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Hugh Ujhazy, Vice President, Telecommunications & IOT APEJ, IDC
In an era dominated by digitally transformed enterprises, business models must adapt or die. At the business unit level, line-of-business managers are being pushed to move faster, ask for more information about their customers, interact more with their supply chain, and listen and respond to their customers. This adaptation sees IoT play a significant role in helping businesses become digital companies—in fact, an IoT strategy directly impacts business models.
As a company evolves from pure-play product provider to digital company, the role of passive data collection and enhanced decision-making changes business dynamics and creates altered engagement opportunities.
Currently, businesses that make and sell products to their customers stay in business because they invest heavily in their value chain. They try to augment benefits and create profit at every point in their value chain. Their value model is wrapped up in the value chain. However, there is an increasing shift in how lines of businesses engage with their customers. Customers need to be at the center of their business strategy rather than at the end of the value chain. The outcome of this is that value chains lose their relevance over time.
To respond to this, businesses have been steadily transforming toward product and services-based portfolios. Business value models based on business agility as competitive advantage center around being able to spin up (or down) services rapidly in response to customer demands. As more and more data is made available to both the customer and the lines of business, the value model becomes one based on the network value of a very broad ecosystem. This complex business model is very much driven by open, collaborative, and integrated business partners and rules. This business model (and its dependence of a data rich ecosystem) changes the way IT engages with its suppliers.
Today, the competitive advantage also comes from platforms that connect core products and services with the broad suppliers and customer ecosystems. Integration and re-use become the common keywords underpinning any competitive advantage.
With platforms, we are shifting away from product-based strategies into ecosystem-based strategies. On top of product performance and price, competitive advantage becomes based on how well products and services synergize to offer better value. Successful ecosystems encourage participation through shared value creation and capture. Data exchange creates positive feedback loops. In such ecosystems, strategic partnerships allow cooperating organizations to mutually benefit through shared revenue and/or value models. This platform-based approach creates a set of dynamic co-operative relationships based on capitalising on the opportunity presented by dynamic data, derived largely from IOT endpoints.
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Businesses making commitments to both IoT solutions and a digital transformation strategy find themselves drawn to a data rich platform approach. There players are looking to consume vast quantities of information about their products, customers, suppliers, and competition in real time or near real time. Business outcomes are created from a rich source of internal, external, and IoT-device-based measurements. In this model, the engagement between IT and the lines of businesses has the following characteristics:
• Communication networks play a critical role as sensors need to be connected in a variety of locations where connectivity isn't always available, nor is it always inexpensive.
• A robust cloud infrastructure is a core requirement. The cloud strategy will require the ability to process both private and public data onto a single platform. Data management will become another core requirement, given the variety of data sources that IT will have to ingest and present to an analytics engine.
• Complex analytics and cognitive/machine learning processes are required to seek outcomes that will drive business change.
• Back-end office (or ERP-like) systems must be connected to the analytics platforms to enable a feedback system for all the interested parties (customers, machines, cross-company departments, product suppliers, supply chains, logistics, etc.).
• Management and systems integration partners will be needed to help drive seamless cooperation of business outcomes across the company's complex ecosystem.
These changes see enterprises running IT as a service. This requires a very different asset-funding model (both internally and with IT suppliers) as IT becomes an opex-driven model. Openly sharing business outcomes with any business unit becomes a core value of IT while at the same time maintaining a highly securable data and information environment.
IoT deployments will create insights into businesses that they had never seen nor considered. Technology buyers will quickly realize that business units will be looking for opportunities in adjacent lines of business that can only be realized via a horizontal "open data platform" that allows discrete and varied data sources to be easily ingested and analyzed. As the volume of data derived from IOT deployments increases, the need for a centralized repository to manage that data will increasingly trend toward platforms.
As an organization places higher emphasis on innovating its business model, it must develop its digital platform concurrently to support its evolution. Considerations in developing a platform will include the following:
1. Envision a future state of the organization's business model. Shift into platform thinking by looking at how primary data, core products, and services can create new value through partnerships to form complements. Ecosystem-based competitive strategies mandate a win-win mindset in attracting and acquiring users and strategic partners. Their participation is key to generating network effects.
2. Develop a technology architecture and road map. Use the DX platform to envision a future technology architecture that supports the organization's future business model. Develop road maps in time horizons, based on current and future technological plausibility to implement. For each horizon, assign weight to the importance of each digital platform criteria to determine what technologies are required.
3. Experiment and adapt. Business models evolve based on data-driven insights and experiments. Develop the culture and the underlying tools to support experimentation.