Avoiding the digital transformation technology trap
Analytics and the Industrial IoT (Internet of Things) are cornerstone competencies for digital transformation, and industrial innovators are harnessing them to accelerate their competitive differentiation. However, for many industrial companies, scalability challenges with these competencies are still limiting broader adoption, particularly by operational users. Most of the issues are organizational and cultural, though they are often viewed as technological challenges, put forward as limitations around data management, lack of skill sets and work cohesion, unexpected cost and desire to avoid software-driven organizational upheaval.
Given that industrial digital transformation is so necessary to remain viable and competitive, why are mistakes, false starts, and dead-end investments with analytics and Industrial IoT all too common? How is it that innovation leaders avoid these challenges? This post will outline where and why many companies go off track when thinking about digital transformation strategies, with a focus on analytics and Industrial IoT. Additionally, it will highlight the six key characteristics innovators embody that allow them to sidestep these challenges. Finally, the post will present a starting point for how to begin to effectively implement digital transformation using analytics and Industrial IoT.
Although monumental resources and investment have been applied to digital transformation, the effort has not equaled success. Data is still hard to access, organize and use. Leaders struggle to understand how to connect strategy to execution. Workforce and organizational culture barriers remain. Return on investment is difficult to demonstrate, leading to an endless cycle of vendor testing. Where investments are made, use cases often don’t scale as anticipated. These issues are compounded by the realization that what works for one use case often doesn’t translate to others that seem similar on the surface.
As demonstrated in Figure 1 above, digital transformation presents many possibilities and starting points – plant floor, supply chain, engineering, smart services and products, etc. The opportunities often require a range of disruption to traditional ways of operating, from the potential for massive revamping of business and work processes to completely new models for customer engagement.
Making sense of these myriad opportunities requires some means to filter decision making on which path(s) to take, why, and in what order. As organizations contemplate the breadth of these opportunities, a natural line of thinking arises: a technology or set of technologies (e.g., platform) can be identified and purchased to drive all or most aspects of change.
As a result, conversations become technology-centric. Digital transformation turns into the pursuit of the silver-bullet solution or a proof-of-concept of the latest-and-greatest technology. Striving to get a potentially high-risk decision right, organizations look to compare solution techniques, tools and technology architectures in “apples-to-apples” ways, even when that’s not possible.
Companies that get stuck in a technology-centric view of digital transformation simply don’t succeed. Without better ideas on how to develop strong digital competencies, what ARC refers to as “digital wisdom,” they often experience collapse of their pilots and projects, leading to the internal perception of wasted investment. With analytics in particular, companies are now experiencing scalability challenges that are limiting widespread use of data as a business asset.
Leaders in digital transformation are committed to succeeding, despite the learning curve. As a result, they have been at it for a few years now, understanding that they need to learn and accrue digital wisdom. That wisdom has translated into a digital backbone — of a culture comfortable with digital-first thinking — that their competitors simply don’t have.
