A few weeks ago Sitra published the Data Ecosystem Handbook. Since the great discussion in the launch event, many of us have continued the discussion on LinkedIn inspired by the Data Ecosystems Part 1 blogpost that looks into the foundation of any ecosystem: Purpose and objectives. To have them in place, there typically is an incubation period during which the partners discover a problem, discuss and build joint understanding of it. During those discussions, the emerging ecosystem often moves from ambiguous collaboration to more defined structure. Or sometimes dissolves as the discussions slowly die when the partners interest focuses on other things.

This blogpost takes a closer look at the essential roles and leadership principles needed to establish a successful (data) ecosystem and ensure collaboration and commitment in the long term.

Partner Roles

Typically, ecosystem starts emerging when a group of partners discover a problem that is worthy of solving. In other words, there is an identified customer, someone who owns the problem, and involved partners that have potential gains for solving the problem. In the ecosystem handbook we named the key success factors key organizations, key people and key project. And in fact they do apply in the data ecosystem as well, with the following notes:

1. Initiator: Every group of partners needs an “initiator” someone who is willing and able to keep asking the right questions and get others onboard.
2. Orchestrator: Once the ecosystem partners have agreed upon the purpose and objectives and started collaborating, there is typically need for an orchestrator. As partners may have conflicting interests, orchestrator is a key role for building trust and mediating potential conflict. That of course in addition to keeping the ecosystem going.
3. Data operator: As the ecocystem partners are either data producers or data users – or in some cases both, it is important to agree upon the data operator role. Like orchestrator, the data operator can be from within the ecosystem or a neutral external party. Often, when it comes to integrating data – new capabilities are needed, and hence, it might be easier to build the data ecosystem with an external data operator with proven capabilities.

Leadership: Fact -based, fair and transparent

Getting started with collaboration is easy. But keeping the collaboration going is more difficult. This is because, the steps needed to discover a problem that is worthy of solving, having the key organizations and people and formulated purpose and objectives (i.e. the key project) take time. This is in our view, because many organizations are very keen to build data driven businesses as long as they own the data. However, to solve meaningful problems, owning all the data needed to solve complex and systemic problems is rarely possible.

So what’s the problem? Looking into the McKinsey’s collaboration barometer, there are a few things worthy of mentioning: Raising management capabilities and insight is one of them. But how to? To overcome fears related to sharing business critical data with potential competition, business model understanding and impact simulation are needed. Overcoming these fears can be done by supporting the “initiator” with fact -based business model understanding. Sounds simple? Yes. However, in real life it is often anything but.

While we may not have all the answers, there are a few best practices that are worthy of looking into. Firstly, business model definition to identify the roles of different partners and the data that is needed to develop new solutions. Secondly, simulating the business model to understand business potential for all partners. And thirdly, speeding up the development by ensuring that the orchestrator role and data operator role are identified and filled when needed and establishing the ecosystem operating model for ensuring fair and transparent leadership.

If you want to know more about the best practices, please contact us!