Why would I need it?
Ask yourself, do you want to sell barrels of oil or gasoline? Which is more usable and valuable for the consumers? Which has bigger profit? If you don’t like the oil comparison (for a good reason), how about milk products then? Raw milk or Swiss cheese? Which is more profitable? Or which carries more value for consumer, wood or timber? And if you build a custom made furniture from wood, the value is even bigger. The point is that unrefined rawmaterial has lower value than refined product.
Data is no different. You need to productize and refine it before sales.
It does not matter if you use your data only internally to refine other services. You need to package data to make it reusable across your oganization. The later you decide to productize data, the more costly it will be. With productized data you can gain results faster due to speed of reuse capability.
You need to productize and refine it for internal use to gain maximum speed.
Is it for us?
Typical users are companies which collect large amount of data from surrounding environment, customers, products, events and devices to enable data driven decision making and improvements as well as innovation.
Data product toolkit™
Data Product Toolkit™ consists of four canvases:
Data Product Value Propositions
Data Product Canvas.
Data Product Lifecycle canvas
Data Asset Evaluation canvas
Canvases are available as Miro boards, which can be filled in cooperatively online in a workshop. Canvases are available as PDF files too.
Data Product Toolkit™ training material
During 2020 we will publish online learning material containing detailed video format instructions how to use canvases efficiently. Material will contain 2-3 example cases too.
BETA program is open!
We are looking for interested partners to test Data Product Toolkit™ more thoroughly with real use cases. Product is close to MVP and it's time to battle test it! Get onboard and learn how to monetize your data!
carve profits from your data
Before you take a deep dive
Data Product Toolkit™ is intended for business purposes. It has been built to enable fast Data Product development. Here are the principles which are behind the methodology:
You should iterate. You can craft and iterate Data Product design until all parts work seamlessly together. It has adopted lean thinking from agile methodology.
Design First. It has adopted design-driven approach from design thinking. Both approaches minimize waste. Jumping directly to implementation often causes waste. Having unified behavior is also crucial when your Data Product portfolio starts to grow.
Access to data product. Easy Data Product consumption is as important as data product itself. Thus we have included accessing data in the model as well. Data Product Toolkit combines productized APIs and data, which both are needed in the data economy.
Business Objective. Why Product should exist. Every data product is unique and is built for a purpose.