But when it comes to startup ecosystems, how are you able to get that primary measurable data from key activities in the startup ecosystem?
One of the key ways of collecting primary data is by relying on the typical support government - community - startups chain. When governments fund innovation and startup support functions - to be accountable for public money spending, they also need to set measures, targets and policies for startup support, funding and operating budgets for support organizations and request for metrics that matter, to be able to justify government spending, to be transparent, equal and accountable on decisions made.
However, governments don’t and often should not have overly defining relationship with companies at operative level (at least not in all stages and levels and in high volume), but should empower and give as much room for grassroots driven initiatives and functions as possible, while still remaining accountable for public money spending for ROI, in form of new innovations, job creation, economic growth etc. as well as keeping own region attractive and competitive.
With similar approach, startup supporting organizations (incubators, accelerators, coworking spaces, etc.) need to measure and report their supporting functions results in comparable format from their mentoring, office space, advisory sessions, key connections, etc. where they rely on public funding or support in some form. As this information is systematically collected via multiple different sources and touch points via same and different support organizations at various stages of startup progress, it gives huge amount and variety of data to be collected about relevant activities and startup journeys over the time. Sharing this general information and performance data, also directly benefits investors, startups and services organizations as well.
Last but not least, data can be collected from services like meetup, Facebook or LinkedIn groups, eventbrite, AngelList, F6S, etc. by connecting those via APIs or importing periodically from these other data sources.
The key factor here related to digital ecosystem infrastructure lies on the intelligent design of database to collect and connect different data collection methods, where primary way is to rely on "national chain of support" and then enrich that data collection from other existing services/sources, depending on what is available and what is most relevant initially and over time and while secondary data brings perspective about the dynamism or trends in the startups ecosystem, primary data brings the "why” and causality, so therefore, both are needed to get the big picture.
This data collection model clearly reflects the direction of digitising interactions among all user roles and organizations in the startup ecosystem to produce, collect and automate data. Trusted networks are needed and when it comes to startup ecosystems, you can create it with digital ecosystem infrastructure where each region defines their own operating model, rules etc. and where the information is collected through multiple touch points, rating systems, interaction with users, activities, services and organizations that require to keep your profile up to date to demonstrate your progress over time in order to get the services, resources and connections you need at every single stage.
In next blog post we will showcase one specific case to visualize collected data from the startup ecosystem. Meanwhile, do not hesitate to leave your comments below or you can also contact us.