The data deluge in the life sciences industry continues, with ever-increasing sources and streams of information being generated at an exponential pace – and the numbers are astounding. It is estimated that by 2025, 463 exabytes of data will be created each day globally1. Of that data, an expected 40 exabytes of storage capacity will be required for human genomic data alone2. To put that into perspective, it has been said that just five exabytes would be equal to all of the words ever spoken by mankind.

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Biopharma’s have the opportunity to accelerate their growth through strategic phases of transformation, fighting through the noise to become data-driven enterprises ready for the future exabytes of data.

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But while the growing capacity and requirement to capture data is important, it isn’t the key concern here. The true value is in acting on the insights that data can generate to unlock value and become a truly data-driven enterprise. Already, it is estimated that improved use of data contributes to reducing drug development time by 500 days and overall cost by roughly 25%. Using data from across the organization for quality control in manufacturing can increase productivity by up to 50%3. The question is no longer if you should be investing in strategic data reinvention – it is how.

Within the industry, the majority of life sciences companies today have initiated strategies to capitalize on this data boom, although most are still exploring options for true data-driven reinvention. Most, but not all. There are some companies that we can look toward as examples of successful data transformation.

In this series of blogs, we’ll examine the current state of data-driven reinvention in the life sciences industry, while also looking at strategies companies have taken to successfully to implement enterprise-wide data transformation and unlock the value it offers. By learning from these successes, life sciences companies have the opportunity to accelerate their own growth through strategic phases of transformation, fighting through the noise to become data-driven enterprises ready for the future exabytes of data.

Three Horizons for Becoming a Data-Driven Organization

In any organization, one can typically find examples of some data-driven successes. Often, though, these successes are achieved sporadically through short-term projects, narrowly defined problem solving, and a lot of heavy lifting with deficient data. These successes are often fraught with costly investments in time and resources – and often cannot be repeated, improved, or scaled. The result? Many CIOs, CTOs, and CDOs struggle to describe where their organization stands on their journey to maturity – which our experience has shown to be a critical component in getting buy-in for scaling digital strategy throughout an enterprise.

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84%

of life sciences executives report that they won't achieve their growth objectives unless they scale digital capabilities4

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Most life sciences executives recognize the requirement, with 84% reporting that they won’t achieve their growth objectives unless they scale digital capabilities4. In response, we have identified three “Horizons” that describe the status of data maturity in a company that can serve as building blocks toward becoming a truly data-driven enterprise.

Horizon #1: Proof of Concept

Few companies stay in this early-phase horizon, in which value is sought through budget-friendly, proof-of-concept (POC) projects. Here, organizations gravitate toward areas such as image analysis or in silico screening as areas for testing for analytics and AI efforts, however, these experiments are often embedded into business units that merely cooperate, but do not collaborate, creating additional processes and costs. These projects are generally standalone, with tight budgets, and priorities protected within small functional domains.

While some successes might be achieved through narrowly focused effort, projects in this horizon are often hamstrung by talent fragmentation, lack of priority sharing, and significant underinvestment in fundamental data needs. Moving out of this horizon requires bravery and bold investments, but for those that are here, the case for moving forward is clear: digging out from the enormous expanses of data is only going to become more challenging.

Horizon #2: CxO Focus

In horizon two (H2), life sciences companies have invested in platforms and have begun to operationalize their advanced analytics and machine learning (ML) capabilities with more maturity in operationalized data supply chains. The critical requirement here is that the organization actively recognizes that data assets and analytic-driven insights are interdependent – and the organization’s leaders execute priorities with this awareness in mind. C-suite executives should be communicating data priorities throughout the organization. This top-down focus results in noticeable improvements in the value that can be captured, including intelligent automation and predictive reporting – which are both notable upgrades from horizon #1. 

As an example of a well-established H2 company, we can look toward Edico Genome (acquired by Illumina in 2018). In late 2017, when Edico partnered with Children’s Hospital of Philadelphia to analyze 1,000 human genomes at high throughput, they employed next generation sequencing (NGS), which provides concurrent, synchronized testing of multiple genes in real-time. NGS not only increased the speed of analysis, but also reduced the cost of processing, down to approximately $3 per whole human genome, while accelerating the speed at which results were delivered. They were able to successfully use data and digital technology to deliver efficient and effective insights in rapid fashion.

Horizon #3: Data as a Competitive Differentiator

Horizon #3 (H3) is the North Star of data-driven reinvention. Companies in this phase have successfully gone beyond experimentation phases into full operationalization of their data and machine learning (ML) products. Beyond the functional platforms of H2, here integrated platforms and scaled use of data reshape the business and operating models, bringing innovative products and new services to market.

As an example, we can look Moderna Therapeutics, which employed the power of cloud in order to compress the time needed to advance drug candidates to clinical studies, achieving uncommonly fast preclinical drug development. CDO at Moderna, Marcello Damiani, explained in a case study from AWS that integrating cloud with their proprietary Drug Design Studio, allows scientists to have an idea for a unique protein, order it online that day, and be running preclinical experiments on it in less than a month. “In traditional pharmaceutical research and development,” he says in the report. “It would take years to get to the point where you are ready for even preclinical studies, much less testing your drug in humans.”

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CASE STUDY: NUMERATE SIMPLIFIES A COMPLEX DRUG-DISCOVERY PROCESS USING DATA AS THE DRIVER

Numerate (acquired by Valo in late-2019), a discovery-stage life sciences company, has started to use machine learning (ML) technologies to more quickly and cost-effectively identify novel molecules that are most likely to progress through the research pipeline. Brandon Allgood, CTO and co-founder of Numerate at the time, explained that they implemented an algorithm-centric model to help pharmaceutical companies take massive amounts of data to accelerate the identification and optimization of clinical drug candidates.

The result? The company discovered drug candidates five times faster than the industry standard while reducing costs by seven-eighths5.

Recognizing Your Horizons

It is estimated that by 2025, a quarter of the people in developed nations (and half that in less-developed countries) will have their genomes sequenced. Understanding the true status of your company’s data reinvention is critical to building an effective data strategy to capitalize on digital trends like this. Unfortunately, many stakeholders mistakenly equate their journey to cloud alone with a move to becoming an H3 company. While this is important, the reality is that the move to cloud is but only one step on the path toward full implementation.

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Horizon #1

POC Study: any project or company that is still in Proof of Concept phases are considered Horizon #1.

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Horizon #2

Supply Chain / Manufacturing:
- Optimization
- Traceability

Corporate Functions:
- HR: Workforce of the Future
- Legal: Text-Based Analytics for Identifying IP

Commercial:
- Real World Data Analytics
- Predictive Analytics for the Patient Journey
- Marketing Optimization

R&D:
- Patient Enrollment Analytics
- Risk-Based Monitoring at Trial Sites
- Novel data sets such as Quality of Life Scores

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Horizon #3

Evidence-based treatment protocols

Data collaboration and data sharing, e.g., sharing with:
- Payers
- Governments
- Other Risk Bearers
- Providers
- Wellness Initiatives
- Service Vendors

Sharing in new ways, e.g.:
- Usage and claims audits
- Supply chain / likelihood of stockouts
- Data aggregation services

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Life sciences companies have industry-specific touchstones that can help them define their current maturity level. Recognizing which horizon your company is in will directly inform the next priorities and steps to be taken. The good news is that wherever your company currently sits in its journey, there are clear pathways to becoming an H3 enterprise. In our next post, we’ll examine the four pillars life sciences companies should embrace to become dynamic and truly data-driven.

In my next post, I’ll explore the four pillars of data reinvention, including pathways to implementation in life sciences. In the meantime for a deeper dive, I recommend the Accenture report, AI: Built to Scale. If you’d like, you can contact me directly at kingston.smith@accenture.com.

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Source:

1 https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

2 https://medicalfuturist.com/the-genomic-data-challenges-of-the-future/

3 https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-pursuit-of-excellence-in-new-drug-development

4 https://www.accenture.com/ca-en/insights/artificial-intelligence/ai-investments

5 https://aws.amazon.com/solutions/case-studies/numerate-case-study/

 

Kingston Smith

Managing Director – Applied Intelligence

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