Three simple tools that help you to make use of big data
At ServDes 2016 Copenhagen, Hardy and Klaus took part in several high-level talks. For Hardy, one of the most interesting was about the data-need-fit: “Towards data-driven business model innovation” by Katrin Mathis and Felix Köbler. In this blogpost he summarizes the main learnings we can derive from it.
With this slide as a starting point the speakers already had my full attention.
Many companies possess rich data on their customers’ preferences and buying habits. This data can help them to provide their customers with better services. Thus, it has the potential of bringing companies one step ahead of the competition.
But how can companies achieve that? The following tools can help to handle big data in a more structured way. (At the end of the article you’ll get information about where to get the templates)
1. The Data Canvas Map
With a data canvas map you can structure your data. By differentiating between internal vs. external and continuous vs. rotational data you can illustrate which data is of high value for you and where can you get it from.
2. The Business Model Canvas
Based on this first analysis you can use the well-known Business Model Canvas. The model helps you to develop your existing or future service offerings. In the context of big data it can show which kind of data is – or could become – key resources.
3. The Stakeholder Map
Stakeholder maps are great to analyze the current ecosystem and helps you to identify key partners which influence the delivery of improved services to your customers.
Based on your knowledge about your key resources (the big data available) and your key partners who help you to retrieve this data, you can identify potential new customer segments. You can also identify new value propositions for new services or enhance your existing services.
So when do you reach your data-need-fit?
You have reached Data-Need-Fit when your customers’ needs get covered by the data you have at hand.
However, these tools only make sense if you already dispose of valuable data. If you during the process find out that the data you’ve got does not reflect the knowledge you search for, do not try to squeeze it in order to get information you actually do not need.
Better: do some research.
Assumptions are a starting point, but in the long term research must be the base of insights. Research solutions can help to collect customer feedback on your service. Take the time to analyze the data in detail. That gives you a detailed view of what your customers are currently missing and you might have an hint on kind of data you need to solve your customers needs. Thus it can provide you with insights in potential new offerings.