DIGITAL CONCEPTS
WITH GREATER VALUE.

DIGITAL CONCEPTS WITH GREATER VALUE.

Data Canvas

07.09.2015 in Business DesignEnglish  by Katrin Mathis
Data Canvas – Tool to use data for new business models

The buzzword “Big Data” summarizes the developments in the IT world coming from the fact that the amount of data is growing faster than the technology to process it. However, in spite of the rapidly growing amount of available data, in 2012, according to inter-trade organization BITKOM, only 4% of German companies used big data as the basis for new business models.

 

No wonder, the employees who work with data on a daily basis are normally not involved in developing new business models. In the same way, those employees, whose job it is to take care of developing new business models, do not normally have a comprehensive knowledge of the entire spectrum of available data.

 

To spark a structured discussion on the potential of data for new business models, I have, during the last few months, developed a new tool. Using trigger questions, the Data Canvas steers the discussion and makes it possible for all participants to get a clear overview of the available data and its potential for new business models.

Prepare the Data Canvas

Invite representatives from different departments to a workshop. Three to nine participants is normally a good number for a lively, yet structured discussion. One of the participants should take on the role of a moderator. Print the Data Canvas template or draw it on a large piece of paper, for instance, A1 or A0. Hang the template on the wall so that all participants will be able to work on it equally well. Place sticky notes in different shapes and colors and thick markers ready.

Some advance preparation will be helpful. Ask the different departments which data they have, and which external data sources they are aware of. Search the internet to get a rough idea of what public data is available for your field without cost, and what data is available that must be paid for. Because there is an enormous amount of data out there, it helps to establish criteria in advance. Is there a special customer group that you wish to reach? Does the data need to be in a special format? Is there a budget for getting access to additional data sources?

Fill out the Data Canvas

You can of course fill out the Data Canvas alone, but it is more insightful to do it as a team. If you have participants from different departments, it will be possible to draw on many data sources, and to consider as many perspectives as possible.

 

Use one sticky note per data source. Write an informative name for the data source on it. With an additional hashtag, for instance #traffic, you can note the thematic reference of the data source. Sticky notes in different shapes and colors are well-suited for adding additional information. Use round sticky notes for unstructured data and rectangular ones for structured data. Use green sticky notes for dependable data sources whose quality has been checked by a reliable authority, yellow notes for less dependable data sources and red ones for data sources of doubtful quality.

 

Note additional information on the margins of the sticky notes, for example:

  • A €-sign or the exact amount for fee-based data sources.
  • A $-sign for data sources with legal or moral restrictions.
  • A U for unique data sources that are difficult for competitors to access.
  • An R for data sources with regional relevance.

These and other conceptual types of information can be explained on the right margin of the Data Canvas.

 

Place the sticky notes in one of the four boxes according to who owns the data and how often these are updated.

 

Data Canvas – available data in the city of Freiburg
Example for a filled Data Canvas at the Open Data Hackathon Freiburg

Evaluate the Data Canvas

Don’t use too much time on filling out the first version of the canvas. One hour is enough for the first run-through. Follow-up with a discussion on which new insights you have achieved:

  • How are the data sources distributed among the four boxes? Where are main points of focus and which boxes have only little data?
  • Which additional data sources would allow you to strengthen your strong areas and fill gaps?
  • For which themes do you have a lot of data or especially valuable data?
  • Which data can you utilize with minimal investment? Which data is expensive to collect or to improve?
  • Which data sources can be combined?

 

Continually updated data tends to have the greatest potential for new business models. Products and services that are based solely on periodic data often place limits on business models. Customers are presumably only ready to pay money for offers based on periodic data at the time of the recurring update. These offers must, therefore, have wide-ranging targets. Continually updated data, on the other hand, opens more possibilities for a continual monetization, also in the case of smaller, more specialized market segments. External data has the disadvantage that it, at some point, may no longer be available. Additionally, competitors will normally be able to access the same external data sources as you can and will be able to easily copy your business model. Therefore, you should combine external data with internal data, and periodic data with continually updated data, thereby increasing the potential of the individual data types.

 

Continue to work with the Canvas. Add additional data sources or remove them. Do more versions of the Data Canvas with a higher degree of detail or other dimensions.

Conclusion

The Data Canvas is a proposal for a method of structuring and documenting available data sources. There is no right or wrong in the concept. Modifications are explicitly allowed. Only one request: When you use the Data Canvas, please share examples and ideas with me and with others.

 

If you still have not found a concrete idea for a product or a service on the basis of your data at this point, then you have done everything right up until now. With a better feeling for the available data, you can identify which user needs can be supported by your data. In the next post, I will explain the process of identifying such a Data-Need Fit.


back

Katrin Mathis, UX Konzepterin und Service Designerin aus Freiburg

Katrin Mathis
MBA in Service Innovation & Design und BSc in OnlineMedien berät seit über 10 Jahren Unternehmen, die digitale Transformation zum Nutzen ihrer Kunden einzusetzen.

 

Treffen Sie mich persönlich