Digital Transformation: Part 1
Article written by Pete Crawford
Transforming Key Strategic Capabilities
This is the first of two articles on digital transformation. Here, we will look at how the digital economy has reconfigured the business value chain and its effect on four key strategic capabilities – employees, customers, operations, and business models. Understanding the transformational components of these capabilities offers revenue maximisation and cost optimisation opportunities. In the second article we will examine execution and orchestration in order to take advantage of these opportunities.
“Insights from Pete Crawford | Head of Data, Analytics & AI, Pete Crawford spends his day-to-day leading strategy, governance and execution over enterprise data platforms, data science and AI capabilities. Speaking at leading industry AI and data events, Pete is experienced in forming and directing multi-disciplined teams to manage enterprise information assets and deliver business outcomes through advanced analytics.”
Digital Transformation is Strategic, Cross-Functional and Customer Focused
The term ‘transformation’ is not incidental. It is far more powerful than ‘change’. It implies creating something entirely new. The recent wave of innovative technologies – such as managed cloud services; data platforms, natural language understanding; computer vision; robotics; machine learning; and blockchain – have been accelerated by forced experiments owing to the COVID pandemic. However, technology adoption is the wrong way to frame opportunities. Instead, digital transformation can be distinguished from earlier eras of business transition in that:
i) Strategy, not technology, is the driver.
ii) Cross-functional business processes, not only IT infrastructure, are the enablers.
iii) Competing for attention to create direct customer relationships, not competing to create exclusive supplier relations, is the focus.
Data is the Strategic Asset Shaping the Scope of Transformation
A second factor distinguishing digital transformation is the emergence of the ‘digital economy’ and the notion of data as a strategic asset. To be clear, data is not analogous to models of ownership or the utilisation of physical commodities[1]. Succeeding with any digital transformation program will ultimately demand managing, governing, using and sharing data to create value. It helps to start from an understanding of the unique characteristics of information:
Data is non-depletable.
Data is non-rivalrous (millions can use it simultaneously and a single piece of data can be used by multiple algorithms, analytics or applications at once).
Data can become less relevant, and less valuable, over time.
Value can multiply when aggregated and analysed with other relevant data.
The price of data is often indeterminate of supply and demand.
Data is not only personal to us but is also created through interactions with other people or services; therefore, data has both a private and a social value (i.e. COVID tracking apps).
Data can influence our own behaviour through feedback mechanisms (i.e. wearable tech and fitness trackers).
The notion of data as an asset, and an adjunct to innovative technologies, is illuminated by the real-time monitoring of environmental variables derived from Internet of Things (IoT) applications. For example, the insurance and energy sectors have embraced embedded sensors. Innovative automotive insurers have been able to improve forecasting and claim reviews by monitoring driving habits accumulated over millions of kilometres of driving data. The collection of attributes, such as speed, acceleration, braking and turning motions, has also ensured that they are able to base premiums on personalised behaviours rather than general inferences. Ironically, however, sensors are expensive to fix when cars do crash – contributing to raising insurance rates.
The Business Value Chain Has Been Reconfigured
The confluence of digitalisation and technological innovation has not only disrupted consumer habits but has reconfigured the business value chain. For many organisations, the point of integration in the value chain on which their sustainable differentiation is built has changed. Consequently, digital transformation has become inescapable when reckoning with a new set of competitive forces that affect how goods or services are supplied, distributed and consumed.
Reconfiguration of supply
Suppliers and content creators, especially those with differentiated product, niche focus and high quality, can attempt to attract consumers directly to avoid the risk of becoming commodified or abandoned. For example, musicians are using StageIt to distribute their performance to reach widely distributed audiences. Or The New York Times, which remains profitable (with a wider reach) by adopting a paywall and subscription model to counter decimated advertising revenues in traditional media.
Reconfiguration of distribution
Distributors of digital goods are no longer constrained by geography; by transaction costs; or by the need to seek exclusive integration with suppliers. Businesses that aggregate demand (i.e. Google, Facebook, Spotify, Trip Advisor) act as intermediaries that control the relationship between third-party suppliers/content creators by integrating forward in the value chain. They aim to attract end users through network effects so that the value of the service increases as the number of users increases. This often leaves suppliers and creators dependent on algorithm-led discovery such as search and recommendation systems in order to reach end users.
Reconfiguration of consumption
What determines the creation of value has shifted away from controlling the supply of a good, or the distribution of scarce resources, to controlling demand for abundant resources – users. Companies such as Apple and Disney have been successful by a strategy placing user experience and creativity at the centre of a differentiated and fully integrated value chain.
The Transformational Components of Strategic Capabilities
In response to reconfigurations in industry value chains, the transformation of key strategic business capabilities depends on developing or re-evaluating a series of components. This section will focus on these components with specific use cases.
1. Transformation of employee experience
Start with employees and cultural norms. Employees can be either the greatest impediment to change or leading advocates. Organisations which focus on the employee experience can establish a culture conducive to successful digital transformation. Employee experience encompasses daily activities in the workplace, a sense of purpose and value and, crucially, aligning expectations with the organisation’s goals and vision. Key to this vision is an investment in principles, processes and training which entrench:
Self-sufficient access to domain-specific information. Particularly around real-time customer intelligence and the reduction of time-intensive insight discovery. More specifically, the advent of self-service analytics still leaves gaps with introducing bias with data selection or problems with consistent insight interpretation. What is really required is self-sufficiency with access, management and maintenance of information systems.
A common knowledge base. Tools which consolidate and share knowledge help break organisational silos and enable groups to communicate and collaborate in real time. An example is Xero’s service design initiative to document, communicate and visualise customer and staff journeys across time zones and remote workplaces.
Distributed responsibility. The ability to rapidly restructure operating models to better coordinate cross-functional teams, external partnerships or co-designed customer solutions. This is best exemplified by the GoodSAM app. Here, emergency calls for cardiac arrest simultaneously alert Ambulance Victoria as well as qualified first aiders in the immediate vicinity who are directed to the incident. Widening the scope of responsibility has saved lives.
A continuous learning culture. Learning within an organisation needs to be viewed as a deliberate, formal practice. This practice can entail customised and highly targeted online courses alongside having highly proficient employees teach key skills to colleagues in small groups. Canva, which aims to democratise design, is one company to take this approach by establishing cultural norms which foster feedback and radical candor.
2. Transformation of customer experience
A focus on building meaningful customer relationships is not new. However, the foundation of digital transformation is to gain a clearer understanding of what customers experience.
The application of digital tools, cross-disciplinary design methods and engagement strategies has accentuated customer experience with:
Feedback loops. Feedback between content creation and content consumption drive a great sense of intimacy between users and creators. This is evidenced by innovation in the media and content creation space with the emergence of models (many of which are direct payment) like Clubhouse, Onlyfans, Substack and Twitter’s recent announcement of Super Follow.
Customer intelligence. A greater awareness of individual preferences or behaviours by integrating customer data across multitudes sources and silos into a data platform to provide a ‘360-degree view of the customer’. For instance, the food retail chain Chipotle created a unified view of over 2,400 restaurant operations to increase customer loyalty by 30%.
User participation and co-creation. Human-centred design tools can be used to enable customers to participate in an organisation’s value chain. This spans collaborative content co-creation (i.e. platforms which source early-stage concepts from consumers to create prototypes such as the clothing company Betabrand); to near real-time insights about new products or services (i.e. Remesh engages with customers via live video diaries and then uses AI to organise responses); or direct advocacy where consumers become the brand media.
Transparent data and AI ethics. Personal privacy and information transparency can become a business feature through an ethical consideration of data collection and algorithmic decision making. In terms of transparency (or simply getting in front of AI regulation), companies need to consider launching AI registers that explain how they use algorithms as part of their product services. The City of Amsterdam’s automated parking control register is an excellent reference point with concise details about the information used by the system, the operating logic, and its governance.
Of course, when it comes to understanding the customer experience, don’t become too data and algorithm dependent – get out and talk to real people.
3. Transformation of operations
Advances in robotics, sensors, IoT and AI are now offering to transform operations outside of supply chains or back office processes. The key components to turning efficiency gains into profit drivers and cost optimisation are:
Linking and combining cross-functional data. Is the first step to transforming supply chain management through the integration of data streams from internal sources with external supply networks in a data hub. The power of data is compounded when new data, such as streamed operational data from sensor devices, is attached to data which has already been modelled – typically from finance or sales – to better understand, for instance, the real-time cost of downtime for a given manufacturing process.
Demand forecasting with machine learning. Estimating demand serves as the starting point for warehousing, shipping, price forecasting, supply planning and the anticipated needs of customers. Machine learning improves on traditional forecasting methods where there are volatile demand patterns, rapidly changing environments or new product launches. Adding complex variables to financial or sales reports such as social media signals; click streams; geo-location devices; IoT; natural language transcriptions etc is an additional benefit.
Decision intelligence and modelling alternative scenario simulations. The ability to model ‘what if’ scenarios can be addressed with ‘digital twins’ – digital replicas that help test, model and predict the impact of various choices on our future. Singapore has embraced digital twins for urban planning and identifying the impact of environmental change.
Providing secure and governed access to a shared information ecosystem. Blockchain technology offers a new architecture of trust based on decentralised control; a shared view of the truth; and the direct exchange of value through tokens. The FMCG industry, specifically major grocery distributors, have trialled blockchain to track food throughout the supply chain, gathering real-time data to spot inefficiencies and create trustworthy audit trails. Unilever are testing blockchain for media buying and the reconciliation of data among advertisers, agencies and publishers.
4. Transformation of business models
Business models are essentially stories that explain how organisations work and provide insight into how to deliver value to customers at a particular cost. Digital transformation clears the stage for new stories and their relationship with strategy. It also encourages companies to experiment, learn, and place multiple bets on new models by setting up internal innovation (intrapreneurial) units. However, to be effective, these units must have influence and input with product development and sales functions.
Business model transformation greatly depends on the initial success of transforming the capabilities previously discussed. It helps to sense and respond to market, competitive and regulatory disruptions. And no new business model or technology innovation will ever transform an industry unless it can be connected to emerging or scalable market needs. Prevailing models in the digital economy include:
Subscription services. Subscription models such as Netflix or the New York Times, as well as on-demand loaning of goods or services (SaaS providers such as AWS), can succeed through capturing significant consumer attention or being recognised for high-quality niche focus. The fact remains, of course, that content creation with differentiated value is hard.
Digital platforms. Platforms such as Coursera and Shopify facilitate a relationship between third-party suppliers/content creators and end user. These platforms succeed by commoditising trust and increasing the economic value of everybody that uses the platform. As discussed earlier, demand aggregators fit into this pattern, but use network effects to capture the total economic value – hence the stoush between Australian media publishers and Facebook.
Integrators. Businesses which integrate across the whole business value chain provide sustainable competitive benefits including differentiation based on design (in the case of Apple, their operating system); an easier adoption path for new products (annual generations of iPhones); and profit maximisation owing to the ability to apply premium pricing for a superior user experience.
Data products. This entails the aggregation, augmentation and transformation of diverse data sets into information-based services. This approach typically takes two forms. Data as a service which offers direct revenue potential such as credit card transaction data used for customer behaviour and retail spend analysis. Or companies such as CoreLogic which provide subscription-based products that access rich property data. And secondly, data-enhanced products which maximise revenue by improving price or sales quantity such as cycling apps that measure movement in real-time and positioning in 3D space so as to simulate and gamify racing.
[1] Such as the trite and lazy analogy that ‘data is the new oil’.
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Article by Pete Crawford
