This is the sixth article in our series on Data Strategy Design with Datentreiber method. In this series we have explained what smart goals are and why they are the basis for a good data strategy. In addition, we have presented one canvas per article:
• Customer Touchpoints Canvas
• Analytics Maturity Canvas
• Data Strategy Canvas
• Data Landscape Canvas
The canvas were developed by Datentreiber and are available for free on our website under a Creative Commons license: you may use and modify the canvas as long as you cite Datentreiber in particular as the source.
The aim of this article is to give you an overview of the Data Strategy Designkit and, in analogy to ‘There is an app for everything’, the Data Strategy Design says: ‘There is a canvas for that’!
The Data Strategy Design canvas helps you to correctly position the different canvas of the designkit and to use them accordingly. This means: keeping an overview of which canvas is used for what and how you can combine them.
Data Strategy Design: Data, Strategy & Design
A data strategy must be feasible, both from a technical and a financial point of view. It must also generate real benefits. Only then it is also desired.
Accordingly, a data strategy focuses on the following:
• The analytical and technical side, i.e. the data and its processing
• Economic and financial aspects, i.e. especially the integration into the business model
• Professional and personal needs, i.e. the benefit for the users of the data
The Data Management Canvas for example helps you to derive measures to improve the availability and quality of your data. Looking at economic and financial aspects, the Value Chain Canvas enables you to analyze business processes or specific sub-processes. The Stakeholder Analysis Canvas shows transparently which roles and persons are involved in an analytical solution or project.
The Data Strategy Designkit Canvas also subdivides all other canvas under the three aspects just mentioned.
This differentiation makes it easier to separate the technical aspects from the other aspects. It thus gives you clarity about the objectives when using the individual tools.
Consider All Aspects
Successful innovation happens in the sweet spot of feasibility, viability and desirability. These are based on the following definitions:
• Feasibility: the technical implementation is possible.
• Viability: the solution makes economic sense.
• Desirability: the solution is actually desired by the designated users.
Applied to the tools of the Data Strategy Designkit, this means, for example, that with the Data Strategy Canvas you can concretize a use case to such an extent that you can identify critical assumptions at an early stage and check them systematically.
With the Strategy Pyramid e.g. you can make your corporate strategy more tangible. Derived from the vision as well as the corresponding mission, you define basic values and guidelines on which your entrepreneurial actions should be based. Finally, you set milestones that allow you to determine intermediate steps to achieve your vision.
In the area of desirability, you can for example use the Value Curve Canvas to determine critical product or service characteristics of a value proposition. This helps you to differentiate your offering from the competition and to reduce or eliminate irrelevant product parts and to increase or recreate relevant factors.
The illustratively mentioned canvas as well as all other canvas are grouped accordingly on the Data Strategy Design canvas. In this way, you can select the right tool for your starting position and objectives from the toolbox.
More information about the purpose of these visual collaboration tools can be found, as mentioned at the beginning, on our website. There is a tutorial available for each canvas. In addition, the Datengipfel seminars offered by Datentreiber also address most of the canvas in detail, and the participants’ concrete questions and use cases are discussed accordingly.
Work Iteratively
An essential point in successfully creating and developing a data strategy is the iterative approach. This applies not only in the design phase but continues until the operationalization of an analytical solution or data product.
In the context of working with the canvas, this means, among other things, a constant change of the tool according to new or changed initial or knowledge situation. In concrete terms, information is transferred from one elaborated canvas to the next in order to take the next step. However, this is not a linear process. It can be quite possible to jump back and forth between one and the same canvas several times in a row.
This is the case, for example, when you take a closer look at a use case with the Data Landscape Canvas and encounter an impassable obstacle (e.g. missing data). You would then return to the Data Strategy Canvas with the next best use case. The connections between the canvas itself as well as the operations that form the connection are also illustrated on the Data Strategy Design canvas.
Conclusion and Outlook
Based on the Data Strategy Designkit, this sixth article in our series of articles has once again given you a complete overview of the procedure and objectives of a data strategy design. With this and the previous articles, you have already created a good basis for immersing yourself in data strategy design.
As mentioned at the beginning, this is not the end of the story. The method of data strategy design and the canvas toolbox are constantly evolving. In addition to customer feedback from numerous workshops and trainings, new ideas are constantly being developed which help to advance the methodology. Accordingly, we are pleased to be able to present the following three new canvas in three upcoming articles:
To conclude, the next article gives you a complete overview of the data strategy designkit. In particular, it focuses the interrelationships of the individual canvas once again. Even though data strategy design is not a linear, but a very iterative process, there are processes that are typical for this, depending on the type of objective of the use case (for example, data-driven marketing).
The following other articles have been published in this series:
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Note: The author of this article is our guest author Martin Raffeiner, managing director of Datenbotschafter Consulting.
Did we awake your interest in the development of data strategies? Then take a look at our Datengipfel Seminars Data Strategy & Culture for beginners and Data Design Thinking for advanced participants. Or contact us if you have any further questions.
