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Professional life is about priorities. And sometimes, choices are very tough and you have to say no. I’ve been invited to join Digital R&D hackathon in Paris organized by McKinsey and develop a prototype using OpenBOM technologies (disclaimer – I’m CEO and co-founder). But I had to say no, unfortunately, because of some other priorities. Nevertheless, I decided to share the information about the upcoming hackathon since I believe my readers can find it interesting. The topic of the hackathon was to develop real use case scenarios of digital R&D.

Here is the passage from the invitation that McKinsey allowed me to share with the readers of Beyond PLM:

Over the past decade, technology acceleration spurred exponential growth in Digital R&D solutions. Most digital R&D companies operate in a “sweet spot”, seeking to solve a specific pain point of the value chain. Our belief is that extracting the value of digital R&D solutions requires combining them to address larger business issues than what they have been designed for. Join us in Paris to connect with other technology providers, get coached on your value proposition, and build a distinctive perspective on digital R&D.

As part of my preparation for the hackathon, before I had to say no, I took a look into the proposal and value proposition of digital R&D. Here is an article I recommend you to take a look – Digital R&D, a $100 billion opportunity.

My favorite part of the hackathon was a focus on the data, analytics and the technologies that can enable a digital value chain – data flowing between companies and becoming freely available to all players of digital research and development as well as product development. My second favorite part was the ability to build analytics and machine learning on top of multi-disciplinary multi-tenant data set of data consolidated from multiple teams, development organizations, customers and also products (thanks for sensors and other IoT technologies). OpenBOM, which I’m CEO and co-founder is developing such technologies allowing us to keep track of data flowing between multiple companies of the digital value chain – development, engineers, researches, customers.

My special favorite is a vision of Digital R&D described in the next principles.

1- Analytics and data driving the R&D process
2- Connecting with individual customers
3- Digital transformation at scale.

Here is the passage I especially like about digital transformation.

Designing the digital transformation at scale. Most pharmaceutical companies are digital laggards compared with companies in other sectors such as media, retail, and telecommunications. Their digital-transformation efforts can stall for many of the same reasons these efforts are thwarted for others—for instance, a limited understanding of the specific ways that implementation of new technologies can create business value, a shortage of native digital talent, and insufficient focus on digital topics from senior leadership. Our experience with companies inside and outside the healthcare ecosystem suggests there are four core principles for succeeding with this kind of all-encompassing change program.

First, healthcare companies (and R&D organizations) need to identify and prioritize their critical sources of value; they need to identify the capabilities that lead to competitive differentiation and those that would benefit most from digitization. Second, they must build their service-delivery engines—not just in managing new digital technologies but integrating agile, data science, and experience design into the fabric of the organization. Third, healthcare companies should look for ways to modernize their IT foundations: for example, moving to digital platforms such as cloud and Software as a Service, managing data and knowledge as a strategic asset, and improving security protocols for the company’s crown jewels. Finally, companies must ensure that they build and maintain core management competencies including governance, financial processes, and organizational health—in other words, all the enablers that allow them to pursue a successful digital agenda.

What is my conclusion? Digital transformation, usage of data, analytics, moving to digital platforms such as cloud and SaaS, managing data and knowledge as a strategic asset- this is a foundation of platform transformation from legacy data control to data intelligence. Modern SaaS platforms have inherited tools and infrastructure capable to manage data and analytics at scale, combing and analyzing information from multiple sources and naturally becoming more intelligent as more people are using these systems. This is a typical characteristic of network multi-tenant platforms – the next step in the development of data management, engineering, product development, and PLM solutions. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing cloud-based bill of materials and inventory management tool for manufacturing companies, hardware startups, and supply chain. My opinion can be unintentionally biased.

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