Increasingly, dominating the digital space – as well as the physical one – is now a priority for ambitious brands. Extending the ecosystem, which relies on the controlled but free flow of data within it, is about enlarging the digital corporate footprint.
Those who dominate will take the compute as close as possible to the customer (or eventual target); pushing data and processes to the edge, speeding service delivery, and, in the process, denying competitors digital oxygen. The ecosystem is essentially a collaborative space – with suppliers, stakeholders, customers, distributors, partners etc. all having extended value chains – however in the private sector, ownership of those relationships and the data that comes with them will be key to corporate value (data as an asset) and to crimping the style of competitors.
Implementing the ecosystem does have challenges. Frequently, organisations attempt this without the correct infrastructure, data ontology, or a reluctance to move from applications. The need to manage and govern the ecosystem data, so that collaboration does not become security breach, requires a data-centric data strategy – putting the data first. The next consideration is how to manage the data at a granular level, so that access privileges and audit trail are in place – the answer will lie in platforming and frameworks. The target is to achieve a governance, risk and security shield which permits the flow of data in and out of the ecosystem – to those authorised and to authorised destinations – but at the same time, provides data and privacy managers with the control they need to observe compliance and protect the assets. Given the potential for multi-lateral data input, the data must be curated over its lifecycle, to ensure sustainable data quality and integrity.
Any organisation wanting to share services or data has to consider these issues and how it will ‘wrap’ the extended value chain in order to achieve the desired levels of collaboration without creating security vulnerabilities.
UPDATE: And the value chain works both ways. With computing at the edge, the latest AI and ML trends are to also capture learning from end user devices to feed the process of iteratively improving responsiveness and efficiency centrally.