As outlined in our first post in this series, the amount of data being collected and stored from a multitude of sources has increased exponentially. In many cases, it is not stored with any standard identification or tagging, creating a poor semantic model for discovery when required. These obstacles get in the way of an organization’s journey and creates roadblocks in agility. 

To succeed, biopharmas will require a much deeper understanding of how to work with less structured longitudinal clinical data, often not reviewed by statisticians — and therefore need a deeper understanding of advanced analytics tools and algorithms as well as core statistical principles. This understanding of the technological dimension, including algorithms related to machine learning and artificial intelligence – as well as new technologies emerging such as CAR-T and CRISPR – is critical to working amongst the deluge of data.

One of the solutions to this is the adoption of F.A.I.R. data practices, which applies the principles of findability, accessibility, interoperability, and reusability to data throughout its life cycle. By adopting F.A.I.R data practices, the information that is collected is easily retrievable and can be spun into different models at increased speed.

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Using AI to understand unstructured data

If the data transformation journey makes you feel overwhelmed, don’t be. Only a handful of companies across all industries – less than 5% of the Global 1000 – have truly become data driven enterprises.  Although these companies span across multiple industries, each share seven similarities in their journey to get there. Becoming a truly data-driven organization takes time, self-awareness and strong leadership. By embracing these tenets, forward-thinking biopharmas will be better positioned to take on a high-value, enterprise-wide data transformation.

 The seven tenets of data transformation

  1. Set high aspirations: C-Suite leadership establishes and broadcasts bold aspirations about their timelines, goals and intentions. The management team clearly communicates bold and ambitious, yet realistic goals.
  2. Govern from the top: Senior leadership takes ownership of the direction and journey, even at times establishing a Data or Digital Transformation Office. This shows a commitment to the journey while amplifying its importance.
  3. Lead with business value: The data and digital strategy ties to and enables the organization’s overall business strategy. Rather than building a platform and expecting demand to follow, build Digital/AI/Data products based on business value.  
  4. Partner strategically: Leading companies are honest when it comes to insourcing and partnering. To facilitate implementation, create opportunities to attract the very best and brightest technology talent, which means adopting some non-traditional roles. Consolidate from the typical 15-30 partners to a critical few: PaaS, SaaS, and Data/AI/Digital, while remaining mindful of vendor lock-in and exit strategies. 
  5. Redefine the ways of working: Be willing to challenge the status quo and use innovation approaches in order to solidify change. Horizon #3 companies have established a strong culture, favoring progress over perfection, with continuous product releases, short sprints and fast learning.  

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    Standardizing the data deluge

    Accenture’s INTIENT platform is an open architecture platform that enables the continuity and flow of information across the enterprise, providing the right data, at the right time, to the right team through standardization practices for data.

    Data access and standardization are just the first steps. Accenture INTIENT enables the application of artificial intelligence and advanced analytics to that data—delivering insights that can lead to better patient outcomes, faster than ever before. This is a fundamental shift in unlocking the true value of data throughout the life sciences industry.

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  6. Be willing to adapt: Define a target state but be willing to adapt the process every quarter or so, since the transformation approach is likely to change every three to four months. This adaptability is the essence of the “experiment often and fail fast” culture and is a key part of the transformation.
  7. Use metrics continually: Successful companies don’t wait until a project or sprint is complete to examine progress. Defining a metrics framework to assess both quick wins and medium-term goals, strengthens the value case, wins adoption from the organization, and provides the arsenal for the necessary course corrections toward success.

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If the data transformation journey makes you feel overwhelmed, don’t be. Only a handful of companies across all industries – less than 5% of the Global 1000 – have truly become data driven enterprises.  

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Taking the first steps

On its surface, starting the journey to becoming a truly data-driven company can appear to be an arduous undertaking. Thankfully, there are pathways, approaches and tools that can help ease the burden. This series of articles provides a glimpse into the value case and critical success factors that can be applied to any organization, across all industries.  

By developing a strategy that incorporates the four pillars of data-driven transformation while focusing on the seven tenets of success, biopharma companies can better position themselves to not only tackle transformation, but successfully transition to truly data-driven enterprises. 

Each of the elements outlined here has thoroughly practiced frameworks, playbooks, and artefacts to steer organizations toward success. The key is to get this data-driven transformation going in four to six weeks, rather than investing three to six months on strategic assessments and more proof of concept projects. 

For a deeper dive into the opportunities being delivered through data reinvention, I recommend reading the Accenture report, AI: Built to Scale. If you’d like, you can contact me directly at

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Kingston Smith

Managing Director – Applied Intelligence

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