​This blog post describes an ecosystemOS data pipe development that has been validated with different startup ecosystems coming from Europe, North America and Asia. Written by the Startup Commons Team.

​Startup events are at the heart of any ecosystem but often times the view of an ecosystem event schedule looks like as follows:

​This picture is quite usual in many ecosystems due to lack of visible startup events data to be shared that helps bring more coordination and collaboration among ecosystem actors. It really doesn’t help to schedule multiple events at the same time and  target a similar audience. 

It would bring more efficiency if we could put all of the tech & business events data out there into a single platform so that events data can be accessed globally by different users and along with that it can also be transferred to other websites, blogs, online communities on user demand.

​Startup Commons Team is working towards Aggregating and Redistributing Startup Events Data globally and automatically. We will create  a standardized data model (GraphQL Schema) for structuring data on Startup Commons side and making that data available via GraphQL API endpoint. Some of the attributes we are collecting are Event name, Event Organisation, ID, category, Date, time, timezone, description, format or  website link.

​Initially, to get more startup events data visible, identify key attributes of startups events and an integrated API service to enable data transfer within and between ecosystems.

From Startup Commons point of view, we are looking at the quality of the startup events data model and the API performance. Regarding the data model, these are the main factors to consider:

• Clarity - attributes and information are clearly defined
• Accuracy - Information is true and correct
• Completeness - information is understandable
• Conciseness - same information is repeated
• Consistency - one piece of information doesn’t contradict another

Additionally, we could get static data from the model itself, data from usage of the data model, data from the comparison with similar data model.

From API perspective, these are some of the KPIs we are looking at tracking:

• Locations 
• Calls Made 
• Best Quality APIs 
• Fastest APIs
• Best Cloud (latency) 
• Cloud Services 
• APIs Tracked 
• Worst Quality APIs 
• Slowest APIs 
• Worst Cloud

In our tireless pursue to 'push the envelope' towards enabling ecosystem level digital connectivity, we have the opportunity to work with amazing new technologies like GraphQL, to unlock new flexibility than their predecessors like Restful API's.

Sometimes in the process we notice that something new doesn’t have full support available for something how we want to use such technology, where we end up solving what looks like a common problem for ourselves. While building our own solution in such common problem, our basic approach is to release it to the public domain.

Startup Commons, together with Prifina - a Silicon Valley based consumer and developer focused personal data platform and Grow VC Group is co-developing a GraphQL Data Model Editor, a complete application where business and technical users collide to create, document, edit, manage, publish, populate, test, verify and deploy ecosystem open standard data models.

Standard data model development is needed to enable digital connectivity that allows ecosystem builders, developers, operators and other key ecosystem actors to share ecosystem data with peer entities supporting entrepreneurs and startups along the journey following MyData principles.

Making data accessible to between ecosystem applications, allows collaborating actors to build a better ecosystem level user experience, where view into progress of building/supporting innovations is more accurate, and where ecosystem builders, developers and operators can compare, analyze, measure and orchestrate ecosystem development more effectively, to make more sound decisions.

​In our internal setup, this published SLS data model can be deployed directly into AWS and used to spin up the needed backend setup. It can also be used to make amendments into the existing infrastructure, which naturally requires clear versioning, deployment protocols, and access controls.

We see the need for further development in at least the following areas:

1. Automated versioning (think Google docs)
2. User and access management
3. Setup to spin up fake data en masse to populate and test data model (using e.. Faker https://github.com/marak/Faker.js/)
4. Create a hosted version

We are scaling up our operations and building our digital team in different positions. What we are building is incredibly ambitious and we want to get inspired by anyone passionate about data, innovation and entrepreneurship, and as such, we have an open-door policy for those that want to get to know us, to connect with others and to share knowledge. We, as serial entrepreneurs, know and accept this risk but we don't know any other way to reach our vision for digital transformation applied to scaling innovation, entrepreneurship and ecosystem development. A tool to empower ecosystem builders with cutting edge capabilities to unlock ecosystem data and put data to work in favor of all ecosystem actors, bringing dramatic improvements in ecosystem orchestration. This is ecosystemOS.

Growth Academy is the worldwide leader in open and globally neutral knowledge for innovation entrepreneurship ecosystem actors and users. We focus on building a 360 degree understanding about startups and the journey through various perspectives and connecting all relevant startup related topics along with links to further keywords, resources and tools, for further self learnings and for taking theory to action.

Today, we are celebrating Startup Commons' involvement in MyData Global’s recently published paper, Understanding MyData Operators. It is a foundational step on a roadmap towards a human-centric personal data infrastructure that involved 34 experts and was supported by 48 personal data operators in 15 countries. The MyData vision published in 2015, describes operators as actors in personal data ecosystems, that provide infrastructure for human-centric personal data management and governance.