Data monetization - AWS Cloud Adoption Framework: Business Perspective

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Data monetization

Use data to obtain measurable business benefit.

Data monetization refers to the practice and associated processes to generate measurable business value from data and insights. While data monetization, as we will see later in this section, is only one of the two commonly practiced methods of generating business value from data and insights, the practice is commonly known as data monetization in title and understood as data commercialization in practice. We’ll define the two practices and their specifics, but first, let’s spend some time on the fundamentals of the concept.

A measurable business value ultimately results in economic benefits. However, most of higher-value business benefits generated through practices of monetizing data (for example, augmented positive impact of better decisions, enhanced productivity resulting from employee satisfaction, and improved retention) are hard or practically impossible to quantify in terms of economic outcomes, especially since simulating a scenario where such practices are absent so to create a measurement baseline is often impossible.

In most cases, the first thought crossing a business executive’s mind when thinking of data monetization is selling the available data generated through the course of business operation. This thought process focuses on data commercialization, only one and often the more simplistic aspect of generating business value through data and insights.

While easier to practice, especially in case of unregulated data, the business purpose of most organizations is different than generating and selling data. Instead, they acquire and use data in support of their operation, generate it as a byproduct (albeit they often fail to fully collect or properly capitalize on the full potential of this asset), devise patterns, and rely on data usage as the special blueprint and strategic advantage that differentiates them from their competitors.

Selling their data in a data commercialization scheme will commonly translate into making their blueprint and strategic advantage, public knowledge. The foregoing is to emphasize that organizations should look at data commercialization as only one of the potential methods to generate business value from their data and, where possible, only in form of composite insights, as opposed to acting a statement of outright disregard of such strategies.

With this preamble in mind, let’s turn our attention to industry practices of generating measurable business value from data and insights. In this conversation, remember to interpret the word data in the following methods as both pure data as well as inferences and insights generated through the combination various data sources or the latter as a source for statistical and analytical methods that generate insights. The two main approaches of Data Monetization and Data Commercialization help you realize the value of data as a business asset.

Data monetization

Data monetization refers to practices that lead to internal business value realization in service of other business disciplines. Data monetization includes two main categories of implied return and captured value.

  • Implied return is the first category of data monetization, and arguably the hardest to measure in terms of direct economic outcomes. Implied return typically encompasses examples with qualitative value measurement. Examples are such benefits as those realized through data dissemination (often incorrectly known as data democratization, latter a sub practice of data governance only applicable to specific and limited number of consensus based situations to manage data quality), improved data-informed business decisions, improvement in product development lifecycle (for example, to reduce rate of returns later in the lifecycle), productivity improvement through employee satisfaction, improved retention, improved overall business efficiency and effectiveness are implied value in nature.

  • Captured value is the second category of data monetization and somewhat easier to measure in terms of direct economic outcomes. Captured value typically encompasses examples with both qualitative and quantitative value measurements. Examples are such benefits as those realized through price optimization (leading to increased volume of sales and thereby profit), cost optimization across the business value chain, customer retention, mass personalization, product cross sell and upsell and market and product opportunity identification.

Data commercialization

Data commercialization refers to the practices that focus on direct exchange through data offerings, data enhanced offerings or insights exchange. This category is easiest to measure in terms of direct economic outcomes since it treats data and insights as an independent product or features of a product. Data commercialization typically encompasses examples with quantitative value measurement.

Examples are direct selling of data through sales or licensing credit, use of data as payment method in exchange for goods and services, use of data in exchange for such value as favorable pricing, conditions (such as favorable shelf space) and discount and addition of data and insights as features to enhance desirability and marketability of a product. The latter example can lead into secondary source of income in form of licensed or subscription-based models, whether direct or through royalty generating third party channels, that generate personalized insights or offer user friendly interface to consume generated data (such as health information from wearables).

Finally, we focus on the basic steps to embark on the journey towards generating measurable business value through data followed by a high-level maturity model to frame our expectations gain a general appreciation of the complexities surrounding these endeavors.

Foundation

  • Data and insights business value strategy — As with any strategic initiative, executive sponsorship is an imperative necessity, business and technology governance indispensable to the success of generating business value through data.

    • Begin your journey by performing a business-focused assessment of the data landscape, both internal and currently relevant, and future potential external. The assessment should direct specific attention to identifying business use cases and revenue potentials where augmented use of data has the potential to lead to additional business value. Use the previous Data Monetization and Data Commercialization model and examples as a guide to help you frame your assessment and classify your findings.

    • The three stages of maturity proposed in the following sections can help inform the stages of your roadmap while you identify initiatives in each stage along the dimensions of business and technology governance, organizational change and communication, talent acquisition and upskilling, process improvement, and technology enablement.

    • Each stage of your roadmap should clearly identify the types of use cases the achievement of its full capability will enable. Managing expectations and clear communication will be crucial to the achievement of this endeavor, maintaining and augmenting executive and business leadership support necessary to see the journey to its successful completion.

    • Aside from identifying use cases, your assessment should cover areas of people and processes and direct emphasis on identifying areas where improvements in business and technology governance can promote adherence to future practices. Use these findings to develop a roadmap to generate business value through data and insights.

    • Obtain executive commitment and support for the execution of this roadmap, secure the resources you need and embark on your journey. Designing the initiatives section of your roadmap in form of shorter in duration yet meaningful in outcome projects (or sprints where applicable) can go a long way to help you maintain organizational interest, excitement and support.

A likely outcome of your initial assessment effort will be the identification of data leaks. Data leaks usually happen in three main categories of:

  • Various business units buying multiple copies of the same external data set from one or different vendors

  • Data or insights shared with outside parties without any clear business benefits

  • Data or insights used to generate business value with poorly tracked the value (if at all)

Clearly identifying and stopping these leaks will be an example of immediate value your assessment effort is generating and can help to direct added resources and commitment to your journey of generating business value through data and insights.

Start

  • Operations data and insights business value — The initial stage of your roadmap should focus on supporting improvement to internal operations, identify efficiencies, develop methods and capabilities to collect additional information in support of enhancing specific process or a set of interacting processes. Quick turnarounds from initiative start to business value is a core characteristic of this step in your journey.

    • In this phase, you should focus on enhancing the culture of data-oriented decision-making in the organization, strengthening the skills required for the upcoming stages of your roadmap (talent acquisition and upskilling) and improving upon the foundational technical abilities you need to promote an agenda focused on generating business value thorough data and insights.

    • Attempt to address most if not all business use cases in the implied value category in this phase. Some of the captured value business use cases could also be good candidates for this phase (such as cost optimization). However, most, especially those that are interacting with or facing your partner organizations (such as vendors, institutional clients and distributors), are likely to gain more traction and maturity in the next stage of your roadmap.

    • Depending on the nature of some of your data and insights sets, your organization and your estimate for its ability to absorb change, develop skills and deliver foundational technology. Limited number of business use cases categorized as data commercialization might prove a candidate for this phase although a good practice would be to delay these business use cases to a later stage of your roadmap and organizational maturity from this perspective.

Advance

  • Community data and insights business value — The second stage of your roadmap should focus on extending your foundation and practices to the community of entities that play a role in your business value chain. This could include a wide range of entities such as your raw material producers, companies participating in your supply chain, government agencies (such as those regulating your industry and sector or the information it handles), your manufacturing line (especially contract manufacturers, including the labor, governmental and regulatory environment that applies to them), your distribution centers, mass distributors and individual resellers (including their return practices), your outwards facing supply chain, various financial institutions, and diverse set of information providers and consolidators to name a few.

    • The scope of this phase is rather extensive and its benefits are undeniable. You will deliver all the business use cases in the captured value category in this stage. Benefits realized in this stage are more closely and easily relatable to economics outcome and are more likely to generate immediate attention.

    • Most of the initiatives in this phase will take longer to complete, especially those requiring interaction with one or more parties involved in your value chain providing or receiving data and insights. In this phase, you will acquire additional data sources, will likely be involved in more technology integration efforts while developing predictive models that will support your business units make more informed decisions, understand and analyze potential challenges to the business before they happen, and take mitigatory actions for farther time horizons. Organizational change management and training will remain an important success factor in this stage as they were in the former one.

    • Depending on the nature of your business, this stage of your roadmap could prove the last stage of your journey.

    • Depending on the technical prowess of your organization, the nature of partnerships with various constituents in your business community, you might decide to deliver some or most of the data commercialization business use cases you have identified in this stage.

    • Depending on the scope and breadth of your initiative, you will likely consider participating in an insights marketplace (often known as data market place) mostly as a subscriber, potentially as a provider.

    • Should you decide to adopt a provider role, keep in mind that selling raw data is often the lowest form of value realization and mostly leads to short term subscribers. Explore avenue to generate and offer insights based on your data as opposed to offering the data in a raw format. Majority of the organizations across industries and sectors identify themselves at varying level in this stage of maturity.

Excel

  • Ecosystem data and insights business value — The final stage of your roadmap recognizes that several external factors, including other businesses likely to be active in different industries and sectors, will regularly influence your reach and business outcomes, could disrupt different aspects of your value chain, and affect your customers and their preferences. In this stage, you will devise approaches to learn from companies that share similarities with your business (for example, market to similar segments of the market, offer complimentary products or services) about experiences they had in launching a product or with an initiative in a segment or geography, or about the manner in which factors in the external environment impacted them and their performance.

    • You will more actively rely on your data and insights channels, develop more informed predictive models based on richer data sets likely wider in variety, and far more actively participate in data and insights marketplaces, likely as a subscriber more than improving your standing as a provider (selling data and insights is a by-product as opposed to core to your business). Your data ingestion practices are advanced and afford you the ability to update your business suggestion while the events and associated data are still fresh and relevant.

    • A somewhat commonly practiced example of an ecosystem level data and insight business value is when companies negotiate the rights to maintain “data residue” from services they provide to their customers. They in turn license this data, often augmented by other data residues or the insight they have generated to third parties.

    • This stage of your roadmap will depend on the maturity of additional disciplines in your internal processes (such as contract and partner management) while it will generate extended benefits to your data monetization (both implied return and captured value) business use cases through models benefiting from improved learning from enhanced data sets.

    • Very few companies will advance to this stage of maturity. Achieving an Advanced status from the perspective of generating business value through data and insights is the proper level of maturity for most businesses.