Covid-19 has been the ultimate disruptor. More than that, it’s changed the way we think about how businesses and industries can be disrupted. That said, the fundamental meaning and business impact of disruption remain consistent.
Technological disruption is the convergence between new business models, new technologies, and new combinations of existing approaches to create a competitive advantage. This advantage enables a business to take market share away from other businesses.
A good example of technological disruption is the increased use of machine-driven automation of business processes and workflows that were previously undertaken, or at the very least, overseen, by humans. This enables greater operational efficiency and opens up opportunities to create new revenue streams.
Disruption is all about adaptation, and is often discussed through the lens of outmaneuvering an incumbent, because this makes a good ‘David and Goliath’ story. It’s important to understand, though, that many incumbents are successfully leveraging new technologies to adapt to new markets and evolve their offerings. They often have the resources to make larger investments into emerging tech than their start-up competitors.
Disruption and digital transformation are often used as interchangeable terms, but this isn’t quite right. Disruption has been occurring since people first started trading goods and services. You could also use that term to describe the evolution of strategic innovation since the dawn of time. It’s a more philosophical definition, perhaps, but still accurate.
Digital transformation, on the other hand, is the process of using technology to become, and remain, agile. Agility enables businesses to adapt to different markets and market conditions, so they can weather external disruption.
As such, while these concepts are certainly linked, they are separate from one another. Digital transformation becomes a part of the disruption story only when it is done well, because only then can it enable the business agility required to adapt quickly and cost-effectively to benefit from new opportunities.
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The same goes for innovation, which is another term bandied around far too liberally. Both the ability to deliver a disruptive capability and the ability to survive through external disruption are outcomes of innovation done well. Innovation is the result of an organization investing in the ongoing ability to adapt to change - whether that’s market shifts, improvements in technology, or even just the changing demands of consumers.
In my experience of leading innovation programs in enterprise-scale businesses, the primary challenge is that innovation is viewed as a process with an end goal - for example, ‘let’s host a hackathon to generate ideas for the next tech unicorn’. When approached like this, ‘innovation’ is doomed to fail, as it does not generate a culture of ongoing innovation that leaves room to test new ideas, succeed or fail fast, and move on. Without this culture, true innovation will always be just out of reach.
The faster and more cost-effective a business can make the process of innovation, the more likely it is to be able to exploit new opportunities as it tests them. This requires a foundation of well-described, structured data that can be easily repurposed for new use cases.
This is where the pandemic enters the story. As organizations have been forced to use data across more widely distributed workflows and adapt to new ways of delivering their products and services, trends that were already in play have been dramatically accelerated.
This has had two significant impacts. First, it has accelerated the success of businesses that have leveraged technology to reduce cost and improve the customer experience. Whether or not this is successful is often more to do with how the organization is set up to exploit technology than the choice of which new technology to leverage. For example, good information management practices will amplify the success of technology implementations, but even the most advanced technology solutions will not be able to make up for an absence of fit-for-purpose data.
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Information is the primary asset
For many organizations their primary asset is information. This takes the form of knowledge and expertise locked in their subject matter experts’ heads. Part of information management is about making this knowledge explicit and actionable. An organization’s information management strategy is the most important factor in making a business ready to adopt new technologies and maximize the potential value they derive from them - i.e. enable agility.
Best practice for beginning to deploy effective information management systems is to ensure your team’s input into domain modeling. This creates a shared understanding of the organization’s information and data landscape: what the business has and does not have, and what is adding value and what is not. Having a strong understanding of your data is the first step on the road to leveraging it for competitive advantage.
organizations are frequently tempted by shiny new technology and buzzwords, but unless the challenges and opportunities of your information landscape have been thoroughly unpacked as a group exercise, it’s impossible to say whether tech investments will have a good ROI.
This is why we’re seeing organizations with good information management practices rapidly reap the benefits of adopting new technologies. It’s also why those that haven’t ‘fixed their plumbing’, but are still making significant investments into technology, are often not unlocking the value they had expected from these investments.
Successful disruptive businesses will choose the new technologies they intend to adopt in a similarly strategic methodology. However, the technology they choose (and the way they use it) will differ depending on their core differentiators and what they are trying to achieve.
Disruptive businesses, including incumbents who are ready to survive and even thrive through industry disruption, are those that choose the right technologies for the right purposes.
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Matt Shearer, CPO, Data Language