Why it could be time for biopharma leaders to embrace data analytics

12 Nov 2019

Siobhan Fleming, business development manager at Siemens. Image: Siemens

Siobhan Fleming, business development manager at Siemens, discusses how and why biopharma companies can benefit from data analytics.

Is it time for biopharma to follow in the footsteps of sectors such as telecoms, advertising and insurance in embracing data analytics? We spoke to Siemens business development manager Siobhan Fleming to learn more.

‘The early movers in biopharma are likely to see the biggest gains’

Can you explain what your current job involves?

I joined Siemens Digital Industries in March this year to manage the business development activities for BioMAC. BioMAC is our advanced big data analytics solution for biopharma – it is supported by the IDA and has been developed in collaboration with the National Institute for Bioprocessing Research and Training (NIBRT).

Working together, we are providing the biopharma industry with a platform to simplify access to and understanding of bioprocess big data analytics. Better analysis and presentation of bioprocess data improves communication between engineering and scientists and makes data-driven decision-making available across the organisation.

My job is to bring the benefits of big data analytics to the attention of the biopharmaceutical industry in Ireland and around the world, and to collaborate with them to develop valuable use cases. Our goal is to improve access to life-saving medicines for patients all over the world.

I have enjoyed some very positive moments over the past six months. Having successfully brought the solution to industry partners in both process development and production-scale operations, the team is making great strides towards achieving our goals. These developments have been recognised by the industry too. Most recently, BioMAC was awarded the prestigious Project of the Year at the Pharma Industry Awards 2019.

Why should biopharma companies embrace data analytics, and how can it benefit them?

Having worked with pharmaceutical industry solutions for more than 18 years, I feel very energised about digital transformation and where the latest technologies can bring us. BioMAC is at the leading edge of pharma 4.0, addressing the challenge of disconnected data and empowering bioprocess engineers and scientists to communicate in a way that they haven’t been able to until now.

I believe the time is right for biopharma leaders to embrace data analytics. Industries such as telecoms, advertising and insurance already place big data analytics at the centre of their business strategies and the early movers in biopharma are likely to see the biggest gains.

There are many paybacks. I see the primary benefit as enhanced understanding of very complex biopharma processes, which in turn underlines several key advantages for manufacturers, including:

  • Improved quality and yield, and enhanced batch optimisation and process intensification
  • Agility to facilitate new manufacturing technologies and adapt to new product portfolios for improved tech transfer, scale-up and scale-out
  • Ability to forecast production requirements and manage the supply chain – analysing raw material and contract manufacturing organisation (CMO) data for better supplier management, optimising planning and scheduling
  • Regulatory efficiency – evidence of process understanding, known as continued process verification (CPV), is now a regulatory requirement and a significant overhead which can be addressed through connected data

The rate of change in terms of portfolio types, personalised medicines and generally smaller batches shows no sign of abating and is being addressed with manufacturing transformation. As an industry, we need to be ready to accept new therapies and manufacturing technologies. Advanced process understanding through data analytics has the potential to reinforce Ireland’s strong position in the biopharmaceutical industry.

Part of the value of data analytics is in its ability to be used and reused and combined with larger and additional data sets for increased value – we see this expanding to include real-world data and patient data sets to really improve patient outcomes.

Is there any particular reason as to why pharma companies may be averse to diving deeper into data analytics?

We have looked in depth at the reasons why biopharma companies might be averse to adopting a data analytics programme. The biggest obstacle is often cultural resistance.

There can be some fear associated with moving to any new approach. It is a change management project as much as a technology project. It doesn’t need to be a ‘big bang’ approach – we recommend starting with small projects to build expertise and trust and ramping up from there.

Are there any major challenges for such companies in adopting data analytics?

So, there are cultural challenges, as I’ve mentioned. But there are also challenges of management sponsorship, expertise, technology and, of course, the data itself. All of these are more complex in a biopharma context.

In a production-focused environment, gaining management support for the project can be a challenge. This is a new approach for the industry and can have unclear return on investment at the start and even intangible benefits, which are a tougher sell.  That said, for those who have taken this leap, the value and the benefits are definitely there.

We are in an era of skills shortages and head-count challenges, and a big data analytics team requires various multi-disciplinary subject matter experts – not just software engineers and data scientists, but also process engineers and manufacturing scientists who can correctly interpret results.

Biopharma captures significant amounts of data. All aspects of the plant have associated information systems, from ERP to process automation, electronic batch records, laboratory information systems and data historians. Unfortunately, this data lives in disconnected silos, inefficient for individual queries.

Add to this the 3Vs of bioprocess data – infinite volume, real-time velocity, and multiple varieties – and we see that data management quickly becomes a real challenge. This is especially true of traditional business information and data visualisation solutions, as they mainly provide offline processing capabilities and retrospective insights.

Do you have any advice for management in pharma companies hoping to start using data analytics more?

We recommend starting small with quick wins to gain support, and building from there. As I mentioned, it doesn’t need to be a ‘big bang’, and it can be an expensive mistake to focus just on the technology.

In my experience, it is better to focus on a specific problem or data set to achieve greater understanding and insights in that area which will inform your next steps.

Rather than trying to do it entirely in-house, we recommend collaborating with a partner that can bring the data and process knowledge and an existing technology architecture, do the heavy data lifting and provide fast, actionable insights.

Finally, find a way to run a project, regardless how small. It is okay to achieve small and quick wins. The results will compound, and if you’re doing it right, the outcome will be a significant improvement in your process knowledge and understanding and, ultimately, in the efficiency of your operation.