Cloud Journey — Part 12 | Ecosystem Digital Servitization using Data Spaces by aws

Chris Shayan
9 min readMar 21, 2024

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Cloud Journey Series:

Introduction

What is a Digital Ecosystem?

A digital ecosystem is a group of interconnected resources that can function as a unit. Digital ecosystems are made up of suppliers, customers, trading partners, applications, third-party data service providers and all respective technologies. Interoperability is the key to the ecosystem’s success.

As digital technologies revolutionize how we do business, companies increasingly compete through ecosystems in which they partner with a variety of other firms and organizations (same or various equity ownership structure). The relationships with these partners are determined by the data and operating resources they provide.

What is Digital Servitization?

Digital Servitization refers to the utilization of digital platform and modren technologies for the transformation whereby a company or an ecosystem shifts from a product-centric to a customer-centric business model.

Digital Ecosystem Operating Model Framework

Before we go throught how cloud can help on achieving the Ecosystem Digital Servitization, it is important to know the required operating model of a digital ecosystem. You need to have a complete Digital Ecosystem Operating Model Framework before using any technologies to enable you.

Firm-level enablers of ecosystem transformation in digital servitization are:

  1. Triggers, three main triggers for ecosystem transformation in digital servitization: synchronizing with fast-paced market change, novel customer demands and readiness, and filling technology and resource gaps.
  2. Culture, the existence of an organizational culture facilitating and supporting ecosystem transformation is a crucial factor that plays a fundamental and significant role in successful digital servitization.
  3. Business Model, The business model represents the structure of how a business operates to create, deliver, and capture value for growth and survival in today’s volatile marketplace. Business model elements evaluation and business model innovation are the most important factors shaping the role of business models in ecosystem transformation.
  4. Capabilities, it became evident that certain capabilities are needed to enable the ecosystem transformation in digital services. These capabilities can be divided into two main categories: digitalization capabilities and relational capabilities.

M. Kolagar et al.’s study is of benefit to senior managers who are driving digitalization and ecosystem partnership initiatives. They encourage stakeholders to place emphasis on firm-level enablers, such as culture change, business model, and capabilities development programs, as a way to create the foundations for ecosystem transformation. For example, getting ecosystem partners involved in pilot projects and seeking feedback from progressive customers are useful means to test innovative digital service business models. They also encourage ecosystem managers to search for partnerships outside their existing value chain. They would also highlight the importance of distinguishing between partnering strategies across different phases of ecosystem transformation.

How can you set up your partner ecosystem?

Now that you understand what a partner ecosystem is and isn’t, it’s time to go over how to make one happen.

The main benefit of a partnership is that it allows you to enter new markets and access more customers without having to do everything yourself. But as Gianvito Lanzolla and Constantinos C. Markides explain in Harvard Business Review, there are two factors that companies must consider when deciding who to partner with and how to partner with them in today’s digital world:

  1. The value of the data they’ll receive from the partner
  2. The partner’s ability to support scalability and provide operational support

Within these considerations, potential partners should be evaluated for their operating resources and capabilities. This measures a partner’s ability to help with scalability. Additionally, Lanzolla and Markides suggest considering potential partners as one of the following:

What is Data Value Chain (DVC)?

To better understand what a Data Value Chain (DVC) is and is useful for, let’s first comprehend where this term derives from. A Value Chain is a set of interlinked resources and processes that begins with the acquisition of raw materials and extends to the delivery of valuable products. A value chain is a set of activities that a firm operating in a specific industry performs in order to deliver a valuable product (i.e., good and/or service) to the end customer.

On the other hand, a Data Value Chain (DVC) is a mechanism that defines a set of repeatable processes to extract data’s value step by step throughout its entire lifecycle from raw data to veritable insights. DVC consists of four main steps:

  • Data generation: Capture and record data;
  • Data collection: Collect, validate and store data;
  • Data analysis: Process and analyze data to generate new potential insights;
  • Data exchange: Expose the data outputs to use, whether internally or externally with partners in ecosystem.

What are Data Spaces?

As relevance of data grows every year, the European Commission bet for the development of data spaces, envisioned as of strategic importance for the growth of the European data economy. The aim is to enable and stimulate the development of Data Value Chains, keeping sovereignty and trustworthiness under European premises and values. In a concise and easy to understand language, a Data Space is defined by Open DEI as “a decentralised infrastructure for trustworthy data sharing and exchange in data ecosystems, based on commonly agreed principles”.

Gaia-X defines it as a “a type of data relationship between trusted partners in an ecosystem who adhere to the same high standards and guidelines when it comes to data storage and sharing”. Furthermore and probably the most distinctive characteristic of a data space is that “data are not stored centrally, but rather at the source and are thus only transferred as necessary”. This decentralised nature allows actors to keep the sovereignty on data.

From a technical perspective, a data space can be understood as a collection of technical components facilitating a dynamic, secure and seamless flow of data/information between parties and domains. These components can be implemented in many different ways and deployed on different runtime frameworks (e.g., Kubernetes).

Finally, a key aspect of data spaces is the fact that they offer support for an interoperable IT environment for data sharing, allowing for data portability regardless of the platform on which it is hosted or consumed.

The Eclipse Dataspace Components (EDC) provide a framework for sovereign, inter-organizational data sharing. They implement the IDS Dataspace Protocol (DSP) as well as relevant protocols associated with GAIA-X. The EDC are designed in an extensible way in order to support alternative protocols and integrate in various ecosystems.

The Minimum Viable Dataspace (MVD) is a sample implementation of a dataspace that leverages the Eclipse Dataspace Components (EDC). The main purpose is to demonstrate the capabilities of the EDC, make dataspace concepts tangible based on a specific implementation, and to serve as a starting point to implement a custom dataspace.

The MVD allows developers and decision makers to gauge the current progress of the EDC and its capabilities to satisfy the functionality of a fully operational dataspace.

As a fully decentralized dataspace is hard to imagine, the MVD also serves the purpose of demonstrating how decentralization can be practically implemented.

AWS for data spaces

In the context of data spaces, AWS aims for a simple-to-use integration between the connecting technology required to participate in a data space and AWS services, enabling customers to share data in a standardized manner while providing them with the services and tools to make the most of their own data, as well as third-party data. Data spaces address the discovery and transfer of data assets based on agreements between the participants of a data exchange; the underlying AWS infrastructure enables participants to run the required data space connector in a secure, scalable, and reliable way, but also provides services to support consuming and analyzing the data after transfer. The role of AWS in data spaces is that of the trusted infrastructure and data services provider, contributing with the building blocks for customers to create, manage, and use data spaces.

There is further value in using AWS for the processing of the data shared through a data space. For example, you can use services like AWS Glue, Amazon Athena, and Amazon QuickSight to process and understand data. Amazon SageMaker enables machine learning (ML) capabilities and data exploration. Also, AWS makes it simple to use security services to keep the data encrypted, as well as provide fine-grained access control while constantly monitoring the activity of the exchange process. Customers can control their data and the access to it by using services including AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS) for data encryption key management, AWS Lake Formation to build secure data lakes, and AWS Control Tower for governance of multi-account environments.

Following Figure depicts a high-level reference architecture displaying the interaction of data space connectors across organizational boundaries and the usage of native AWS services for analytics and ML.

In this reference architecture, the data space connector is deployed on docker containers and may use different orchestration technologies like Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS) to deploy these containers on top of a serverless compute engine such as AWS Fargate. For data sources and data targets, in this case, Figure 3 shows a combination of Amazon S3 and Amazon Relational Database Service (Amazon RDS).

AWS analytics and ML services can provide additional value in processing and analyzing the data needed by the business. With AWS Glue DataBrew, you can extract, load, and transform (ETL) the data without coding skills. The processed data can be later be used to generate business intelligence dashboards using Amazon QuickSight. With Amazon SageMaker, you can run predictive analysis by training models on a larger set of combined data from the data space. By having access to more data with potentially additional variables and features, ML models can be more accurate and developed more quickly.

All these components can be run on serverless, Amazon Elastic Compute Cloud (Amazon EC2) on-demand, or spot compute infrastructure, which can offer virtually unlimited compute capacity. Plus, customers inherit comprehensive compliance controls; AWS supports 98 security standards and compliance certifications, more than any other offering, including PCI-DSS, HIPAA/HITECH, FedRAMP, GDPR, FIPS 140–2, and NIST 800–171, helping satisfy compliance requirements for virtually every regulatory agency around the globe.

For the interaction across all the services described that require network connectivity, the traffic can be made private using Amazon Virtual Private Cloud (Amazon VPC). Amazon VPC provides a logically isolated virtual network, with only the specific services that need to be published made accessible via Elastic Load Balancing.

You can also extend the data spaces and connect it to your own customer data platform, the following part shows an example of setting up a data platform for your customer strategy using aws.

Some other links:

Customer Data Platform on AWS

This Guidance shows how you can build a well-architected customer data platform with data from a broad range of sources, including contact centers, email, web and mobile entries, point of sale (POS) transactions, and customer relationship management (CRM) systems.

You can read more in detail in here.

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