Late last week the Element team was excited to participate in our first ever Customer Advisory Board (CAB) meeting. As expected, it was a great opportunity to spend time with some of our leading customers understanding challenges and how they are facing them, and discussing key trends such as digital twins. Such direct interaction is invaluable for us as a source of insight and feedback and beneficial to the CAB participants as one mechanism for sharing ideas. While the specific contents of the discussion are private, there were some key themes that stood out to me that I would like to share.
In a sense this is more of an execution challenge for large industrial enterprises than a lack of motivation or a clear strategic perspective. Data professionals, especially data scientists and data engineers are difficult to attract and recruit. COVID, for its part, has provided impetus for remote access. There are resource contention and priority conflicts for the business operations SMEs (subject matter experts) - focus on running the business today versus help build a better, more data-centered (or data-empowered) way to run it tomorrow.
Low code / no code tools can help resource-constrained SME teams be more effective so they spend less time wrangling data and more time on valuable insights. Another approach that can help with the resource constraints / contention and help propel transformation initiatives faster is to adopt a central (COE-like) approach and focus organization-wide instead of being more narrowly (e.g. single plant) focused. To gain support from senior leadership, transformation teams appeal to strategic imperatives such as ESG or tight supply in the labor market. Seeding the COE teams with colleagues with field / operations experience and equipping the COE teams with suitable technology that allows them to support the broad effort are key enablers.
For all the marketing hype that is associated with digital twins with every vendor looking to stake a claim to this “new” approach, digital twins are already a reality. Most participants reported having digital twins already in production with more on the way. They are using digital twins to both address single use cases across multiple sites and multiple use cases at a few sites. It is one thing to build and deploy a limited digital twin but scaling digital twin efforts and keeping the twins effective on an on-going basis is critically dependent on a solid data underpinning that supports that scaling.
While companies are experimenting with deploying advanced analytics approaches such as AI and ML, most feel that simply making basic, reliable analytics more widely available is a key priority. To be clear, companies are at different stages of their data and analytics journeys but taking a holistic, data-centric approach sets up the teams for immediate and future success as they work to establish a robust data foundation for the business and avoid both the perpetuation of local spreadsheets and the creation of disparate, siloed data “mini-marts”. The data-centric approach allows them to collaborate using a shared basis and supports initiatives to democratize data access enabling greater data use / value extraction.
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