What is data monetisation and why leaders should accelerate their data monetisation adoption strategy

Data Monetisation is the process where organisations use their data to differentiate themselves from others, increase their market share and increase revenue. Data monetisation can be applied by using data to optimise and reduce operation costs, improve customer experience and retention through advanced analytics and/or through selling data as a product.

To better demonstrate the opportunity of monetising data. Figure 1 illustrates why organisations consider monetising their data. Improving customer experience, improving supply chain performance, and increasing revenue were cited the most as the top 3 reasons for monetising data.

In this article, I will focus the lens on two critical elements of data transformation. Firstly, why executive leaders must treat data as a strategic asset and ensure that creating data strategy is on top of their agenda (“Why should I care and why should I invest in data?”). Secondly, I will provide insights and methodologies that will help them pursue a data strategy to inspire organisational culture change and spike creative and innovative thinking, measure value delivered and continue to maximise the value generated.

The Opportunity:          

The rapid investment in digital transformation by organisations, diversification of data sources and the advancement in technology and the Internet of Things (IoT) are creating an opportunity for organisations to create value from data that will accelerate their market leadership and financial growth. According to Markets and Markets, the data monetisation market size in terms of revenue was estimated at $2.9 billion in 2022 and is forecasted to increase up to $7.3 billion by the end of 2027 (component, n.d.). 

Nevertheless, data itself is like oil: if unrefined it cannot be used nor translated into value (Clive Humby, 2006). Most organisations struggle to analyse their data effectively to unleash its full potential.

The Problem:

There is a gap in how to define a compelling data monetisation strategy, and Leaders are still learning how to formulate the right data strategy that will deliver near-term value to justify data investment, as this requires organisations and data leaders to transform the way how they think about their data across people, process technology and organisation culture.

How to Start:

First and foremost, in order for data leaders to make progress in defining and communicating their data monetisation strategy, they need to understand the following five definitions:

·       Data Literacy / Data Democratisation: The ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value (Gartner, 2021)

·       Data-Driven Organisation: A data-driven organisation is an organisation that treats data as a strategic asset and then builds capabilities to put that asset to use not just for big decisions but also for everyday action on the frontline (Ishit Vachhrajani, Amazon)

·       Data Liquidity: Refers to the ease with which a data asset can be monetised. (Wixom, 2021)

·       Data Strategy: A long-term plan that defines the technology, processes, people, and rules required to manage an organisation's data assets (Amazon, 2023)

Often data leaders ask: how do I start, and what are the guiding principles for defining and implementing a data monetisation strategy that will get the highest and quickest return on investment, enhance their decision-making process and support the future growth in data volume and ever-evolving use cases?

Figure 2 shows the six core principles data leaders must know that will guide them in defining a compelling data monetisation strategy. These are:

1.      Principle one: Data can be used to improve and optimise operations resulting in reducing operating costs (contributing to bottom-line). 

2.      Principle two: Data can be wrapped with the organisation's product offering to provide reporting and analytical features that increase the product’s value proposition and increase customer satisfaction.

3.      Principle three: Data can be sold, providing a new revenue stream (contributing to top-line).

4.      Principle four: Organisations must build the right capabilities (i.e. Data infrastructure and systems), processes and policies (i.e. data governance, data quality processes and data access policies) to treat data as a product which can generate impressive financial returns.  

5.      Principle five: Organisations must have the right attitude and culture to data. 

6.      Principle six: Crawl, walk, run, then sprint!

It is difficult for any organisation to define and implement all six principles in one single strategy. Leaders need to choose the right mix of principles and manage them in line with their organisation’s strategic objectives (as a whole).

The best way to achieve this is by pursuing incremental adoption. This approach is lower risk and provides higher value creation for the organisation (Wixom, 2021). The value-effort matrix (Figure 3) is always a great way to categorise different opportunities into four quadrants that will help data leaders to identify which data monetisation principles they need to adopt – Always start with high-value, low-effort and lower-risk opportunities (Action immediately quadrant).

A sell (Information System) data monetisation principle will manifest as costly because it requires mature data capabilities and data-driven culture. By contrast, the improve or wrap approach lays the foundation for organisations to start their journey to becoming data-driven organisations. In either situation, these organisations must adjust the stagger their investments to achieve both short-term and long-term goals. (Wixom, 2021), (What is Data Monetization?, 2021)

To put this framework into practice: operations optimisation and cost reduction should be on top of every organisation's agenda. Here are the steps for achieving it:

1.      Step 1: Identify a few manual back-office processes that can be automated with minimum effort to save manual labour costs.

2.      Step 2: Identify the level of effort to automate this process. 

3.      Step 3: Identify the value that automation will generate; this will always be a contribution to the bottom line (dollars saved). 

4.      Step 4: Plot each opportunity on the effort-value matrix based on the effort required to implement it vs the value it will deliver.

5.      Step 5: Identify opportunities that will require a low effort and will deliver high value, that is the “Action Immediately” quadrant.

6.      Step 6:  Identify the data monetisation principles the strategy needs to include, which are Principles one and four-six.

Selecting the right data monetisation strategy

For many organisations, it may not be obvious which data monetisation strategy is the best fit. Why do some high-tech and public sector organisations focus on operation optimisation while others focus on customer focus or future ready? In reality, no two organisations are the same (vision, structure and culture). Choosing and committing to a data monetisation strategy, even if only a high-level one, is an important first step. When an organisation commits to a strategy, it sends a clear signal to the organisation's internal stakeholders and teams about which activities and investments need to be prioritised. To begin, leaders should ask three questions:

1.      Which strategy offers the most value given the organisation’s business model?

2.      Which strategy best aligns with the organisation’s vision and mission?

3.      Which strategy is most achievable given the current state of the organisation’s capabilities - People, process, culture and technology?

These considerations can be used to formulate a strategy in two ways:

1. Top-down approach: With the top-down approach, organisations select the data monetisation strategy that is best aligned with their overall strategic intent. They then perform a gap analysis between the business model and data capability they require against what they currently have.
2. Bottom-up approach: With the bottom-up approach, organisations identify the most achievable data monetisation strategy that best aligns with their current business model and data capabilities utilising the effort-value matrix (Figure 2). High-value, low-effort initiatives (i.e., the top left quadrant of the value–effort matrix) must be prioritised first. Leaders should reflect on how they can use the first data monetisation strategy in the short term to build up to the second, long-term choice.

Neither approach is right or wrong; they are simply different – much like how the organisation defines their internal roadmaps and priorities. 

Capabilities required to pursue the right data monetisation strategy

Organisations rely on five data capabilities that are required to choose the right data monetisation principles: Improve, wrap, sell or a combination of the three and define the right compelling data monetisation strategy, whether it is a top-down approach or/and a bottom-up approach.

Figure 4 shows the five core enterprise data capabilities organisations are required to fuel their data monetisation strategy.  

Mastering all five data monetisation capabilities require lots of effort and investment; organisations tend to adopt an incrementation adoption of these capabilities to help them to demonstrate the value and minimise the investment cost. For example, if an organisation chooses a bottom-down strategy, then they need to adopt an Improve or wrap principles, or a mixture of both. Then Organisations chose the right capability to achieve that, as shown below (highlighted in orange):

While organisations focus on information sell (Information systems), they need to master all of the five capabilities.

Conclusion                      

Defining a data monetisation strategy can be complex but rewarding - if defined and executed successfully. Data leaders must comply with what they want to undertake, what capabilities they have vs require, risk tolerance, cost, value and what the organisation is capable and committed to achieve.

By committing to a data monetisation strategy, data leaders can bring disparate business units together with a shared purpose and vision. This commitment helps them position the organisation to maximise returns from data monetisation investments.

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