Data Monetization Canvas

Why to use?

Design an analytical solution for a data/AI product applicable to one or more use cases.

When to use?

When you have an idea or a concept for a use(r) case related to a data/AI product and need to design an analytical solution. This process helps derive the technical, organizational, and personnel requirements needed to develop the product.

How to use?

I. Preparation

1. Fill the canvas header:

a) Label Focus on in the canvas header with a white sticky note with the name of the data / AI product or use case idea. You can copy the Focus on sticky note from the Analytics & AI Use Case canvas you were previously working on. Or choose an use case (idea) for example from the Business Model / Case, Analytics & AI Maturity or Priority Matrix canvas.

b) Footer: Add a legend with sticky notes in the corresponding color:

• Green sticky notes: "Existing element"

• Yellow sticky notes: "Planned element"

• Red sticky notes: "Missing element"

• White sticky notes: "Question / Assumption / Insight / Decision / Task"

II. Data / AI Product Design

Begin by adding sticky notes in red, yellow, or green to represent missing, planned, or existing elements respectively to the following boxes to sketch the data flow and analysis steps. Note that for a new data product most elements will be missing:

② Start with the end on the right side of the canvas (see References for an easy start):

a) Data Value: Information value should be measured in terms of business performance metrics (e.g. key performance indicators, KPI). Place corresponding sticky notes in this box. Also, provide notes on information quality—such as model performance metrics—and on model features, which serve as indicators of sustainable, explainable, responsible, and trustworthy AI, as well as data quality indicators.

b) Data Products: Detail the intended outputs of your data / AI product with sticky notes, focusing particularly on the desired information, which includes specific questions the product aims to answer. In cases where the information is not calculated directly from the data, but is predicted / estimated using machine learning, for example, users expect an explanation of how the information is obtained (cp. "Explainable AI").

③ Data Analytics: Think about different analytical approaches to get the desired information and sketch the data preparation, analysis, and visualization steps in this box, using red, yellow and green sticky notes. Connect these notes (of all boxes) with horizontal arrows to illustrate the flow of data and information.

Evaluate the merits and limitations of various algorithms and methods to ensure they align with the required standards for information quality and model features, such as explainability (e.g.: explainable "white box" vs. opaque "black box" models). Conclude by selecting the most promising solution.

④ Data Sets: Identify and ideate essential and beneficial data sets as input to the analysis pipeline (in order to derive the desired information).

Place a sticky note for each identified data set in the Data Sets box and link these to the initial data preparation and analysis steps in the Data Analytics box.

References

A: From the Solutions box of the Analytics & AI Use Case canvas, copy all blue sticky notes that define information desired by the user, to the Data Products box. Change their color to red, yellow or green - depending on the availability of the information - and, if necessary, specify the desired information more precisely i.e., give a metric a distinctive name, define its unit, the temporal resolution, the granularity etc.

B: For the Data Value box, search for matching sticky notes in the Benefits and Objectives & Results boxes on the Analytics & AI Use Case canvas. The value of the data or information is measured by how it enables the user to achieve their specific objectives and results.

C: If you are uncertain about data availability, explore the data landscape using the Data Landscape canvas. Copy all sticky notes from the Data Sets box to the Sort in boxes of the Data Landscape canvas. Identify the sources for these data sets, ideate additional data sets, and position all of them within the data landscape quadrants. Assess their availability and accordingly update the color of the sticky notes on both the Data Landscape and the Data Monetization canvases: green → data asset, red → data gap, and yellow → data (set with) issues.

III. TOP Requirements Design

Next, derive the technological, organizational, and personnel requirements from the design of your data / AI product:

• Add green sticky notes for existing technological, organizational and personnel resources or capabilities.

• Add red sticky notes for gaps in your technological infrastructure, organizational structure or personnel structure.

• Add yellow sticky notes for technological, organizational or personnel changes and initiatives already planned, in progress or with issues.

⑤ Technology: Begin by assessing the technological infrastructure required for your project. Start from the left side of your canvas and proceed to the right, moving systematically beneath the corresponding boxes from Data Sets to Data Value. For each stage of your data's lifecycle—accessing, cleansing, integrating, storing, analyzing, and visualizing— consider which tools, platforms, and systems are essential.

In the Technology box, located beneath the upper boxes—Data Sets, Data Analytics, Data Products & Data Value—place (multiple) sticky notes for each stage of the data lifecycle. Each note should be labeled with the names of the technical products or categories essential for operating the processes at that specific stage. Arrange these notes directly below their respective stages to maintain a clear alignment.

To visually depict the technology stack within each stage, stack these sticky notes—or alternatively, use rectangular shapes—vertically to represent the layering of technologies. For example, a Python module might be positioned on top of the Python programming language, which in turn is based on a cloud computing platform. Connect these layered sticky notes with horizontal arrows to illustrate the flow of data and how each technological component integrates with the overall data handling processes.

⑥ Organization: For each technical component, identify the team, department, division, subsidiary/sister company, or external provider that "owns" and is responsible for its implementation and operations. In the Organization box, place sticky notes that represent these organizational units. These should be positioned below or near their corresponding technical components to clarify the dependence. Connect these sticky notes with horizontal arrows to illustrate the flow of communication, such as the transfer of requirements.

⑦ People: Lastly, identify the key personnel required to implement and operate the data/AI product and its technological infrastructure. Place sticky notes in the People box, positioned below the corresponding organizational unit. Label each sticky note with the individual’s role, function, title, or skill, and optionally, include the number of full-time equivalents (FTE) in brackets. Use arrows to map out the reporting lines and/or collaboration and communication connections between these personnel.

Tip: According to Conway's Law ("The structure of a system is determined by the communication patterns of the people who design it.") you should think about reorganizing before implementing the system. To enhance understanding, you might add vertical dotted lines to visualize it on the canvas:

• The dependencies between the data sets and analytics and the technological components,

• The responsibilities of the organizational units regarding the technological components,

• The affiliations linking the people to their respective organizational units.

References

D: Update the Analytics & AI Maturity canvas (blue boxes) with the technological, organizational and personnel capabilities to complete your analytics & AI roadmap.

E: If the implementation and operation of your data product(s) require fundamental changes to your business model, also update the Business Model canvas.