In part one of this series we showed that life sciences companies must first sell knowledge products to corporate partners, regulators and investors well before they sell new therapeutic products to patients. Implementing a life sciences R&D cloud platform can reduce the time required to achieve key milestones, increase, increase organizational efficiency and serve as a source of competitive differentiation. This post will describe how engineering information architectures can accelerate the process of creating unique insights that enable companies to monetize their knowledge.
Naming a Common Problem in Life Sciences
When pre-commercial life sciences companies miss projected deadlines for finalizing a corporate alliance, filing patents, submitting regulatory documents or closing financing, it’s often difficult for senior leadership to diagnose the root cause. Talented employees appear to be working hard, and necessary resources are on hand, but it feels like there is friction at every turn slowing progress. Everybody wants to go faster, so solutions such as restructuring a department, buying a new piece of software or outsourcing a function are implemented. Nothing changes and frustrations mount. When point solutions fail to accelerate the pace of achieving milestones, it signals a systemic problem. Often, the systemic problem is a weak or non-existent information architecture that permeates the entire company. Without great information architecture and IT systems that deliver solutions, companies struggle to efficiently produce the core asset that enables monetization – unique scientific insights.
“Without great information architecture and IT systems that deliver solutions, companies struggle to efficiently produce the core asset that enables monetization – unique scientific insights.”
Unique Insights Are the Core-Asset of Life Sciences Companies
Life sciences companies establish organizational alignment by creating vision statements that set long-term aspirational goals, mission statements that describe corporate purpose and strategic plans that map how to win in target markets. Often, ‘how to win’ involves exploiting unique or differentiated insights about a therapeutic target, disease pathology or new molecular entity. For therapeutic discovery and development companies, unique insights are the core assets that attract the financial and human capital required to navigate long, expensive and high-risk R&D cycles. Without unique insights about a disease or therapeutic intervention, it’s an uphill battle to find investors and corporate partners that support a long-term vision.
How Are Unique Scientific Insights Produced?
Scientific insights don’t spring to life from the mind of a lone genius having an aha moment, but rather are produced by hard-working scientists that conduct experiments and perform analyses over extended periods of time. It’s important for companies to recognize that scientific insights are produced because, like any other good or service, a well-designed production process can optimize output. Unfortunately, many companies don’t think strategically about producing insights and thus don’t engineer processes designed to maximize the predictability and output of insight production.
“It’s important for companies to recognize that scientific insights are produced because, like any other good or service, a well-designed production process can optimize output.”
The process of producing unique insights starts by generating or acquiring data sets comprised of facts created by conducting experiments. These facts are analyzed, interpreted, processed and transformed into information that provides context and meaning to an area under investigation. Experts then assimilate multi-dimensional information about a target, pathology or molecular entity to produce new insights that can be exploited to advance an R&D initiative. Value is realized by turning insights into knowledge products such as scientific publications, patents, regulatory submissions or corporate presentations that enable a company to monetize its business before product commercialization. Importantly, no single insight produces value for a company all at once. Rather, countless insights build upon each other during the R&D process to create a compounding growth in knowledge that differentiates a company’s program from its competition.
What Do Rome and the Life Sciences Industry Have in Common?
Creating and selling knowledge in the life sciences industry is quite challenging. To win, a plan must be developed that details how a company will efficiently obtain, distribute and consume the resources required to produce insights. Roman aqueducts were engineered to capture water at its source, move it through purpose-built conduits, distribute it to numerous locations in the city and enable diverse use cases such as supplying private homes, public baths, latrines, watermills, farms, and mines. Aqueducts fueled the growth and success of Rome.
Today’s pre-commercial life sciences company must think about the flow of digital research objects (data, literature, information, software, workflow, etc.) much like Romans thought about water. Digital research objects must exist in a repository that can be tapped at will, distributed to different parts of a company, and facilitate diverse workflows that produce insights fueling company growth. A digital aqueduct capable of optimizing pre-commercial knowledge production is only built by implementing sound information architecture principles. Missing or poorly designed information architectures will throttle a company’s ability to transform R&D spending into monetized assets more than almost any other corporate oversight.
“Missing or poorly designed information architectures will throttle a company’s ability to transform R&D spending into monetized assets more than almost any other corporate oversight.”
We define corporate information architecture as the combination of rules, systems, and infrastructure that enable ALL important resources to be obtained, easily found, used to complete a task or advance a workflow, shared to streamline collaborative work and secured to protect intellectual property.
Information Architecture Drives Organizational Efficiency
Much has been written about biological data and the complex information landscape that today’s life sciences companies face. Powerful new instrumentation and the associated research protocols make generating data faster and more efficient than ever. Data (results from experiments) and information (synthesized findings in publications) are available in near-endless abundance both inside and outside a company. Clearly, this represents an exciting opportunity for the industry to ask and answer previously elusive biological questions. But, in an era of abundance where almost all companies can produce as much data as they want, the bottleneck shifts and the gating factors to success become organizing, processing, accessing and analyzing all that data.
Companies that rethink and redesign information architectures to effectively leverage abundant data and information will be best positioned to address the new bottlenecks. Designing systems that enable team members to quickly find, process, analyze and transform data into usable information that, in turn, leads to novel insights increases organizational efficiency. Without an information architecture designed for today’s environment, companies will struggle with digital transformation strategies, limit their ability to use resources productively, miss the value that cognitive computing, artificial intelligence, and machine learning applications could bring to their organization and, most importantly, decrease the pace of achieving key R&D milestones.
“In an era of abundance where almost all companies can produce as much data as they want, the bottleneck shifts and the gating factors to success become organizing, processing, accessing and analyzing all that data.”
Information Architecture is Often Ignored in Life Sciences
Information architecture is often ignored by decision-makers and rarely enters the corporate ethos before major problems surface to highlight its absence. One reason for this is that an in-house information architect doesn’t typically exist at early to mid-stage development companies. Without a qualified internal champion, little thought is given to building an information architecture that will align with business and research objectives, enable scientists to efficiently generate insights and accelerate the creation of knowledge products required to capitalize on the business. In the rare case, that information architectures are considered, the implementation task usually falls to a disinterested IT, finance or research team member that has competing interests from their primary job responsibilities. The result is a haphazard approach that is reactionary, only responding to the organization’s information needs in the moment, but with no infrastructure or strategy to maximize the usefulness of resources over time.
Companies that operate in this paradigm unintentionally create information labyrinths that team members can’t navigate when resources are needed to advance workflows or drive collaborative work. As companies grow and more resources are added, a flywheel effect is created perpetuating the problem and making effective resource utilization ever more challenging. This is the friction that companies often feel when driving toward milestone deadlines that are slipping.
“Information architecture is often ignored by decision-makers and rarely enters the corporate ethos before major problems surface to highlight its absence.”
The Signs of Poor Information Architecture
Life sciences companies that suffer from nonexistent or poorly designed information architectures will experience:
– Insufficient Coverage of Subject Matter: Key resources needed to make the most informed decision are missing, leading to greater uncertainty.
– Inability to Leverage External Resources: Available literature, data, and analytics (particularly open access) can’t be obtained and used at scale.
– Inadequate Search: Key resources aren’t integrated and findable through one enterprise search interface. Papers, data, analytics and other valuable corporate information are scattered across a company on hard drives and employee computers.
– Redundant Resources: Key data, literature and applications will be purchased multiple times because they can’t be found or shared across the organization. Capital is wasted.
– Proliferation of Point Solutions: Many different applications are required to solve very specific problems. The applications can’t be effectively shared or integrated and numbers grow over time with little evidence of value produced.
– Poor Interoperability: Papers, data, analytics and different file formats aren’t ingested, transformed and integrated in a single environment to enable multi-content workflows.
– Lack of Collaboration: Effective collaboration and messaging tools aren’t embedded within digital workflow environments slowing the pace of work.
– Delayed Timelines: Teams are frustrated and the time required to produce knowledge products like publications, patents, study reports, regulatory submissions, and corporate presentations are far too long creating missed opportunities.
– Outdated and Unscalable Infrastructure: IT solutions have been pieced together from vendors outside the life sciences industry making expansion and new FTE onboarding difficult. Vendor lock-in creates an inability to capitalize on powerful cloud-based computing advantages.
What Does Great Information Architecture Look Like?
As we mentioned at the beginning of this post, life sciences companies typically have clear vision statements, mission statements and strategic plans. From here, management can define what digital research objects are required to meet company objectives. To reiterate, digital research objects are the data sets, scientific publications, patents, research protocols, study reports, white papers, scientific software, workflows, etc. used by a company to derive unique insights about a therapeutic target, pathology or molecular entity. The scale and variety of digital research objects are large and rapidly growing so companies must decide what will be produced in-house and what will be procured from external resources.
Once a company defines what it needs, attention should next be focused on mapping requirements that empower the consumption of digital research objects in the course of typical workflows. Questions like:
– What is the comprehensive list of needed resources and where will we get them?
– How can we make diverse resources easily findable and accessible?
– How can resources be pushed/pulled to the right person at the right time?
– What type of data and information processing is required to make diverse file types interoperable and easily used?
– How can sharing and collaboration capabilities be incorporated from the beginning?
– How will resources and work products be stored to facilitate reuse?
– What type of control and authorization is needed to secure work products?
– How will we scale with growth?
“A comprehensive and integrated platform enables researchers to move fast by dynamically choosing and combining the exact resources required to complete a workflow without the typical delays of searching for missing information or waiting for IT to build a one-off data mash-up.”
After deciding what digital research objects will be consumed and how consumption will fit into routine workflows, attention can be turned to designing infrastructure and a technology stack that will execute the plan. The emergence of cloud computing, cheaper storage, more powerful CPUs and rapidly scalable systems that can be securely accessed from anywhere, represents a new era where IT strategy can be a source of competitive differentiation. Skilled engineers can now build modern life sciences IT stacks that tightly integrate previously unconnected resources. A comprehensive and integrated platform enables researchers to move fast by dynamically choosing and combining the exact resources required to complete a workflow without the typical delays of searching for missing information or waiting for IT to build a one-off data mashup. Figure 1 represents how effective information architecture can be developed to guide the build of a truly integrated knowledge production platform.
Figure 1
Source: Catalytic Data Science Inc.
Great Information Architectures Create Long-Term Value
Great information architectures not only create platforms that store, distribute and compute on all that a company knows, but also enable competitive advantage. Building the infrastructure to obtain, transform, integrate, distribute and consume digital research objects fast and efficiently decreases the time required to monetize knowledge products. Put another way, great information architecture can drive increased R&D ROI. Great information architecture also future proofs a company for growth and positions it to benefit from the inevitable emergence of new technology. For example, cognitive computing, artificial intelligence, and machine learning approaches might create significant value for a company but it will be impossible to deploy on small-scale resources that haven’t been normalized for computational use and exist in countless unlinked silos across an organization. The need for pre-commercial life sciences companies to produce differentiated knowledge products is indisputable and great information architecture is a critical step to getting there.
What’s Next?
In our next post we will write about the infrastructure and technology stack required to create an exceptional corporate knowledge production system.
In the meantime, If you would like to learn more about Catalytic and how we are helping life sciences companies be more successful in driving competitive advantage, please contact us here or request a demo of the Catalytic platform.