Digital Transformation: Part 2
Article written by Pete Crawford
Executing Digital Transformation
This is the second of two articles on digital transformation. The first piece illustrated several components – relating to employee experience, customer experience, operations and business models – that offer transformational opportunities. Here, we will describe a framework to orchestrate and execute 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 Execution Depends on Cultural Change
Just as the approach to digital transformation requires a strategic, cross-functional and customer-focused mindset – rather than a focus on technological outputs – the success of execution depends on embracing new orientations:
i) Transformational change is ongoing and cultural, not finite or punctuated like a project.[i]
ii) Delivery is a series of agile enterprise-wide micro-transformations, not one ‘big bang’.
iii) Achievement is the creation of a modern enterprise that is inclusive; genuinely committed to sustainability; and able to handle data at scale – not just a digital platform connected to a new business model.
The big question, of course, is why not just follow traditional change management principles and best practices as a digital transformation execution strategy? However, while there are many overlaps, traditional change methodologies typically follow a beginning-end pattern. The nature of digital transformation is also dynamic, interdependent and contingent on network interactions both inside and outside of the organisation. Not accommodating these factors has contributed to a 70% failure rate of transformation initiatives and sustained success in a only 16% of cases[ii]. What emerges is an approach to orchestrating successful transformation based on five main themes.
Digital Transformation Execution Framework
| Theme | What Needs to be Answered? | Components |
|---|---|---|
| 1. Strategic Foundation | Why is change necessary? What is the end goal? How are we going to get there? |
• Vision and communications • Investment and commitment • Targets and tracking |
| 2. Operating Model Choices | How are we going to organise and coordinate the transformation? | • Traditional PMO? • Transformational office? • Product-led transformation? |
| 3. Data and Technology Ecosystem Needs | Does the technology stack help transform the customer experience and does the data ecosystem transform the employee experience? | • Data strategy • Data infrastructure • Data literacy and privacy |
| 4. Workforce Needs | Do we have the tools and the talent at all levels of the organisation to fully leverage new opportunities? | • Digital tools • Data skills • Coaching, learning and hiring |
| 5. Ways of Working | Is the entire organisation aligned and equipped to accelerate and sustain transformational capabilities? | • Cross-functional • Customer networked • Experiment and urgency |
The Themes of Transformational Execution
1. Strategic Foundation
Not unlike any major initiative or program, orchestrating a digital transformation starts with a series of strategic choices. These choices either affect how ideas are embraced within an organisation – in which case they establish a new set of cultural norms – or they are resisted. Gaining acceptance means:
Having a clear business strategy. The transformation can only be validated if it structurally aligned and measured in accordance with the wider business strategy.
Link investment to clear, ambitious targets. Conveying external benchmarks, establishing exponential change targets and issuing timelines are crucial signals of strategic importance.
Communicating strategic context to reinforce the urgency, commitment and significance of the transformation targets. Employees and business partners need to be fully immersed and committed to a clear narrative which articulates why change is necessary and what the end state will look like. Inevitably, this produces tension between challenging targets, autonomous teams who are accountable for results and the development of workforce capabilities. Communications needs to acknowledge these tensions and resolve them by being:
Audience specific (investors; government regulators; customer/user education; talent/recruitment; internal employees).
Customer-centric with an emphasis on customer needs and jobs to be done.
Customised to employee roles to help them adapt their own jobs and beliefs and, in particular, addressing transformation as a threat by discussing training paths to upgrade expertise.
Augmented by access to authoritative documentation (reasoning; goals; expected timeline with constraints; resources and points of contact).
Setting cross-organisational metrics and markers of digital progress. The most relevant measures typically address aspects such as digital ROI; time to market of digital apps; and track whether key talent has been attracted, promoted and retained.
Presenting a simple outcome-oriented transformation roadmap. The role of the roadmap is to prioritise and promote three to five initiatives that can be scaled to change customer behaviour.
2. Operating Model Choices
In simple terms, an operating model is the conduit between strategy, technology stack, development environment, and the organisation of talent to achieve business outcomes. At the centre of the operating model the question of how best to organise and coordinate targets, performance metrics, leadership, and scope across workstreams. Four main approaches emerge to closing the gap between strategy and execution. These approaches are summarised below.
A dedicated transformation hub. This is a good option when transformation is enterprise wide and no single business function has the experience to coordinate the scope or speed of parallel workstreams. Advantages include:
Direct translation of corporate strategy into digital priorities.
Centralised strategic planning, cost control and procurement over innovative technologies.
Governance and communications ‘nerve centre’.
Leverages specialisation and expertise over multiple use cases.
Integrates with other centre of excellence models (i.e. AI, RPA).
Product-led transformation. This option can be considered if the organisation has already established an effective information product management function built around strong data and analytics capabilities. Advantages include:
Pre-existing cross-functional navigation.
Geared to identify and understand customers (internal and external).
Experiment and prototype mindset to assist with speed-to-market.
Can be cross-pollinated with domain experts to build a lean, learning culture.
Funding a team rather than a project aids sequencing of initiatives.
A standard project management office. This option is constrained by primarily working within the context of a business function or silo. This approach only warrants consideration if investment is significant but not strategic. Not recommended.
The digital innovation lab. This is an option taken when organisations seek to experiment, learn and place multiple bets without large up-front investments. It is also synonymous with internal entrepreneurial divisions. While this approach may be valuable for ‘incubating’ transformational strategies such as new business models it is not recommended as a way of orchestrating transformational delivery. These types of units often have weak connections to core IT or lines of business.
3. Data and Technology Ecosystem Needs
The adoption of cloud-native data management platforms aimed at consolidating transactional, interactive and social data are central to the promise of digital transformation. Data platforms, supporting analytics capabilities embedded at a business domain level, are a transformational source of customer intelligence and innovation. Data architecture and infrastructure requirements can be as modest as business intelligence systems or as ambitious as the convergence of analytical and operational ecosystems that feed machine learning frameworks. In either case, choosing the right data analytics capability is paramount. In this sense, execution relies on:
Understanding the context and demands on the data ecosystem.
This requires discovering:
How the organisation decides when to collect data or purchase external data?
What types of data are collected and what is the primary source for each type?
Which stakeholders are the nominal ‘owners’ of each data source?
How granular is each data source? How has it been used in the past? Are usage events tracked?
Is there a unifying element (i.e. customer_id) that joins different data sources for data modelling purposes?
What tools and processes are available to move data between systems and formats?
How are the data sources accessed by different groups of users?
What data access tools are available? How many people use each of these tools, and what are their positions?
How are users informed of new and changed data elements?
How are decisions made regarding data access restrictions? By whom? Based on what criteria? How is this tracked?
What analytic tools have been tried?
How have the results of this analysis been judged? What were the metrics and benchmarks?
Ensuring that data strategy and data privacy management is supported by infrastructure best practice.
| Weak Infrastructure | Strong Infrastructure | Comments |
|---|---|---|
| Siloed | Interoperable | Systems can be easily integrated |
| Proprietary | Open Source | Systems can be easily replaced and are not vendor dependent |
| Bespoke | Off-the-Shelf | No vendor lock-in or inflated pricing |
| Hosted In-house | Cloud | Reduced cost and secure, remote access |
| Hyper-specialised analytics units | Self-Service Insights | Analyst tools are becoming available to non-technical users |
| Irregular Data Formats | Standardised APIs | Data can be easily shared |
| Ad-hoc Security | Privacy-by-Design and Differential Privacy | Data platforms are protected from abuse of personal data with built-in governance mechanisms |
4. Workforce Needs
Much of the strategic foundations, operational planning and technology ecosystem of digital transformation will go to waste unless there is universal user readiness, tool adoption and a collective commitment to sharing relevant information. To this end, organisations must activate a culture of continuous learning in the areas of:
The identification and acquisition of data skills. To unlock the full value of data there needs to be a fundamental skills framework featuring:
Defining data.
Classifying data.
Improving data usability.
Understanding data visualisations.
Communicating evidence to decision makers.
Why data privacy matters and how privacy practices affect employees, customers and partners.
The availability and implementation of digital tools. Digital self-service coupled with self-sufficiency in using tools make the power of transformation innovations accessible. The adoption of particular tools depends on business context but can include:
Messaging and virtual design or content collaboration.
Real-time data workflow and tracking management.
Self-service data analytics.
Dashboards connected to centralised data platforms.
Learning management systems to create training courses.
Coaching and talent identification to encourage the growth of new behaviours. Data skills and digital tools alone are not going to achieve cultural change or the creation of a continuous learning culture. Empowered employees, collaboration and urgency depends on distributed leadership and effective responsibility existing at all levels of the organisation. This requires:
Leadership development programs to challenge old ways of working.
Prioritising coaching to help team members grow through structured one-on-one sessions.
Having leaders dedicate time on hiring goals based on identified specific skill gaps.
Treat learning as a deliberate practice by providing immediate task feedback to individuals as in the form of small lessons taught by the most talented colleagues. Repeatedly reinforce positive behaviour.
Set OKRs for cultural change and track them relentlessly. Measures include the percentage of workforce actively involved in cross-functional teams; recognition of people who collaborate; and cultural gap identification with the use of assessment instruments.
5. Ways of Working
Ways of working is the articulation of culture across an organisation. A sustainable environment for digital transformation execution is cross-functional; experimental (and, equally importantly, tolerates data-informed failure); and operates with a sense of urgency. A number of traits contribute to embedding this culture:
Leveraging internal knowledge networks.
Eliciting and sharing in-depth input from customers.
Sequencing transformation workstreams to focus on one business domain at a time (maximising ROI).
Utilising similar datasets, technology solutions and team members for multiple use cases to reduce expense.
Applying agility by investing in high-fidelity prototypes anchored in data to validate risk as early as possible.
In conclusion, we can consolidate many of the themes and components necessary to orchestrate a successful digital transformation using a simple roadmap example.
FOOTNOTES
[i] A notable 63% of respondents (from 690 organisations) ranked cultural challenges as the biggest impediment to transformational efforts, Harvard Business Review, Rethinking Digital Transformation, November 2019.
[ii] ‘Unlocking success in digital transformations’, McKinsey survey, 29 October 2018.
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Article by Pete Crawford
