A Service Dominant approach to Analytics Transformation
By Lionel Kho, Chief Strategy, Data and Analytics Officer
Congratulations! You have just been appointed into the senior executive role of Chief Data Analytics Officer, Chief Transformation Officer, Chief Operating Officer, or some such glorious title. You are tasked with articulating and delivering the “Value of Analytics” (and likely, also Data Science, ML and AI) to the organisation.
It is a monumental task, and you know it. The most common reflex is to appease the CFO by gravitating towards defining ‘Value’ in direct financial metrics: ROI, incremental revenue, OPEX reductions, or worse… headcount. If you are fortunate enough to work in a customer forward organisation, you may be tasked to include CSAT and NPS metrics.
Stop. This path is a trap, riddled with pitfalls for the unsuspecting. The value generated by Analytics is deeply embedded to, and can greatly amplify, the total value your business aims to generate. It is not a challenge that can be simply quantified with just financial metrics. To do so would risk understating the pervasive impact of Analytics, limiting investment to isolated projects and pilots, rather than establishing a transformative business capability and sustainable practice.
Instead, we need to slay the metaphorical dragon: Analytics Transformation.
It is crucial to distinguish between simply doing analytics and undertaking an Analytics Transformation. Analytics is the exploitation of data to find meaningful relationships and actionable insights which create business value, limited by objective and scope.
Analytics Transformation, however, is a far broader and deeper enterprise undertaking.
I define Analytics Transformation as:
“A foundational and strategic realignment of an organisation's body of knowledge and skills (Competencies), workflows and processes (Practice), data and technology (Assets), and culture and structure (Organisation) to embed and scale data-driven decision-making into its core operations (Distribution), thus providing Service to Stakeholders, enabling them to achieve satisfactions that assist in generating a sustainable competitive advantage, who then in turn, service customers that are the ultimate co-creators of business value (Value).”
I call these: Competencies, Practice, Assets, Organisation, Distribution, Service, Stakeholders, and Value, the Dimensions of Transformation.
Before diving in, we should acknowledge a difficult truth: the failure rate for data, analytics, and digital transformation initiatives is incredibly high. Research estimates transformation failure rates ranging from 70%, to as high as 90%.
- McKinsey notoriously highlighted that 70% of business transformation projects fail, and suggests that digital transformations, which often include analytics, face similar or even higher failure rates.
- Gartner estimated 80% of data and analytics governance initiatives will fail by 2027, partly attributing CDAOs taking a “centre-out, command-and-control approach” rather than targeting tangible business outcomes.
- BCG also points to a failure rate of 70% for digital transformation, listing Integrated Strategy, Leadership Commitment, High Caliber Talent, Agile Mindset, Effective Monitoring, and Data Strategy as six key factors.
- Closer to home, a 2024 Melbourne Business School study found “80% of all data science and AI projects fail to achieve their stated goals,” where “the actual failure rate for analytically immature organisations exceeds 90%.”
As practitioners, we have a greater collective experience of transformation failure, rather than of transformation success.
The fundamental challenge of an Analytics Transformation, in consideration of the Dimensions of Transformation, is that transformation is necessarily multidisciplinary in nature. Only limited value is created in a vacuum by a single team, report, or ML Model. True value realisation requires the successful convergence and collaboration of diverse skill sets and business functions that have historically often operated in tribal silos.
This multidisciplinary requirement often exposes a significant challenge in leadership capability within the Analytics domain. Many who rise to leadership positions in data and analytics have built their careers on deep technical expertise in statistics, machine learning, data architecture etc. This technical depth is, and will remain, important.
However, leading an Analytics Transformation demands more than technical mastery. It requires a broader set of capabilities often described as T-shaped skills: deep expertise in one or two technical areas combined with a broad fluency across related business domains, particularly in strategy, operational excellence, organisational models, product, marketing, risk, customer experience, and change management.
Simply put, a leader of Analytics Transformation must have deep and wide knowledge, to impact the business significantly and successfully.
Lastly, we must ask ourselves what is meant by Value, in our organisational context. As a 2024 MIT Sloan Management Review5 states, despite data's effectiveness, it is often tough to attribute business outcomes to analytics efforts, contributing to a downward spiral of lost momentum and funding.
A successful Analytics Transformation must begin with a clear understanding of Value. Not value defined only from the perspective of one stakeholder (read: CFO), but Value as it is perceived and experienced, by all Stakeholders, across the whole business value chain, and ultimately, as it is perceived and experienced by the customer.
We must broaden our definition of Value to include intrinsic values (time, effort, etc) in the value chain, often masked by the financial value we desperately wish to assign to investments made in Transformation activity.
This shift towards Customer Centricity has been pervasive in organisations for decades. Customers are bombarded with surveys for NPS, CSAT, and CES scores. Should not this customer centricity also become pervasive in technical domains, such as Analytics?
To achieve this crucial perspective shift, we will look to a piece of thought leadership from the marketing and service domain: Service Dominant Logic (SDL).
SDL offers a fundamental rethinking of value creation. Unlike traditional perspectives that focus on products, SDL supposes that it is Service, the application of resources for the benefit of another, that is the fundamental unit of economic exchange.
This is a profound shift from Goods Dominant Logic (GDL), in which economic activity is seen as the production and distribution of goods (which I shall call Products), where value is embedded within the Products by the producer, and exchanged for currency.
GDL focuses on the "value-in-exchange." The value equivalent of a Product at the point of consumption. The producer is the value creator, the customer consumes its value.
SDL shifts the focus from the output to the outcome. The consumer's experience and satisfaction become a core part of value creation. In Analytics Transformation, we may say that business (Stakeholders) co-creates the value generated by Analytics Service, and in turn, the customer co-creates the value generated by a business.
There are Eight Foundational Premises6 of Service Dominant Logic:
The application of skills and knowledge is the Fundamental Unit of Exchange. Value is exchanged through the benefits of specialised competences. Goods (Products) are used as transmitters of embedded skills and knowledge.
Indirect Exchanges of Value masks the Fundamental Unit of Exchange. Enterprise thinking (generally) focuses on imparting value into Products, but this hides the true value exchanged in the utility of the Service a Product provides.
Products are Distribution Mechanisms for Service. The importance of Products is not so much in owning them, as in obtaining the Satisfactions they render, or provide a platform with which to render.
Knowledge is the Fundamental Source of Competitive Advantage. Skills and Knowledge resources provide the Fundamental Unit of Value upon which competitive advantage and growth are built.
All economies are Service Economies. Services and the skilled and knowledge resources they represent have always been the essence of economic activity.
The Customer (or, Stakeholder) is always a co-creator of Value. It is only in the final use of a Service that Value is realised, and therefore the customer must be involved in the maximisation of the Value of any Service.
The Enterprise can only make Value Propositions. Businesses can only offer the proposition of Value (something potentially of Value) and seek to maximise the Customer Value Proposition to entice Value co-creation.
Service Logic is Customer Oriented & Relational. It is only in the relationship with the Customer that a business creates Value, in the use of a Service, in the Context of that Customer.
…which I will synthesise into The Three Tenets of Analytics Transformation:
Value is only attained when Analytics is a Service (not, as-a-Service).
The proliferation of Product thinking has diminished the fundamental truth: Analytics is a set of skills and body of knowledge (or, Competencies). Its Value is derived from the application of Competencies (or, Practice) to render the utility of Service and obtainment of Satisfactions, for a given Stakeholder.
Analytics must be of Service to a Stakeholder, to co-create business Value.
Instead of seeing a dashboard, or a model output as the final "product" delivered by analytics teams (a GDL view), we now see them as Resources or Assets. Only when these Resources or Assets are integrated by business users, customers and other stakeholders, into their own processes and decision-making activities is Value co-created. We move from delivering outputs to generating outcomes.
Value is only co-created at the “Intersection of Interests.”
Value is not created by the producer of Analytics and delivered to the consumer of Analytics. Stakeholders do not simply consume Analytics outputs. Instead, Value is generated through the joint efforts of multiple operators, including the Stakeholder, who integrates the Service rendered by Analytics, for the attainment of Satisfactions, in their own context.
Value can only be co-created where Practice meets Purpose: at the “Intersection of Interests.”
The Value of an Analytics Resource or Asset is not determined when it is delivered (per GDL). Instead, it is realised and determined only when in-use, providing a Service as a Resource or Asset, by Stakeholders.
Value is only propagated when Analytics is a Distributed Service.
Whilst Practice is the application of Competencies, its Service must still be rendered to Stakeholders to co-create Value. Products, or the productisation of Analytics Practice, is the transmission mechanism for Analytics Services. Products may be artisanal or industrial but are often essential to obtaining Stakeholder Satisfactions.
Analytics Service must have a transmission mechanism to distribute Value.
This transmission mechanism is as equally important as the quality of the Service it seeks to render. It must be embedded into operational workflows beyond the domain of the analytics team. Distribution of Service, in my humble opinion, is often where Transformations fall short in the delivery of Value.
So, after all that, what is the Value of Analytics?
The Value of Analytics is as a Distributed Service, where the Value generated and realised, as defined by Stakeholders in the value chain, is co-created through the attainment of Satisfactions, at the Intersection of Interests.
For some stakeholders, the Value will be in effort saved, for others, in insights gained. For customers it is in the gains generated, or the pains alleviated. Each of these Values can ultimately be translated into some form of currency, whether that is in OPEX saved or Revenue generated. However, that is not the place to start. We must first understand what Satisfactions are being sought by each Stakeholder, to whom Analytics Service is being rendered: what do they want us to achieve for them, and why? If we do not first ask this, we risk limiting, or in the worst case, destroying, value in the organisation.