The answer is scale…what’s the question?
October 13, 2021
Any program that sets out to address a key question or business challenge will ultimately succeed or fail according to one key factor: whether it can achieve scale. For digital transformation, the challenge of scale goes to the very heart of success. Organizations must learn to overcome the challenges that come with moving from experimental proof of concepts to full-scale transformation, something I call the ‘transformation chasm’.
By George Long
Many highly promising proofs of concept have failed to gain traction and deliver value over time. Why? Because of their inability to scale. that many industries are making progress on their own digital transformation journey’s, but none have achieved comprehensive digital operations across the board:
How can organizations overcome that barrier and breakthrough to achieve fully digital operations and deliver sustained value at scale? They must cross what I call the “transformation chasm.” This is comparable to the big leap that start-ups need to make when they try to progress from a small team of, say, 15, to a business of 50 or more people. It’s relatively easy to manage a team where everyone knows one another. Moving to the next level requires processes and structures, and for many, it's a step too far. To achieve value at scale, large organizations need to make a similar leap forward.
Most digital transformation programs have set themselves a goal or north star, in most cases, this can be translated as sustained delivery of value at scale. In this case, we can now understand the challenge through those two main dynamics—Value & Scale. A simple matrix using these axes can then determine the maturity of any digital transformation.
Any initiative can be plotted onto this matrix, and depending on where it sits, it’s possible to guide the next steps to advance that project or program.
For instance, a project that sits in the top left-hand corner has generated great value but hasn’t been built to scale easily. That might include things like siloed proof of concepts or minimal viable/value products. These are often easy to stand up, run in a very unconstrained and agile way, and look great initially until they try to move into scaled production when their limits quickly become apparent.
On the other hand, projects that sit at the bottom right-hand corner of the value/scale matrix are often built on legacy technology that runs processes today. They’ve already achieved scale but won't increase value because the people and technology behind them lack the necessary flexibility.
These are extreme cases, of course. But they represent the fundamental flaws that stand in the way of achieving sustained value at scale. These flaws collectively create the transformation chasm that is the barrier or barriers to achieving a wider success.
There are five key themes that all organizations seeking to overcome the transformation chasm need to make sure that they address effectively:
For transformation to be successful, it needs to be fully endorsed by leadership. Executive sponsors must convincingly, and regularly, communicate the purpose of the transformation to the workforce. This can't be limited to a one-off announcement but should be ongoing reinforcement that speaks to the workforce and presents a convincing case about why the transformation matters and the positive impact it will make.
In terms of investment, key programs of work also require central funding (at least initially) to ensure they are able to develop into meaningful projects without running up against internal barriers, budgets and/or politics. Projects that scale then become self-funding as value generated is used to reinvest into further scaling..
For businesses to realize the potential of their digital programs the way humans interact, and programs are managed need to fundamentally shift. This operating model change can be complex but can also start by making a few simple changes. For example, Product Owners need to be responsible and accountable for their initiatives from inception to delivery at scale. A common thread throughout the lifecycle of projects must be maintained to ensure success. Champions are also essential, and these come in many forms:
Speed is vital to ensure that programs are relevant for operations. Clearly identifying areas where value is being lost creates measurable quantities that can guide programs on where to focus their efforts. Once identified, these projects can be prioritized on a loss solving (i.e., value-generating) versus ease to implement matrix. High value, easy to implement projects (considering people, process and technology) should be prioritized and combined to integrate processes.
Once an organization has a way of measuring and accounting for loss, solves these challenges through the integration of processes, automation becomes the next logical step before optimization and finally, cognitive capabilities take over. This ‘layer cake’ framework (shown below) can be used by Product Owners to simply communicate where to focus efforts at the start of a project and how to build maturity.
The operational technology landscape is highly complex, with hundreds or thousands of tags, sensors and indicators. Some of these are understood by experts that work in a particular process or on an asset, but others have been part of deployment over many years or decades. Knowledge is often siloed and hard to extract when looking at the end-to-end supply chain or across many locations. Understanding the data that these assets generate is essential as data is the lifeblood of any transformation.
A clear data strategy is therefore a must-have. This must address the identification of patterns and put data champions in place. Data can flow through the business providing insights across multiple dimensions.
Data should be governed in a platform. Platform strategies can vary across companies, but platforms that cannot evolve may become a barrier to progress. The cloud is likely to be the optimal place for the tools required to contextualize and harmonize data but advances in edge computing and the ability to mirror data constructs between cloud and edge are becoming more common.
The advance in Digital Twin technology to create a harmonized and contextualized data set from operational and enterprise data is fast becoming a key enabler to driving digital transformation at scale.
Analytics that sit in a platform are essential to convert data into insights. But the use of analytics must be considered in the human context, with transparency about analytics' role in automated decision-making vital to bring all users on board.
Finding a consolidated way for users to consume data and feeding their experiences, insights, and decisions back into the platform is another key consideration. Standardized solutions across processes help forge a more connected and aware workforce that is easier to govern and scale.
Finally, any business case and solution plan for a proof of concept should be reviewed against all these technical aspects mentioned above. If the technology cannot scale, the investment should be written off as a test of what is possible, or the project should be stopped until it can align to a scale play.
The challenge of fostering a culture of continuous evolution should not be underestimated. People are naturally averse to change and plotting the journey to a more autonomous future that involves people at its core is hard but arguably one of the most important steps to take. This is one of the biggest barriers to the velocity of any transformation. Below is a stepping-stone approach that considers people's role and engagement in transformation. This is not a linear journey but applied to many projects in a digital transformation program it can help bring the workforce on the journey together.
Step 1: In order for people to start to build trust in data then surfacing that information through visualization and alerting and embedding these into day-to-day operations sets a solid foundation.
Step 2: Data and analytics are only part of the story, add human experience, creativity and ingenuity into the mix and you now have a powerful recipe to generate real insights. Humans are considered in the decision-making loop.
Step 3: The first step towards automation is where processes are semi-automated and systems can recommend ‘next best actions’ for humans to carry out.
Step 4: Intelligent systems self-assess state, run simulations and identify optimal parameters for humans to approve at a click of a button. Here the human moves from being in the decision-making loop to ‘on the loop’.
Step 5: Finally, the data, analytics and systems are in control where adjustments are made automatically, and processes optimized. Humans are still involved in evolving the models and processes, but they are now out of the decision-making loop.
Accenture Industry X is dedicated to helping companies use the power of data and digital to redefine the products they make and how they make them. Industry X brings the best of Accenture's people together with industry experts who have deep and detailed knowledge of our clients' operational contexts. These teams can support companies 'cross the chasm', ensuring value is added at scale, at every stage.
Accenture has teamed up with Microsoft to bring an innovative ‘Intelligent Twin’ program to market. The Intelligent Twin program encapsulates the five themes noted above. It combines a clear vision and benefits approach, a user-centric operating model, and a loss-focused mindset to identifying and solving use cases. State-of-the-art, scalable Digital Twin technology goes across the length of the value chain and from sensor to boardroom, engaging the end-user from day one to support cultural change.