Ultimately, the desire for each investment decision is to add value to the company, to drive consistency and agility in decision making across the enterprise, and to improve the quality of decision outcomes.
Understandably, enterprise decision quality maturity is a journey for an organization and progresses best by adhering to a staged approach. We propose that enterprise decision quality can be developed, assessed, and progressed through the following maturity levels (Figure 1):
A Trajectory For Portfolio Decision Quality and Agility in Pharma R&D
The value of decision maturity as pertains to biopharmaceutical research and development investments can be summarized in the chart in Figure 2. The illustration highlights that individual asset decisions may initially feel burdened by the alignment processes imposed by a portfolio structure but both asset and portfolio decisions improve in agility and quality as the enterprise matures its practice. Most important is that the quality of decisions improve as key decision influencers and the broader organization understand and participate in the decision process. As a result, the enterprise is more aligned in supporting strategic investment decisions, in better managing overall portfolio risk and achieving greater value for the company.
Levels of the Decision Analysis Maturity Model
Level 0 – Gut Feel, Intuitive Decisions
The organization does not apply decision frameworks in decision making. Decisions are loosely structured and guided by the experiences, intuition and biases of the decision maker and decision influencers. This approach tends to leverage structured data inputs in a way that supports the decision.
Level 1 - Ad Hoc & Independent Analysis
This first level capability enables an organization to effectively support decision options under uncertainty and enables decision makers with analysis and comparable decision options to guide an informed decision. This capability will persist throughout an organization’s maturity journey, however the approach, the quality, and the agility with which an organization approaches ad-hoc analytics is improved throughout its maturity cycle. When an organization is in this first level, the analytic process is unstructured, and the model inputs may be dispersed throughout the organization. The decision practitioners begin their analysis approach by building decision models from scratch and scouring for reliable data inputs to support the model. Such ad-hoc models tend to be time consuming to build and may suffer the effect of “beta software”, where due to fast turnaround requirements, the decision model may be burdened with unvetted reference data, quality control challenges and inadequate testing. These models, if built and tested appropriately should prove useful to guide individual decisions. They tend however to be discarded after the individual analysis they were built for. They may also carry an inherent risk that, if not tested adequately, may inadvertently reveal misleading or even inaccurate results. The ad-hoc models, often due to technical limitations, may be limited in their scope of analysis and may have limited scalability potential when analysis insights reveal secondary analytic questions.
Level 2 - Standardized Processes, Models & Measurements
A learning and maturing organization will quickly move from ad-hoc and independent analysis to a level two maturity. This is where an organization begins to standardize the process of decision making by implementing agreed decision frameworks, common decision models and aligned measurement criteria for comparison of decision options. This maturing and reusable decision framework adds efficiency to the decision process and increases familiarity and credibility of the decision models amongst stakeholders. This maturity level also brings the organization closer to the enterprise decision quality construct, where decisions are not only assessed independently but with the use of a common process and model, may be compared with or evaluated against one another. Furthermore, the organization begins to collect actual outcomes of past decisions and retains that information within a knowledge repository. The organization will now be able to look at the criteria and outcomes assembled for one decision then apply a similar kind of method to inform another decision. This signals that the organization is leveraging the value and knowledge acquired from past work to further manage uncertainty, to adapt to changing circumstances, and to better guide and inform future decisions.
Level 3 - Enterprise Trusted Data & Analytics
On the third level of the maturity model, the organization moves into a structure where not only does it have common processes, models and measurements, but also establishes a common data management practice and platform. Here, the key data that informs decisions within the enterprise’s core business practice is collected, curated, organized and integrated to be used as inputs for business decision making. At this maturity stage, the organization establishes active involvement and collaborative engagements with key business data owners to curate and validate trusted data sources. With managed data quality, the attention of decision influencers can then be focused more wholly on the quality of the decision model and the value of decision insights without getting distracted by the quality of model inputs or the credibility of the decision model. This increases decision maker’s trust in the analytic processes and decision influencer's buy-in. It then ensures that decision insights can be more confidently committed to action. At this level of maturity, the organization will now be at a place where the decision makers and decision influencers are gaining value in their practice and through their improved collaboration. The organization may at this level already be collaborating in a common shared technology platform, possibly in live sessions, to interrogate the decision model, to compare decision option metrics and to commit to decision recommendations.
Level 4 - Real-Time Scenario Planning & Analysis
At the fourth level, the organization is leveraging a dynamic, collaborative decision platform and framework to draw together key decision criteria and to analyze decision scenario options in real-time. Here, the organization has matured its business decision processes, standardized decision models, managed trusted data references and delivered an interactive, collaborative environment for decision influencers so that they can collectively analyze enterprise goal seeking scenarios with upside and downside sensitivities, that will inform how to confidently progress the goals of the enterprise. This level of decision maturity signals a very agile and strategically focused organization that is well informed about its internal potential and can confidently respond to the risks and competitive pressures from the environment. At this phase of maturity, the organization is tracking its decision analysis, decision behaviors and decision outcomes to augment internal decision intelligence and to improve future forecasting and predictions of environmental trends.
Level 5 - Augmented Prediction & Learning
In the last level of decision maturity, the organization is heavily leveraging machine learning and predictive insights from a plethora of information sources including industry data, internal performance databases, and curated enterprise decision intelligence. This augmented prediction reference can signal potential business opportunities, comprehensive competitive and investment risk assessments, and can propose strategic decision options that may otherwise not be evident to a business decision maker. Augmenting human decision intelligence with the predictive power of rich data sets, trained machine learning and predictive algorithms, will enable the organization to respond more rapidly to business opportunities, to better manage business pressures, and to more effectively shape and execute corporate strategy.