The engine of the new data economy

Synopsis

• Organisations need to cooperate globally and must form a data economy

• The Data economy is rooted in transforming data into value within and outside the enterprise

• Value transfer is not only about data sharing but also sharing information and insights, plus action orchestration

• Technologies like IOTICS make it possible to effectively create and transfer value across boundaries and at a global scale

Introduction

In a resource-constrained world, organisations need to digitally cooperate to increase their societal impact and transform their businesses into sustainable operations.

For digital cooperation to happen, data coming from within and outside the organisation must be effectively and efficiently transformed into valuable information that generates insights that, in turn, have an impact on the environment and contribute to the prosperity of the organisation.

In practice, organisations must be part of a data economy based on ecosystems of parties cooperating on shared goals whilst not losing sight of their respective bottom lines.

The formation of such ecosystems and the ability to benefit from them must harness new technologies that are, like IOTICS, natively designed with security and interoperability in mind. Organisations must also foster cultural changes where actions are exclusively driven by data and information sharing becomes the accepted norm.

The mechanics of the new data economy

A data economy is a digital ecosystem where data is retrieved, shared and transformed into useful and valuable insights, producing an impact in the real world. To scale globally, address the challenges of the twenty-first century, and offer sufficient assurances to the participants, such an economy needs to adopt a new set of principles including decentralisation, accountability, and transparency.

Additionally, organisations that wish to participate in a data economy in order to maximise their social impact and effectively achieve their financial goals must adopt technologies, strategies, and cultural changes that maximise the conversion of raw data and information into value, where value is broadly defined as any result that has an impact on the real world.

The data value chain is a straightforward yet useful representation of this transformation process. It models the processes that turn “raw” data into “value.” It’s comparable to a factory assembly line where data passes through several stations before being transformed into a final product whose adoption impacts the actual world. Data generates more value the quicker it moves through the chain.

Image from Open Data Watch – CC BY 4.0 International license-free use with Attribution

In a healthy and valuable data economy, value flows bottom-up (from where data is produced to where impact is performed) and across enterprises, much like how global supply chains work in the real world.

It is worth noting that, while the first two stages of the data-value chain, collection and publication, are driven by technical investments, the last two, uptake and impact, are influenced by human factors such as data literacy and information as a second language, as well as a shift from “data ownership” and “share none unless” to “share everything except,” as identified by this Gartner analysis.

Beyond data sharing

In recent years, many companies have upgraded their capabilities, and new start-ups have emerged to provide “data sharing” capabilities. Nonetheless, readily available solutions only provide centralised access to raw data, which is frequently out of context and cannot accurately and unambiguously determine its meaning. While this approach may be appropriate for retrofitting existing “big data” technology, due to its inherent limitations, it is unlikely to support any data economy at scale.

The currently accepted approach to data sharing is well described by Snowflake’s definition:

At a basic level, data sharing is the ability to distribute the same sets of data resources with multiple users or applications while maintaining data fidelity across all entities consuming the data.

These are the issues with this definition:

• No semantics: data isn’t always self-describing in terms of its inherent meaning.

• Sharing is by copying: this is a common method of sharing, but it may not be suitable for everybody, especially for those cases where data carries information that needs to be protected.

• Lack of quality: data sets are seen as “blobs,” possibly out of context, with no way to validate their quality. And the onus is on the consumer to verify it.

Value can be transferred if not only data but also information and insights are securely and effectively made available in the ecosystem. In other words, data must be in context for information to be available, and information must be processed in order to derive insights. Furthermore, having a mechanism that allows the orchestration of actions across ecosystem participants, using the same mechanics used for data, information and insights sharing, will further support effective cooperation.

Ultimately, in a data economy, efficient and effective cooperation can be achieved by transferring value within and across participants by

• Interoperating by sharing meaningful and self-descriptive data.

• Enabling modelling of data, information, and insights, and delivering them promptly: not too early or too late,

• Minimising the cost of building and supporting the infrastructure that enables the sharing.

Accelerating the transformation of data into value

In order for organizations to collaborate, technology must enable them to safely communicate and exchange data, information, and insights relevant to their goals and to orchestrate actions derived from insights back into the real world: the efficient and secure flow of value within and across the boundaries of the enterprise strengthens collaboration and speeds goal achievement.

No matter how data is made available for use (digital twins, data products, etc.), it is critical to provide a semantic layer that makes the data self-describing and interoperable.

Moreover, in order to accelerate value creation, information and insights can also be modelled as aggregated data or events that represent changes in the real world.

The data-action loop can also be accelerated by orchestrating actions and dispatching them to the real world using the same secure mechanisms adopted for data sharing.

Conclusions

Organisations wanting to achieve their bottom-line and make an impact in the real world must digitally cooperate and form a data economy based on decentralisation, transparency and accountability where value flows within and outside the organisations’ respective boundaries.

The data-value chain is an accurate model of how value can be created from data as it takes into account both technical and human factors.

IOTICS’s technology provides the infrastructure and tools to accelerate the publication of data and its uptake. It gives businesses a competitive edge in their commercial activity by connecting them together, enabling them to cooperate and form efficient and effective data economies, even in previously impossible or impractical situations.

IOTICS adoption may be gradual and driven by use cases. Data, information, insights and actions can be modelled to represent real business assets that are discoverable, accessible, interoperable and reusable and made available via a decentralised semantic layer that overlays existing sources and systems.