The Data Asset Evaluation canvas is the 4th canvas in our Data Product Toolkit. It was created after we noticed that some of our clients are not starting their journey from the most obvious and typical starting point.
Four valuable outcomes
Before we take a deep dive into the canvas, let's have a look at the four valuable outcomes of using the canvas and related data product exploration approach.
Light-weight method to do data asset inventory from a business perspective
Identify what data we might lack to succeed in business objectives
Evaluate organizational capabilities - human and system - to enter the data economy
Identify low-hanging fruits - easiest assets to monetize
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Data Asset driven product exploration approach contains 6 steps.
First - identify data sources and do data asset evaluation. Fill in Data Asset Evaluation Canvas for each data source. It helps you do clarify what are your assets to build upon.
Second - Design data products with help of Data product canvases and use the data asset evaluation results as a basis for inspiration. Remember that you need to fill in just enough information so that you can go forward. Normally you draft multiple data products. Let your ideas flow while designing!
Third - Focus and select appropriate designs for further processing. Instead of generating more, your task is now to select the most promising designs from your pool. Data Product Canvas contains a method to do systematic selection to remove bias caused by you.
Fourth - Implement data product mockups with near-zero cost and timeframe. Your selected tools and data economy platform must enable almost costless and near-zero time to market processes to create data product mockups. In short, mockup creation must not contain any development work. It must be a no-code method understood by business people.
Fifth - Publish to appropriate markets or users. You need to have a channel for the data product mockups in order to market them efficiently to customers. You want products to be findable. Your data product channel must work in the self-service principle - customers must be able to take products in use without you giving them a helping hand, creating accounts, or guidance.
Sixth - Analyse the usage and feedback - then use the result as a seed for another round until the product is found for full development. You continue from step 2 and go back to the design board and iterate the product design further. Finally, after some rounds, you have found a data product that has been evaluated by customers and you can push it to production.