Corinium Access Advisory Board

Professional Bio

Stuart is a senior information and data professional with over ten years’ experience in leading data and analytic initiatives. He has wide ranging experience of leading multi-site, multi-national technical teams delivering Business Intelligence, Analytics and Big Data strategic programs. He has worked with several institutions including TSYS, Nationwide Building Society, RBS, ABN AMRO, Virgin Money, Deutsche Bank and Shell Energy.

In his current role with Holovis International Stuart is leading the implementation of a new Data & Analytics strategy, combining the latest developments in Analytics, Machine Learning and Data System design with Holovis’ world leading expertise in virtual, augmented and mixed reality and immersive experience solution delivery.

The company specialises in the creation of immersive, multisensory and interactive environments to engage audiences, take them on transformative journeys and deliver powerful messaging. It operates in three primary sectors; entertainment, pioneering methods of biometric tracking to achieve personalisation throughout leisure destinations, Enterprise, developing proprietary software ecosystems to further Industry 4.0 initiatives and aid virtual manufacturing and simulation, creating high end synthetic environments for training.

He is busy launching new products including a Data & Analytics solution called DeepSmarts and a computer vision based distributed sensor array and decision engine product called HoloTrac.

Last Corinium Access Event Attended: The Data Analytics Transformation Roundtable

What were your reasons for signing up to the Roundtable?

I have attended Corinium Events before and have always found the content provided and peer group present to be inspirational and informative, this smaller scale format also appealed to me as it enables better conversations and discussions over the usual set piece presentations or talks.

Main takeaways for you, from the evening?

That we all share the pain of driving Data & Analytics initiatives! There are common challenges whether they are regulatory, pace of technological change, adoption and reskilling or simply meeting the demand our clients and organisations are generating. It also highlighted for me the great potential for cross use-case and sectorial knowledge sharing, particular in terms of the emerging technologies Holovis are leveraging.

After having participated in one, what are your thoughts about the Roundtable Format?

I think the format is a refreshing change and enabled the discussion to evolve naturally, allowing more opinions and voices to heard, something that is vitally important to foster in our industry. The conversational nature lead to candid and insightful exchanges from a good range of industries and differing use-cases.

Would you encourage your peers to attend these types of events, and why?

Yes. It is a great opportunity to learn from your peers, gain insights into how challenges are being faced and overcome and then drive the cross pollination of concepts and ideas from across organisations and sectors. The specific format is valuable in that it allows a more rounded conversation to take place, some of the most interesting comments and talking points that were discussed perhaps would not have been raised in a more traditional forum that some may find stuffy or intimidating.

Thought Leader Contribution

What are the biggest challenges at the moment that you’re experiencing in your role?

• A key focus of my role is driving our innovation pipeline. Holovis is a high technology company and our continued growth and success is contingent on graduating new concepts through our pipeline and bringing them to market such as HoloTrac, DeepSmarts and MemorySpace.
• Product development that relies upon new or emerging technologies is, perhaps unsurprisingly, very difficult to resource quickly enough to meet our demanding schedules. Scaling our teams in this way is therefore challenging and poses a real opportunity risk and reduces our first move advantage.
• Anyone who has worked in Data & Analytics will understand the issues that can occur when trying to deploy a data science or analytics solution into production systems. The best practice and tooling available to avoid the inevitable disruption and delay have improved, certainly over the last five years, however producing production ready data science projects is still challenging, especially when you’re dealing with ML use-cases.

How are you currently working through those challenges?

• The key for us at Holovis is to view innovation as a culture rather than purely a process. We focus on having a strong, diverse pool of talent that are free to contribute constructively to find a solution to any challenge. We have a rigorous and systematic method for recognizing, and perhaps more importantly, recording use-cases and problem statements. Finally, we apply our own Holovis project management system and agile methodology coupled with a strong product delivery team with a clear ‘route to revenue’ strategy.
• We have adopted a hybrid approach, continuing to grow our U.K., U.S. and China based teams but augmenting them with dedicated Product Development offices, this allows us to resource our product development quickly and to a high standard while not disrupting our other projects and associated resource pools, which tend to have more geographical constraints and different candidate profiles.
• Holovis has an excellent pedigree in delivering complex, technically challenging projects so we had a formidably strong project management system we could adapt. When we began our product development as a distinct function, we ensured that we refocused on a high cadence, iterative agile process with acceptance criteria that included integration with our core architecture including our In-Mo framework, a ‘Rosetta Stone’ for visualisation devices and systems. We also demanded good Data Management and Security as a key deliverable, rather than trying to retrofit it into the systems after the fact which often happens in software development elsewhere.

What are the biggest barriers to working through those challenges?

• The pace of technological change is always a challenge, it requires more and more focus and resource to keep on top of developments, particularly in ML.
• Resourcing to scale will continue to be a barrier as the demand will remain high for the foreseeable future and new talent concentrations take time to evolve and are complex to predict.
• Innovation and ideation create change and change needs to be handled correctly and products need to evolve to stay relevant without causing issues or diminishing the core functionality. Achieving this balancing act, particular with Data & Analytics, can feel like swimming against the tide but is all part of the febrile, challenging and exciting era we’re fortunate enough to be a part of.