Case Study

High Performance R&D in a Changing Environment

By Anil Krishnananda


Large U.S. government research and development (R&D) organizations will continue to face funding challenges due to the nation’s changing fiscal environment. These organizations are projected to see a decrease of over 5% in appropriated funding in FY 2020-21 due to shifting priorities and economic impacts of the ongoing COVID-19 pandemic.

To survive, and in fact thrive, R&D organizations must do more with less and identify opportunities for alternative sources of revenue. Research enterprises that secure both “direct” funding (from their parent organizations) and “reimbursable” funding (contract research dollars) need to realize that in many cases their greatest management discretion lies with the reimbursable funds. What this means in terms of the ability to influence outcomes is that direct funding is often set in stone, but organizations can more effectively market for additional reimbursable money.

Yet, many research organizations are poorly positioned to pursue a reimbursable-focused growth strategy. The reasons are numerous, but include an inability to adequately characterize existing reimbursable business, limited to no forward-visibility into trends in the broader reimbursable market, and internal systems, processes and cultures that are incapable of supporting such an initiative.


Guidehouse espouses eight components (see Figure 1: R&D Operating Model Framework) that, when combined, enable R&D enterprises to function on a high-performance basis. Systems, Data, Processes, Architecture, Behavior, Organization, Governance, and Performance Management all play critical roles in ensuring an R&D enterprise understands its current business and can optimally position itself for the market.

Figure 1:R&D Operating Model Framework

It turns out that relevant data is only the first step in a chain of actions necessary to secure new reimbursable business. Just as important is converting that data into actionable information, using it and other sources externally to identify market opportunities, developing a road map for what must be done to position the entity for a larger role in the market, and then initiating the pursuit. One can think of these steps (outlined in Figure 2) as a form of business model transformation — but in many ways, these steps are just good business practices. In the private sector, they come naturally, and out of necessity, since no company is guaranteed future revenues. In the public sector, they are still important, maybe more so today than at any other time, but they are skills that are infrequently exercised, and as a result, typically poorly done.

R&D organizations need to understand their current situation, potential challenges to their businesses, and ways they can improve their business intelligence to ensure future success. It is essential that labs identify the right set of investments in facilities, programs, people and awareness, and the corresponding operating model requirements, to drive superior market positioning. All this must be done without placing an undue burden on staff.


Figure 2: R&D Operating Model Transformation Stages

Technology enablement should be a key lever to increase the effectiveness and efficiency of research project management operations across the life cycle — from opportunity intake to project execution and ongoing portfolio analysis. Enablement also ensures all involved organizations are a part of the process, using systems effectively to deliver actionable information, and capitalizing on the resulting business intelligence to pursue growth objectives. One of the most important side benefits is that data management is streamlined so that an authoritative source for data is maintained for the benefit of all users — saving time, resources, and repetitive data calls (as well as the morale of your staff).

Data should only need to be captured once, and thereafter used as needed. Manual transfer of data should likewise not be required if all necessary interfaces and linkages are implemented. Finally, use of visualization tools are critical to synthesizing the data into actionable business intelligence. True awareness of trends in the reimbursable portfolio allows senior leaders to focus resources on those priorities that will generate the greatest possible benefit internally in terms of efficiencies, and, externally, in terms of market positioning. Of course, none of this is possible with inaccurate or incomplete data, so getting high-quality data is still the most important step. A big part of data management is built on effective governance and policies that in many R&D settings are poorly articulated. For organizations further along in their operating model transformations, artificial intelligence and machine-learning capabilities can be exploited for predictive and prescriptive analytics, all of which give the organization unprecedented power to understand its business and aggressively pursue new business in the marketplace.

One final note: Transformation is more about the journey than the destination. It’s a philosophy centered on performance and built on knowledge. Organizations cannot truly understand their markets if they don’t first understand their businesses. Furthermore, no self-respecting R&D enterprise should be happy with the status quo — even governments. All organizations must grow to thrive. The old government mantra that growth is for the private sector is a fallacy. All R&D organizations must set a growth agenda — it’s not just about top-line funding either — growth comes in many forms, including capability expansion, customer-base diversity and development, improved morale, more efficient operations, updated facilities, etc. Operating models exist only to ensure the long-term stability, survival, and growth of the organizations they were created to support.

It is not possible to be a world-class research enterprise if the fundamental data intelligence of the entity is rooted in the distant past. Leaders must simultaneously look inward and outward, and they need to build a data management ecosystem that serves the organization, not the other way around.


In a recent engagement with a Department of Defense research and engineering command, Guidehouse was able to identify a reimbursable market worth ten of billions of dollars. Using the high-performance operating model we outline here, we were able to identify the business process improvements, technology updates, and other changes to gather the data necessary to pursue the market opportunity. Of course, having the right data, and in a format that allows strategic insight, is only the first step. Other things must happen to ultimately secure growth, but organizations cannot even approach the pursuit if they don’t first understand the nature of their current business.

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