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.

LOOKING TO Leverage and utilise your data? REACH OUT.

Whiteark is not your average consulting firm, we have first-hand experience in delivering transformation programs for private equity and other organisations with a focus on people just as much as financial outcomes.

We understand that execution is the hardest part, and so we roll our sleeves up and work with you to ensure we can deliver the required outcomes for the business. Our co-founders have a combined experience of over 50 years’ working as Executives in organisations delivering outcomes for shareholders. Reach out for a no obligation conversation on how we can help you.

Article by Pete Crawford

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’.

LOOKING TO Leverage and utilise your data? REACH OUT.

Whiteark is not your average consulting firm, we have first-hand experience in delivering transformation programs for private equity and other organisations with a focus on people just as much as financial outcomes.

We understand that execution is the hardest part, and so we roll our sleeves up and work with you to ensure we can deliver the required outcomes for the business. Our co-founders have a combined experience of over 50 years’ working as Executives in organisations delivering outcomes for shareholders. Reach out for a no obligation conversation on how we can help you.

Article by Pete Crawford

Digital Transformation Playbook

Technology is changing at a rapid pace and while technology is changing, companies will continue to be forced to change. New technologies can disrupt established businesses, but more importantly they stimulate opportunities for innovation. In today’s environment, business owners are more concerned about missing opportunities to grow, than become obsolete.

Technology prompts companies to rethink how they do business.

Technologies including big data, the cloud, the Internet of Things, and Artificial Intelligence are helping entrepreneurs to develop new business models and disrupt the established way of running operations.

Digital technologies are:

• Enabling businesses to operate in new ways to deliver more value to customers and generate more productivity and cost efficiencies

• Altering competitive landscapes

• Changing the economics of markets

“CONTENTS

> Technology
> What is digital transformation?
> Guiding your digital transformation strategy
> A digital transformation approach
> Tips for successful digital transformation
> Benefits of digital transformation”

Guiding your digital transformation strategy

Digital Strategy

A clear strategy determines your organisation's ability to reimagine and transform your business for the digital world. A multi-year digital strategy focused on driving customer experience, operational efficiency, and new revenue.

The digital strategy is the foundation for operating the business and delivering on business targets. New revenue streams, customer experience, and operational efficiency will all be viewed from a digital lens.

Technology modernisation is critical to your ability to meet changing market demands.

12 benefits of digital transformation

Need support with your digital transformation? Reach out to the Whiteark team.

We’re a team of doers led by Jo Hands and James Ciuffetelli. We don’t believe in unnecessary layers; and between us we have over 50 years of collective experience, expertise and global connections. Delicately weaving these together, we engage with you directly, with a single-minded focus on the task at hand. Collaborating at a senior level to propel organisations forward, we intricately map out and execute your next move, ensuring you’re prepared, protected and prosperous. Contact us to book in an obligation free conversation today.

Digital Transformation

Digital technologies have recast business models and business value chains for over two decades, in almost every facet of work. The financial services, retail, media, entertainment and travel sectors have all been upended.

The burgeoning internet of things, along with technologies like edge commuting, materials science and even 3-D printing means that the industrial sector is likely to see the same upheavals as the commercial and services sectors have currently endured.

Those disruptions will prove uncomfortable and even destructive for some, yet the long-term benefits of digitisation are now much better understood.

“Article written by Andrew Birmingham, Editor-in-chief and Associate Publisher at Which-50.com”

In a report from Telstra called “Embracing the Digital Economy” the authors write, “Increased digitisation in Australia could add up to $90 billion to the Australian economy corresponding to 250,000 new jobs by 2025. The digital economy benefits are an Australia-wide opportunity that can have profound impacts for communities.” (https://www.telstra.com.au/business-enterprise/news-research/research/embracing-the-digital-economy)

Much more, faster

The first 25 years of disruption seem almost evolutionary compared to the huge acceleration of digital transformation since the global COVID pandemic upended the economy.

Bond Capital’s Mary Meeker — author of the famous annual “State of the Internet” report — predicted in April last year that the businesses which will weather the disruption best will be those that embrace the core tenets of digital business. They will rely on Cloud services, sell always in-demand products or products that make businesses more digitally efficient, they will be easily discoverable online and can serve customers with limited contact.  

Later in 2020 McKinsey & Company studied the impact of COVID-19 on its corporate clients and reported that most had, on average, experienced seven years of transformation in just six months. Another way to think of that is that if you didn’t reform your business during those months you are now seven years behind your competitors!

The effect was even more pronounced in the Asia Pacific region, which saw an average of ten years worth of transformational work in just six months. 

And while much of the focus was on the impact of the shift to work-from-home arrangements, the biggest impact was actually found elsewhere. The management consultants reported the largest leap in digitisation was in the share of offerings that are digital in nature — now at 55 per cent on average globally, up from only only 35 per cent before the pandemic began.

But this is not simply an issue for global enterprises. One of the extraordinary effects of the pandemic shutdown is that every community everywhere in the world was impacted at about the same time, creating a rare economic alignment requiring adjustments to businesses large and small.

According to McKinsey, the reason why every business no matter its scale or maturity has had to react is because the biggest inhibitor to digital transformation — the inertia of business as usual — was swept away. 

BAU was simply no longer a viable option. 

Take ecommerce, for instance. About ten per cent of Australia’s retail trade happened online pre-COVID, according to National Australia Bank. That ballooned in March 2020, simply because there was no alternative. 

And it immediately created new problems.

Australia’s logistics sector was calibrated around single-digit or low double-digit online sales. It simply was not equipped to cope with an overnight shift to mass online trade.

And remember, that huge shift in consumer behaviour occurred when internal borders were closing and international travel was greatly restricted. 

Customer service was likewise disrupted. Internal call centres in Australia were closed as staff were ordered home. Companies that relied on outsourced call centres overseas fared even worse in some cases. 

Luzon province in the Philippines, which hosted call centres for many Australian businesses, shut down with less than 24 hours notice — leaving businesses to contend with how they could get their staff home from the office in the middle of a strict curfew. Taking calls from customers simply wasn’t the main concern!

The impact on call centres of the massive shift to work-from-home revealed the extent to which those businesses which took digital technology seriously suddenly had a massive advantage over those that didn’t. Companies that relied on Cloud-based software-as-a-service applications were able to migrate staff rapidly to home working arrangements. 

Those who were late to the party struggled to adjust.

Measuring success

Digital transformation, like many business process reengineering projects, often suffers from the difficulty of proving the benefits. 

BCG for instance, says that as few as 30 per cent of digital transformations deliver the intended business benefits.  ( https://which-50.com/only-30-per-cent-of-digital-transformations-are-successful-bcg/ )

However, such statistics need to be taken with a grain of digital salt. These figures tend to reflect the original deliverables in the business case, and really represent a failure of planners to understand and define the long-term benefits.

McKinsey has identified five metrics leaders should focus on to determine success in digital.

These include:

The return on digital investments. Don't just look at the value of an individual project, but rather how an initiative supports your strategic organisational goal. Don’t try to fix everything at once, but focus on a critical process or a customer journey and then broaden out from there.

Percentage of annual technology budget spent on bold digital initiatives. Don’t starve your most strategic and bold initiatives with parsimonious budgets. And recognise that technology projects have changed — the days of big monolithic IT architecture is past, moving instead to best-of-breed tools, customer applications and what the technologists call micro-services (https://www.gartner.com/en/information-technology/glossary/microservice#:~:text=A%20microservice%20is%20a%20service,independently%20deployable%20and%20independently%20scalable).

Time to market for digital apps. Don’t boil the ocean. Instead, focus on the quick translation of ideas into tools for frontline use. Time to market for things like new analytics models or new application tools should be measured in months, not years!

Percentage of the leaders’ incentives linked to digital. Align management incentives to the organisation’s digital goals and make sure that incentives across departments do not work at cross purposes. Your technology chief needs to be involved heavily in product design and delivery and their incentives should be inked to things like new application builds, cycle time, and business value generated.

Top technical talent attracted, promoted and retained. Finally, and crucially, focus on attracting and retaining the best talent in areas such as data engineering and analytics, design and user experience, and core technology. And remember that the talent you need will change as your digital maturity improves. So the staff plan can not be a set-and-forget engagement.

Digital transformation is a super cycle that will last decades. Many companies are only part of the way in their journey.

Industry analyst Gartner, for instance, says that 87 per cent of senior business leaders say digitalisation is a company priority — yet only 40 per cent of organisations have brought digital initiatives to scale. And Gartner warns that the gap between aspiration and achievement is widening (https://www.gartner.com/en/publications/the-it-roadmap-for-digital-business-transformation).

 The changes ushered in by the COVID-inspired acceleration are likely to prove sticky, according to Gartner, and consumers will continue to reward businesses that make the experience of being a customer simpler, faster and easier. 

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Article by Andrew Birmingham

Governing Data

Article written by Pete Crawford

Moving from Principles to Practice.

Let’s face it, data governance has a reputation of being a worthy, essential, but staid topic – a necessary prop underpinning aggressive innovative strategies or new analytic frontiers such as automated decision platforms powered by Deep Learning models. Part of this perception stems from a traditional notion that the function of data governance is to maintain data quality and reduce risk by upholding data protection regulations. By adhering to this formulation, governance is primarily viewed as a set of ‘command-and-control’ rules with escalation points.

A more balanced view is to regard data governance within the wider perspective of value creation. With this lens, governance becomes an important extension of developing data literacy skills and task-based, ethical accountability throughout the organisation.

“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.”

The journey from principles to practice starts by outlining why governance is central to any data strategy or business transformation endeavour. An assessment of what sort of governance model is most applicable to the power dynamics of the organisation follows. This leads to key milestones for establishing a governance program and the types of activities that support a well-informed and data privacy compliant playbook.

Why Data Governance Matters

Strategic drivers

• Changing regulatory requirements with data protection and privacy – which carries large financial penalties for compliance failure.

• Customer experience – the outcome from improved operational decision-making stemming from superior interpretation, sharing and evaluation of data.

• Accountability – linking the quality and trust of data with an assurance that employees responsible for business outcomes are part of its governance.

• Reputation management – by containing potential data breaches which invariably lead to costly PR fallout, weakened business valuation, customer churn and consumer abandonment.

Tactical drivers

• Data is available, discoverable, consistent and appropriate to different domains of users.

• Guidelines for the acquisition, sharing, processing, combining, retention and deletion of data.

• Capture of all consumer data event journeys coupled with consistency of interpretation across all data consumers.

• Prevention of bias and discrimination which may be inherent or inadvertently introduced through data, algorithms or practice.

Governance Structure Options

Item	Top-Down	Bottom-Up
Summary	~ Leads by authority ~ Hierarchical	~ Democratic control and management of conditions ~ Shared vision and principles based on network relationships
Principles	~ Standardised rules ~ Dedicated data stewards	~ Everyone has clearly defined accountabilities
Operating Model	~ Defensive - risk mitigation focused	~ Agile – policies and processes closer to the end user
Suitability	~ Rules are set by executive branch or dominant business unit who owns the business problem and proposed solution	~ Data is treated as a shared strategic asset across multiple business functions
Risk	~ Lack of context or sense of ownership over the application of rules	~ Coordination complexities across multiple business units

Setting Up a Data Governance Program

A governance program – like any change initiative – should begin by recognising the importance of storytelling. More specifically, there is a need to rethink and reframe narratives around enterprise use of data that look beyond ownership, protection or unquestionable economic value in order to bring into focus concepts that validate access, trust, agency and partnerships. Key considerations include:  

Awareness

• Ensuring executive sign-off and visible sponsorship of vision, principles, structure, funding and timeline.

• Understand, up-front, key messages from a strategic, tactical and operational perspective and how they should be differentiated according to role or domain.

• Be clear with stakeholders that there are time, resource and budget implications – too often governance is assumed to be a component of BAU, or worse, achievable by simply standing-up a committee.

Roles and responsibilities

• Establish and clearly communicate who is running the program. A common challenge is that people assume IT ‘owns’ the data.

• Identify data domains.

• Form a governance council from business leaders and partners.

• Identify operational data stewards. Stewardship is a trained and formalised accountability which describes a task-based relationship to data. It is not a hired position – anyone can be a data steward.

Standards, policies and processes

• Commence discovery to identify critical pain points for what business units cannot do because of a lack of availability, quality or knowledge about data.

• Review and consolidate existing policies and practices that define enterprise data engagement behaviours.

• Define missing policies e.g. how are algorithms being monitored for fairness?
  
  

Value creation

• Take a human design-centric approach by engaging with data consumers, both inside and outside the organisation, to recognise their aspirations and pain points when dealing with their ability to share, use or retrieve information.

• Educate stakeholders by translating governance principles into business context.

• Formalise data literacy programs by focusing on improving how employees:

  • Use numbers, statistics and infographics to convey important messages;

  • Evaluate data collection or automated decisions for bias and discrimination;

  • Use data analytics, find insights, identify trends and make decisions;

  • Use data platforms in a self-service capacity;

  • Link and share data without compromising privacy or proprietary.

• Clear associations are established between data quality, data usage and customer experience. Measurable incentives should form part of a group’s performance evaluation.

Barriers to success

• Lack of leadership – or, conversely, total reliance on top-down command. Leadership needs to visibly support the program and reward team accountability over data.

• Lack of investment – to counter, a simple cost-benefit exercise can help set a baseline against the costs of compliance if data governance is not implemented.

• Business units retain a proprietary sense of ownership of data – the breakdown of silos needs to be central to a coordinated data strategy and modernised data architecture plan.

Creating a Governance Playbook

A clearly defined playbook is required to put a data governance program into action. Each activity should be clearly documented, communicated, frequently updated and referenced to relevant regulatory or ethical standards. Some selective activities are listed below.

A playbook can be exhaustive, but if starting from scratch then concentrate on:

• Start small by focusing on a particular business unit or data domain.

• Define business ownership and identify roles and responsibilities.

• Map data flows across infrastructure and to operational tools.

• Place data education and task-based accountability at the centre of the program.

• Set measurable goals (especially around end user experience).

Strategic activities	Operational activities
~ Vendor risk assessments ~ Data sharing agreements ~ Data broker or marketplace evaluations ~ Establishing decision rights ~ Issue resolution and approval path ~ Acceptable use and consent standards ~ Technology platform options ~ Communications plan ~ Measuring and reporting value ~ Data collection bias ~ Algorithmic fairness	~ Data taxonomy and classification ~ Data collection standards ~ Data quality specifications ~ Data lineage and data flow maps ~ Data masking standards ~ Data privacy impact assessments ~ Issues register and matrix ~ Privacy-by-design ~ Differential privacy ~ Model registry and feature stores ~ Automated decision observability

Evaluating Governance for Short-Term Effectiveness and Long-Term Value

Business Impact Metrics Examples	Operational Metrics Examples
~ Compliance cost ~ Application development cost ~ Customer satisfaction ~ Data quality	~ Data governance maturity level ~ Data management efficiencies ~ Data literacy ~ Data governance issues register

LOOKING TO Leverage and utilise your data? REACH OUT.

Whiteark is not your average consulting firm, we have first-hand experience in delivering transformation programs for private equity and other organisations with a focus on people just as much as financial outcomes.

We understand that execution is the hardest part, and so we roll our sleeves up and work with you to ensure we can deliver the required outcomes for the business. Our co-founders have a combined experience of over 50 years’ working as Executives in organisations delivering outcomes for shareholders. Reach out for a no obligation conversation on how we can help you.

Article by Pete Crawford

Structuring Data Teams

Article written by Pete Crawford

Understanding Roles, Responsibilities and Operating Models.

How data teams organise themselves and evolve their operating model directly dictates the speed and success in delivering clearly defined value. There is often ambiguity associated over team roles – especially as tasks change in response to technology services that accelerate the ability to automate, collaborate and experiment. In these circumstances, how data teams are structured effects knowledge sharing, ownership over data initiative outcomes and alignment with business objectives.     

“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.”

The organising principles, or structure, of data teams can be reduced to four key elements.

1.     Leaders who can set context, navigate trade-offs and supply decision-ready data

Organisational data expectations can switch between defensive-oriented objectives (security, privacy, governance, regulation), platform-oriented objectives (infrastructure, data discovery, data quality) and offensive-oriented objectives (self-service insights activation, enterprise-wide data literacy, decision automation, partnerships, monetisation of data products). To accommodate this mix of expectations, data leaders must balance domain and technical expertise with an understanding of how to:

·      Advocate and embrace change both within the data team and at an executive level

·      Create a shared context and comfort with knowledge transition among the data team and their collaborators

·      Set an environment of ownership over decisions and accountability over outcomes 

·      Facilitate partnerships with knowledge hubs such as research institutes and open data organisations

·      Focus upfront on the challenges of operationalising data products by adopting a go-to-market mindset

2.     Assessing the state of the data team

Regardless of whether data and analytics is a new or a mature capability there are a clear set of questions that need to be regularly addressed:

People

·      What data and analytics skills are currently in the organisation?

·      How are skills and capabilities being identified?

·      How are people being recruited?

·      What career paths are available?

Processes

·      How is task prioritisation and job allocation handled?

·      Is the team assigned problems to solve or given a list of features to build?

·      How are objectives communicated and what results are measured?

·      Is there scope and incentives for training and skill development?

·      What workflow processes, collaboration tools and CI/CD practices are used?

·      How are ideas generated, assumptions validated and products tested with customers?

·      Who ensures data quality or ethical accountability over data or algorithms?

Relationships

·      What is the funding model?

·      What is the level of data expertise at the executive level (and greatly affects meaningful dialogue over the strategic engagement)?

·      What external relationships exist (SaaS vendors, post-graduate research programs, R&D audits)

·      What formal and informal mechanisms exist for empathising with customer or business problems?

3.     Role clarity

In broad terms, data team roles can be segmented into four groups:

Engineering: Data engineer; machine learning engineer; software/application developer;

Analytics: Data analyst, data scientist

Governance: Data steward (an accountability also commonly assigned to existing roles)

Complementary: Data lead/manager; data architect; product manager; project manager; business analyst; human-interactive designer (which greatly depends on the scope of ambition and funding)

Clarity over roles matters on three levels:

Firstly, the allocation of roles and relevant skillsets in relation to the sophistication, ambition and investment of analytical objectives demanded by the organisation.

Secondly, the alignment of responsibilities against key activities in the data value chain:

And thirdly, the emergence of new roles in response to rapid changes in data architecture, cloud infrastructure and tooling. This must also take into account that:

·      New titles and highly specialised skills will, over time, become more generalised.

·      Responsibilities are becoming less departmentalised and skills less mutually independent as DataOps practices mature, formalised learning grows, new data infrastructure ecosystems emerge. For instance, processes that use machine learning to automate the end-to-end development of machine learning pipelines (AutoML) are gaining greater adoption.

·      There are divergent approaches as to whether traditional data governance responsibilities or the emerging application and monitoring of ethical behaviours requires separate roles or simply describes a relationship to data and not a position.

4.     Making structural choices to optimise communication and complement capabilities  

Five models are presented which support the distribution of skills and responsibilities between data team members and across the rest of the organisation.

1.              Centrally pooled

Engineers and analytic specialists report to one data manager and consult to other business units. This model supports strong top-down governance, coordinated data management practices and inter-team knowledege sharing. It works best before operations scale.

2.              Distributed

Engineers are centralised under one reporting line while analysts report directly to business units. This model supports immersion in business operations and customised data transformations but places a heavier load on engineering capacity and consistent data interpretations. 

3.              Steamed

Engineers and analysts are managed as separate teams with separate data leaders. This model may suit organisations with large infrastructure initiatives and a set of clear strategic analytic priorities. It can also lead to weaker collaboration and knowledge sharing.

4.              Domains

Engineers and analysts are aligned to source-oriented domain data with data ownership placed into the hands of the business domains. This model is contingent on relinquishing centralised data ownership for distributed data architecture, global governance, open standards and domain-oriented data served as a product. This is similar to the Tribe model but with a formalised infrastructure layer to fully support domain ownership. It does, of course, require a very sophisticated engineering stack, API integration, deep domain knowledge and data-literate business units.   

KEY

LOOKING TO Leverage and utilise your data? REACH OUT.

Whiteark is not your average consulting firm, we have first-hand experience in delivering transformation programs for private equity and other organisations with a focus on people just as much as financial outcomes.

We understand that execution is the hardest part, and so we roll our sleeves up and work with you to ensure we can deliver the required outcomes for the business. Our co-founders have a combined experience of over 50 years’ working as Executives in organisations delivering outcomes for shareholders. Reach out for a no obligation conversation on how we can help you.

Article by Pete Crawford

Data Team Integration Models

Article written by Pete Crawford

Positioning data and analytics within the organisation.

Regardless of whether a data team is comprised of two, 20 or 100 people its ability to produce actionable insights and outcomes is heavily compromised if capabilities are not aligned to business needs. Furthermore, business needs are typically diverse, competitive and evolve as strategy unfolds and analytical use cases grow in complexity. To this end, it is worthwhile examining the differences between a handful of commonly used data capability models.

These models can also be viewed in the context of their suitability to divergent modes of business engagement, analytics maturity and resource coordination.

“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.”

Considerations

The adoption of a particular data and analytics organisation-alignment model is contingent on a number of considerations:

• What data or analytics models can be shared?

• What is the overall data governance structure?

• What is the state of data infrastructure development?

• How is data currently accessed and distributed across the organisation?

• What is the analytics maturity or independence within other business functions?

• Is innovation or AI a central tenet of business strategy?

• Has there been consistent success with operationalising analytical models?

Common Options

The adoption of a particular data and analytics organisation-alignment model is contingent on a number of considerations:

• What data or analytics models can be shared?

• What is the overall data governance structure?

• What is the state of data infrastructure development?

• How is data currently accessed and distributed across the organisation?

• What is the analytics maturity or independence within other business functions?

• Is innovation or AI a central tenet of business strategy?

• Has there been consistent success with operationalising analytical models?

Centralised

A single data and analytics team serves the whole organisation. Data people (analysts, data engineers, data science) sit together and treat other teams as clients.

Pros:

Sustainable funding; career growth aids talent retention; data is recognised as a strategic asset

Cons:

Lack of shared motivation or cooperation between groups; prioritisation challenges

Centre of Excellence

Combines the coordination of a centralised model with independent innovation intent.

Pros:

Focus and coordination when introducing new capabilities benefitting the rest of the organisation; optimal model for developing new infrastructure tools

Cons:

Can become isolated from business concerns; expertise may skew toward deep specialisations but neglect operationalisation skills; high operating costs.

Decentralised

Resources are funded and appointed by individual business units.

Pros:

Appropriate when there is limited inter-divisional coordination requirements (or inherent, irreconcilable internal conflicts)

Cons:

Duplication of resources; lack of ownership over data quality; data silos inhibit efficient data strategy

Federated / Embedded

Attempts to balance enterprise aspirations of the CoE model with the capacity to contract-out expertise for functional customisations. Analytics personnel report to business leads. Data engineering remains with the core data group.

Pros:

Encourages motivation and alignment of data-business objectives; retention of team identity; suitable for organisations with mature analytical competencies and systems

Cons:

Can lead to high costs; leadership conflicts between hub and spoke

Democratic / BI

Promotes self-service and domain-specific data ownership through development of data-as-a-service APIs and dashboards.

Pros:

Strong investment in data infrastructure; accessibility; rewards literacy with data visualisation

Cons:

High cost of infrastructure and training; systems need to be extremely robust as on-call engineering resources are scarce; limited role for data science and infusion of emerging data practices

Comparing Integration Models

Each model can be described and broadly evaluated against a set of coordination, management and capability factors.

A simple traffic light system denotes a generalised level of efficiency with deploying each model.

Mapping Integration Models

The five data and analytics integration models discussed can be loosely positioned in relation to both the complexity of an organisation’s analytical use cases and their capacity to control and coordinate data or personnel.

LOOKING TO Leverage and utilise your data? REACH OUT.

Whiteark is not your average consulting firm, we have first-hand experience in delivering transformation programs for private equity and other organisations with a focus on people just as much as financial outcomes.

We understand that execution is the hardest part, and so we roll our sleeves up and work with you to ensure we can deliver the required outcomes for the business. Our co-founders have a combined experience of over 50 years’ working as Executives in organisations delivering outcomes for shareholders. Reach out for a no obligation conversation on how we can help you.

Article by Pete Crawford

Treating Data as a Product

Article written by Pete Crawford

Approaching analytics with a product orientation.

Even prior to COVID, the development of mature data and analytics capabilities was regarded by over 75% of organisations as a ‘mission-critical function’ as central as IT, HR and finance units.[i] Now, there is even greater urgency to formalise data as a cornerstone of digital transformation. However, it is estimated that between 60-85% of analytical initiatives fail to be operationalised in support of wider business goals.[ii] In part, this can be attributed to conventional issues such as lack of executive commitment, talent, investment or adherence to the principles of a clear data strategy. But a major obstacle is simply that building or growing a data function is reduced to delivering a set of applications, features and capabilities – regardless of common agreement over data accessibility and actionable analytical insights.

“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.”

The value of data is too important to be managed as a project and organised along the lines of an IT program. Data is a strategic asset better served by adopting a product-team model.

Problems with Data Projects

• Investment is attached to a pre-defined scope of work instead of funding a team

• Work is focused on data acquisition and infrastructure development leaving teams removed from owning solutions to actual customer problem – unfortunately, this exemplifies the classic ‘mercenary’ versus ‘missionary’ debate that typically leads to an inability to retain talent

• Data governance is assumed to be a ‘one-size-fits-all’ model more obsessed with reporting structures than responding to nuances such as the diversity of data-sharing interests across an organisation or competing stakeholder incentives

• Teams of specialised data engineers and data scientists operate without appropriate understanding or application of a suitable product prioritisation framework – compromising effective analytical use case development

• The operating model with which the data team engages with ‘the business’ fails to change in response to the dynamics of data requirements or data consumption

• There are dependencies associated with maintenance – such as analytical models which dynamically influence customer behaviour – which contribute to future arguments over funding and resources after handover

In contrast, a product-oriented approach recognises that the outcome of data and analytics activities must produce a product which solves a customer or business problem and will evolve once the product interacts with users. Success or failure starts with whether or not it is adopted by users.

Organisations growing their data function should be encouraged to borrow processes from product, people and financial management disciplines and incorporate them into formalised data management best practice.

Recommendations

1.     Focus on context

A discovery period comprising interviews and audits to understand the context of the organisation from the perspective of the analytics value chain. This is the bridge between data and strategic corporate direction.

2.     Understand alignment challenges by assessing data maturity

This exercise uses the discovery phase to map the current state of capabilities (i.e. data architecture; data security etc.) against target state. To be effective, the target state must align with strategic OKRs and feed into an outcome-oriented data roadmap. Most importantly, the selection of metrics must reflect how the maturity of these capabilities inform the digital transformation of the business toward customer-centric innovation.

3.     Communicate a clear change narrative

A fundamental principle of a data strategy involves integrating data and eliminating silos. This course of action is typically disruptive to existing operational patterns and team structures. Productivity, collaboration and morale suffers without a change narrative and segmented communications plan crafted to the dispirit issues and ambitions of the organisation. Defining and mapping stakeholder interests along with a commitment chart are valuable tools in monitoring the effectiveness of these changes.

4.     Determine the most suitable operating model

Balancing the assorted skills sets of a growing data team against the complexity of data requirements of the rest of the organisation requires flexibility and continuous optimisation. Baseline data maturity and relationships with other business functions will dictate the initial operating structure – typically a ‘Centralised’ model – and how it evolves over time.

5.     Have a rigorous prioritisation process

Inevitably, data teams need to make considered choices as to which experiments to run and how to juggle competing data product options with ad hoc internal requests or unexpected challenges. An understanding of prioritisation frameworks is an essential product management capability. It stands outside Agile and DataOps methodologies and must be sensitive to time, visibility, type of quantitative or qualitative inputs and a culture of data-based decisions. Having dedicated product managers (ideally with a background in engineering work) is highly preferable for building bridges with business units and tracking execution. This is even more critical with AI/ML focused use cases.

6.     Own the customer problem

To be clear, an effective data product roadmap is not a series of features or applications with projected timelines meaningful only within the data team. High performing data teams are trusted to own a set of customer problems. The success of candidate initiatives across discovery, validation and delivery cycles is measured against end states (the problem to be solved from the customer perspective) and metrics (OKRs) alignment to top-down business strategy.

7.     Take responsibility for improving enterprise data literacy

Solely focusing on the strength of data capabilities within a specialised group inside an organisation is no longer viable. In this case, knowledge will not scale and a data literacy gap widens.

Enterprise-wide literacy with data and analytics needs to recognised as an explicit driver of business value. It requires formal inclusion in a data strategy and change management program. A data team will be expected to play a significant role as translators and ambassadors leading education workshop programs to enable measurable improvements in how the rest of the organisation access, interpret and deploy relevant, task-specific analytical models.

8.     Obsess on tracking data events

Data events need to be tracked with the rigor of financial assets. Managing and communicating analytical decisions across the organisation can only occur by capturing events, properties, triggers, values and pathways as data is acquired, transformed, distributed and operationalised. This process must be simple and visual. It augments inputs to a prioritisation framework and OKRs. Moreover, a growing data function needs to look beyond reporting to analysing data patterns that help internal teams such as sales, finance, HR, marketing or product make actionable decisions.

Article by Pete Crawford

[i] Gartner, Fifth Annual CDO Survey, March 2020

[ii] TechRepublic, 85% of Big Data Projects Fail, 10 November 2017

Digital Commerce

According to Gartner, five areas of digital commerce are being transformed due to the effect of Covid-19 on customer behaviours and businesses accelerating their adoption of online and digital alternatives.

1. Contact Purchasing

Contactless purchasing has become a preferred payment. Gartner predicts 80% of ordering and replenishment  will be contactless by 2024.

Customers:

Contactless payments and pickup/delivery

Operations:

Contactless business operations where companies can use robotics, artificial intelligence and computer vision to assist employees across the supply chain

2. Virtual Product Reviews

Currently the adoption of 2D and 3D product previews remains light.

Gartner predicts the uptake  will increase - software vendors offering visual configuration tools have reported a rise in business due to the pandemic.

Virtual Product Reviews may reduce the need for samples and showrooms and enable more customer self-service when buying configurable products.

3. Live Commerce Streaming Services

Live commerce involves video streaming to demonstrate products and interact with shoppers in real time to encourage purchases. Brands that have implemented livestreaming for selling or customer engagement are seeing early signs of success.

4. B2B Buying Experience

Businesses that sell B2B should think about transforming their shopping platform into one that has a more consumer-like feel to gain traction with younger professionals as they will expect a B2C like experience.

5. Enterprise Marketplaces

Enterprise marketplaces are online marketplaces operated by organizations that enable third-party sellers to sell directly to end customers.

Need to make the most of digital commerce trends?

Let us help. To learn more about how to leverage digital trends and pivot your business, contact us on whiteark@whiteark.com.au

Data Strategy

Data is a valuable resource but often businesses find it challenging to unlock that value, due to the sheer amount of data available - as well as the challenges which come with collecting, organising, interpreting and activating it.

Research shows that 54% of organisations still struggle to provide stakeholders with data that can inform their decisions.

A data strategy can assist businesses with overcoming these challenges and accessing the value of their data while efficiently using their resources.

DATA STRATEGY

• Unlocks the power of your data

• Helps you to harness the volume of data, which is always increasing

• Improves data management across the entire company

• Assists with efficient resource allocation

Decision-Ready Data

Decision-Ready Data is critical to informing business decisions and deciding on strategic direction. However, research shows that although organisations have been building analytics and insight capabilities, over 54% of organisations still struggle to provide stakeholders with data that can actually inform their decisions.

Common Data Quality Challenges include:

Accuracy, comprehensiveness, completeness, centralisation, source of truth.

Common People, Process & Technology Limitations include:

Process design, platform/technology, capacity, agility/execution speed, lack of automation, analytical capability, organisation alignment, prioritisation.

A data strategy can assist businesses with overcoming these challenges and access the value of their data while efficiently using their resources.

Importance of having a Data Strategy

1. Data Strategy Helps Unlock the Power of Data

2. Volume of Data Is Increasing - 90% of the data in the world became available in the last 3 years.

3. Data Strategy Improves Data Management Across the Entire Organisation

4. Data Strategy Helps You Use Resources Efficiently

Data strategy is a central, integrated concept that articulates how data will enable and inspire business strategy.

Essential Data Strategy Principles

Integrating Data and Eliminating Silos

• Makes data more accessible and fosters collaboration between different departments

• Helps people get data more efficiently and can enable new data-driven projects

Streamlining Data Collection and Sharing

• Having established procedures means you can collect more data more efficiently, and that the data you collect will likely be higher-quality

• It also keeps your information consistent and well-organised, which makes it easier to use and helps you derive value from it

Setting Clear Goals and Objectives for Data Management and Use

• Your goals will drive your data strategy and activities and help you improve how you handle data

Making Data More Visible and Accessible

• It’s crucial that you find a way to store data so people can quickly find and access the information they need without having to create copies of it themselves.

Making Data More Actionable and Easily Shared

• Putting your data in a consistent, usable format will reduce the number of steps employees need to take before they can use it and make it easy to share within the company

Establishing Clear Processes for Data Management – Data Governance Model

• Data governance refers to setting rules and standards for how individuals and groups within an organisation manage data. The goal of data governance is to make data easier to access, use and share to achieve broach broader business goals

• The key goals of a governance model should be clearly defined to ensure success:

  ⊹ Avoid siloed decision-making

  ⊹ Use synergies between business units to improve data assets

  ⊹ Provide business units with support and resources to prioritise data challenges resolution

  ⊹ Support the management of key initiatives across business units

Establishing Guidelines for Data Analysis and Application

• Define guidelines for how employees should analyse and use data

Get hooked on data

You don’t need to be a big fish to make the most of your data. You just have to start somewhere.

If you’ve read a few of my articles, you’ll know I’m passionate about the importance of data and analytics to drive good business decisions both strategic and operational.  Not everyone is doing this, which means there are lots of exciting opportunities for many businesses to unlock. 

Knowledge is power

The organisations that have mastered data analysis have reaped the benefits. Just look at the big players like Netflix and Amazon. Their ability to meet the viewing and buying needs of their many audiences has propelled them into household names. But you don’t need to be a big fish to make the most of your data. You just have to start somewhere.

The 80/20 rule

This rule is relevant for data, analytics and insights. You don’t need to have all the data and the data doesn’t need to be perfectly clean, but it’s imperative you use what you have to take a step in the right direction.

From my experience the following are practical steps to start enhancing your data, analytics and insights capability easily.

Practical steps you can use

Seek out the people in your organisation who are interested in data and analytics.

Normally you will find them in finance or IT.

Give these individuals or teams a specific question to answer each week.

For example – tell me more about the customers that are leaving us:

• Profile

• Demographics

• How long have they been a customer

• Claims history

• Payment/credit history

• Customer service complaints/customer experience scores 

Get them to present this to the relevant Executive.

Ask them to detail what they’ve learnt and what would they do differently.

Now it’s time to think about your data strategy

As you start to build momentum, write a list of all the strategic and operational questions you want to answer.  This should create a pipeline of questions that can be investigated leading to recommendations to the Executives on how these insights should change strategy and operations. 

The changes should then be tracked and monitored through your regular key metrics reporting to understand how these shifts have positively impacted business results.

Need a data champion to kick things off?

From finding the data champions within your business, to identifying the key data outputs best suited to help your business grow into a smarter, stronger operator, reach out to me. My expert experience, passion for data analytics and team of data and marketing professionals may be just the kick-start your company needs to grow with data.

More data equals more opportunity?

A good data strategy will help you look at your business to identify outputs that will help you deliver.

More data, more opportunity
In 2020 we’re in an amazing position to understand our customers and business functions like never before. And it all comes down to the sheer volume of data at our fingertips.

It starts with strategy
Whether it’s via cash flow reporting or CRM segmentation, every organisation holds a significant amount of customer, member, consumer and employee data. But analysing this can seem like a giant task. Where to start? What’s important and what’s not? That’s where a data strategy comes into play.

Which data matters most?
A data strategy will look at your business objectives then explore data outputs that will help you achieve these. For example, if you’re moving away from a product led approach to a more customer focused organisation, data based on your key customer segments - which are growing, which are profitable and how to mobilise the organisation around this, will form the basis of your data strategy. If, on the other hand, your business strategy is focussed around transitioning from an office based salesforce to a mobile model, important data outputs could include facilities and cost driver analysis.

A clear focus cuts through data clutter
So to get it right, you need to start from the beginning. Once you can define your business strategy, you can define the data points you should be exploring and how to utilise this data to understand and then change the way you drive your business.

Data strategy health check
At a top level, you can get a clear idea if you’re using data effectively by answering the following questions. If you’re unsure, you may need a hand with your data strategy.

• What products/services are profitable?

• What customers are profitable?

• What customers are likely to leave you?

• What is the profitability of your business units?

An accurate understanding of these questions leads to:

• Improved customer experience

• A more tailored customer approach, ultimately results in higher revenue

• An ability to optimise the cost base

• Utilise your spend data to understand, forecast and optimise the costs in the organisation

• Increased productivity across the value chain

• Improve the sales and service approach to drive efficiency in key business processes.

Bringing in a data expert

Data is measurable and accountable, allowing you to optimise your strategy and operations through real insights and clear direction. It can just be a bit of a handful to determine what data you should be investigating and how. If you need advice on where to start reach out to Jo Hands or an expert from our data team on whiteark@whiteark.com.au

Digital Transformation. Are you ready?

You can’t afford to wait for this pandemic to be over, so what are you doing to future proof now.

People have an amazing capacity to forget. To revert to their old behaviours. And that might have been the case if COVID had come and gone quickly. But it’s not going anywhere, and we all know by now - this time it’s going to be different.

The question on everyone’s minds is not ‘when will this be over?’, it’s ‘what will the new normal look like?’

As business leaders, however, we don’t have the luxury of being able to sit on the sidelines and watch. We need to anticipate the market. Analyse data and look into the future. Of course nobody knows for sure what the ‘new normal’ will look like, but it doesn’t mean we can’t prepare nonetheless.

“There are decades where nothing happens; and there are weeks where decades happen.”

— Vladimir Ilyich Lenin

What’s apparent all around us, however, is that the already-fast digital transformation of organisations, has accelerated to a furious pace.

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B2B

In the B2B space adoption of zoom, teams, slack, trello, webex and other digital tools has gone through the roof. And in the background of all that, there are projects frantically going on to protect the security of a dispersed workforce, and to move instantly redundant legacy systems to the cloud.

B2C

In the B2C space the changes are even more obvious, and they happened immediately. If you didn’t have an effective online presence before, you’ve either been playing catch-up, or you’re already out of business. What people have been buying has changed too.

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McKinsey surveyed consumer sentiment and behaviour across 45 countries, and on 8th July published their results*. One of the consistent themes, worldwide, was a shift to more mindful shopping. In the US, for example, 31% of people surveyed are changing to less expensive products to save money, and 21% are researching the brand, and product before making a purchase.

And they’re not researching those brands standing in aisle 7 with a mask on. They’re at home, searching online and then getting their groceries delivered to the door.

And that kind of capability doesn’t just happen overnight. So the businesses that were prepared, and ready for the world to go digital have not only survived, they’ve thrived.

To gain a little insight into Australian organisations’ digital preparedness for COVID, we spoke to Rube Sayed, General Manager of a Sydney-based Managed IT Services company, Datcom Cloud.

“For some of our clients it was a seamless transition. They were already 100% in the cloud. Phone, apps, security all in place – and they just got on with it. For many, however, they had to rush through projects that would normally take a year or so, into months. We’ve had to expand our workforce by about 20% to deal with it all.”

— Jo Hands, Director Whiteark

So while you might need a crystal ball to know what’s going to happen in the future (in 2020 – who can tell) you certainly don’t need one to be prepared.

If there’s a digital transformation project you still haven’t gotten around to yet.

Don’t wait. Give me call, or reach out on LinkedIn. And I can help.

As the saying goes, you don’t just stumble across luck, it’s what happens when preparation meets opportunity.

So ask yourself, are you just ready? Or are you COVID-Ready…..

* McKinsey Ref: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19#