Our mission is that our clients can act data-driven in their daily operations. For us at Seita, this means that our clients, when dealing with data, are empowered to:

  1. Discover new customer value continuously
  2. Understand the risks they take
  3. Own the process

“Acting data-driven” – that is a grand theme. Value can be created from data in so many ways. To have a consistent method of evaluating identified opportunities, we developed our¬†Data Opportunity Canvas.

It consists of six segments addressing technological and economic feasibility of an idea how to create value from data. With the data opportunity canvas, you’ll be able to compare across different opportunities to make an informed decision on which ones are most interesting for your business to act on.

Each filled-out canvas should clearly define what technical knowledge and services are needed to create value from data. It should also define arguments to convince decision-makers about the expected economics when acting on this opportunity. We’ll explain the goals of each segment in more detail below.

The canvas

Six perspectives, six goals

Service stakeholders

How does the service involve stakeholders?

The goal here is to define the atomic service: a service representing the lowest level of detail, that creates value for a primary beneficiary. Other services may be derived from the atomic service, creating value for secondary beneficiaries.

As a starting point, we advise to identify what valuable decisions relevant stakeholders could take differently, and then working back to the services that these stakeholders would need to support those decisions.

Data specs

What is needed to transform data into services?

The goal here is to find sufficient requirements for a technological solution that supports the desired services.

We advise to focus on requirements for supporting the atomic service, and to use separate canvasses for derived services.

System view

A useful high-level perspective.

The goal here is to show how the service connects the primary beneficiary to the data.

We advise to start with a very rough sketch [data -> system -> service to beneficiary], and use iterations to describe system components with more precision until it is at least clear which skills are needed to build and maintain the system.

Benefits

Benefits to each relevant stakeholder.

The goal here is to plan for incremental success, because different benefits are usually realised at different stages. Plots are a great way to force yourself to think about the dynamics that underlie stakeholder value.

We advise to start by defining measurements of success for each stakeholder, and then working towards expressing value in a unified form (for example, revenue accumulated or time saved).

Costs

Investments to build and maintain services.

The goal here is to look further than just the costs of developing your technological solution, and to prepare for your mode of operation. For many data opportunities, non-personnel costs are negligible.

We advise to separate personnel costs based on their role or skill required (one person may be qualified to take on multiple roles). Required skills and roles can be based on what you wrote in data specs (algorithms and usage) and system view.

Risks

What threatens success the most?

The goal here is to express uncertainty, which should help convince decision-makers about whether to act on the data opportunity. Some examples of lock-in with tech suppliers: use of an obscure algorithm or lack of export features.

We advise to start by checking any use of question marks in other sections, and listing those issues here. Sometimes big risks can be resolved simply by checking some assumptions.

Get started

We are working with this model in our Data Opportunity Workshop.