Biopharma’s Journey: Four pillars to data-driven success

Digital reinvention in biopharma is at the heart of current and future business in life sciences. Its true virtue is the ability to help life sciences companies connect vast pockets of data, while collaborating better across the ecosystem to create more meaningful patient and healthcare provider (HCP) experiences. We can look toward evidence such as the improvement in biomarker-based cancer drug development. By using data, this area of biopharma has demonstrated a much higher success rate, improving from 3.4% to 8.3% in the overall oncology drug approval rate in 2015.

In my first post, I explored the three horizons that describe the status biopharma companies find themselves in on their journey to becoming truly data-driven enterprises. While the majority remain in the second horizon, the goal of moving into the third horizon and becoming a truly data-driven organization does have several identifiable pillars for success. In this post, we’ll take a look at those pillars and the steps that can be taken to move toward the third horizon. 

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Becoming a truly data-driven enterprise

In our work with clients, we have identified several capabilities or disciplines, which we call the “four pillars,” that are critical to data-driven reinvention. Biopharma companies that focus on the following four pillars are more likely to achieve their data objectives and capitalize on the exponential growth of valuable information. 

Pillar #1: Data Management & Governance

Key to true implementation of a robust data management and governance strategy is the adoption of a “fit for purpose” approach for intelligent solutions to data management, automation, and integration challenges with these attributes at the core.

1. Automated Data Management Processes: critical data elements are aligned with business value drivers and next-generation curation of metadata powered by automation
2. Data Lineage: data quality rules and tracking across the data supply chain should be integrated across the pipeline to allow for quality issue identification and remediation across the data lifecycle  
3. Data Governance: identify data owners and operationalize change management while establishing accountability and adoption protocols

Taken together, these core attributes ensure that trusted data platforms are able to evolve to become central, differentiating capabilities for biopharma firms, with strong data management, exploration and self-service capabilities. Once enabled across the organization, they can help identify patterns, trends, and opportunities that were previously unknown.  

To better understand this, we can look toward the structure of leading biopharma companies that are deploying global, enterprise-wide data strategies that leverage advanced data management, operating models, and technologies. Good data management and governance allow companies to acquire, grow, refine, safeguard and deploy data as a strategic asset, as illustrated below:

Specific aspects of Data Management and Governance

Pillar #2: Data Platform Architecture

There are several recommended capabilities to be integrated into data platform architecture, from ingestion pipelines to real-time and self-service processes. Machine Learning and Operations (MLOps), for example, provides the ability to scale, run and maintain thousands of models on a common AI platform. Investment in architecture ensures that very large data sets are not just stored securely but available for rapid computing capacity.

Most journey to cloud efforts focus on shifting workloads from legacy, on-premises applications to the cloud, simply to save on storage and computing costs. However, a truly data-driven organization needs to have data platforms with a mosaic of software to enable a shift from Infrastructure as a Service (IaaS) to Platform as a Service (PaaS). Many biopharma firms pursue single point solutions, but a strategic and coordinated approach to building a platform is a far more beneficial approach, with the choice of PaaS ranges from cloud native to cloud agnostic or custom. 

As an example, we can look to Takeda Pharamceutical Company, which has entered into a five-year strategic development plan with Accenture and AWS to further accelerate its digital transformation. This collaboration will increase Takeda’s ability to respond with greater speed, agility, and insights across the value chain. 

This will enable the company to deliver transformative therapies and better experiences to patients, physicians and payers faster than previously possible.¹

Pillar #3: Product-based Organization & Scaled Skills

A truly data-driven organization needs a framework to organize and execute processes as though they are a digitally native company. To do this, leading biopharma organizations should focus on building digital teams, with a combination of strong business engagement, data modeling, platform engineering, and product management skills. It’s a material shift from the status quo of mostly program management and some engineering skills. With over 50% of the talent – both in-house and across the partner network – needing to be retrained or given new skills like agile in Data, MLOps, or Spark on Cloud, investing in talent is critical.

Data-driven organizations must be designed with key enablers that provide the agility necessary to support growing business demands. To do this, operating models must account for this type of talent, cross-functional teams, and data at scale.

Example Operating Model for the Scaled, Strategic Use of Data

Pillar 4: Business Adoption

It wasn’t so long ago that technology constrained the pace at which firms could innovate and grow. Now, the opposite is true, with an organization’s own capacity for change directly related to their ability to deliver at speed. Adopting these digital platforms – internally, across the ecosystem, and with consumers – should be a key focus for any organization working to become truly data-driven. To be sure, an organization’s ability to adopt data-powered decision-making is foremost a cultural issue, with organizations often underestimating the challenge and scope of necessary cultural change. 

Putting in place a meaningful journey management framework with leadership interventions, hackathons, learning plans, and supporting metrics is vital to finding success, but the buy-in needs to be implemented from the top down at every level. By defining clear objectives and reasons for the move toward deeper integration of data strategies, Biopharma companies are more likely to secure buy-in. 

Biopharma's journey

There is so much data currently being produced in biopharma today that it has made classic scientific observation and collaboration models near unusable. Research from the University of Ottawa in 2009 noted that 2.5 million new scientific papers were published annually – and that was a decade ago.  The question is, how do we continue to manage all of this and share it for scientific insights? 

Most biopharma organizations are still on their journey to true data reinvention, with the vast majority recognizing that becoming data-driven is core to a successful present and future. In order to make the transformation to a truly data-driven organization, we can look to leaders in other industries who have successfully navigated this transformation.

 In my next post, I’ll explore lessons learned from leaders in other industries. 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. 

Source: 

1. Accenture Newsroom. Takeda Accelerates Digital Transformation with Accenture and AWS. October 13, 2020.

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