Why a Data Ecosystem is essential for Enterprises to Operate, Innovate and Collaborate

Why a Data Ecosystem?

As Enterprises need to deliver faster results, in a trusted and sustainable manner, the role of the underlying Data becomes crucial. A key obstacle to this is all the different capabilities within a Data Architecture and how some Enterprises need to invest a lot of time to integrate the different elements of these capabilities, how its being done differently in different parts of the Organization and how as new Innovations and Technology advancements come together these capabilities continuously need to be refactored. All with good intent, but this is where it becomes essential to build a Data Platform (or better called a Data Ecosystem) which can be leveraged across the Enterprise, has complimentary yet flexibility in terms of capabilities and is setup to adapt as the Enterprise evolves over a long period of time.

The benefits of these can be compounding in nature. Compounded Agility and Trust whilst minimizing Risk. Let’s also not ignore the importance of this from a Data Literacy and Data Skills perspective and to motivate the Enterprise to learn, understand and adopt the Data Ecosystem (compared to time spent integrating, and relearning tools).

What are the Key Principles of a Data Ecosystem?

What are the Critical Components in a Data Ecosystem?

• Data Infrastructure – This being a foundational pillar as all the other capabilities sit on top of the Data Infrastructure (or better in plural). Enterprises are more and more moving towards a hybrid data infrastructure, leveraging on-premises, multiple clouds for different purposes. At the same time a critical aspect to consider in this component is Security and Policy Management to support Regulated Industries, Data Residency, and aspects such as GDPR/CCPA. But at the same time, it’s essential to scaling applications by onboarding and scaling them within this data infrastructure.

Critical Key Words for Data Infrastructure – Hybrid, Secure, Containerization, Scalability & Elasticity, Foundational Building Block

• Data Storage and Compute - This is where it's important to understand that we are no longer in the world where a single data, lake or data warehouse is sufficient. As your data infrastructure is evolving. There will be various data storage compute capabilities that you need to leverage based on the type of use case, the speed of the data, the type of pattern you're trying to apply.?At the same time common frameworks and formats are appearing to unify the storages (such as Apache Iceberg or Delta) and common compression formats (Parquet, Avro etc.). Its important for these Data Storage to work across Hybrid Data Infrastructures so if the Enterprises decide to move to a different Cloud Hyperscaler for instance, this doesn’t necessarily need to require a huge re-effort in Data Storage and Compute.

Critical Key Words for Data Storage & Compute – Speed and Performance, Different Storage and Compute Frameworks based on Usecase patterns, Common Frameworks, Cross-Hyperscaler

Holistic Data Management - This is where it is important to have an end to end, holistic yet flexible data management ecosystem which can work across a hybrid multi-cloud data infrastructure and leverage the power of the underlying data storage and compute without being too tied up in any one of these. That's where it's essential for the data management console to be centralized in a way it's designed, but really decentralized in the way it can be executed across a hybrid multi-cloud infrastructure. A clear example of this could be, let's say, the enterprise is looking at a snowflake or a databricks component for storage and compute. This is where the data management, capabilities, such as data quality, should be converted into snowflake native procedures or databricks native spark capabilities which can be used and run in the data ecosystem leveraging the capabilities that this provides.

Critical Key Words for Holistic Data Management – Holistic & End-End, Hybrid and Multi-Cloud, Leverage Data Storage and Compute, Unified Design yet De-centralized Execution

Data Governance and Data Products – It is important to empower the enterprise where the data management components can be supported with a strong data governance layer and a data products-based data sharing layer. A key aspect to support this is a strong underlying metadata foundation that links the business and enterprise concepts towards the underlying technology complexity and empower the non-technical data users can understand how to work with the data without really understanding the nuts and bolts of the underlying storage, compute, and infrastructure ecosystem. This needs to be scaled with a strong layer of automation, so that there's a lot of collaboration and recommendations to support your Data Ecosystem to be used in the right way, as well as a lot of manual tasks can be automated. So, in essence the Data Governance and Data Products layer needs to be very tightly integrated with the rest of the Data Management layer.

Critical Key Words for Data Governance & Data Products – Data Sharing, Business Layer, Metadata & Automation, Tight Integration with Data Management

Analytics and Operational Processes – This layer is the result to support analytics and operational processes and includes AI/ML, Self-Service Reporting, Operational Processes and Applications. This is where it's important that the data management, data governance capabilities together can offer the right trusted data products that the analytics or the operational users and systems work on. The analytics then uses this intelligence to work with the underlying Data Storage and Compute and Data Infrastructure layers to gather the right underlying datasets that are relevant.

Critical Key Words for Analytics & Operational Processes – Trusted Data Products, Leverage and Collaborate with Data Governance, Understand via underlying Data Management, Power and Scale with Data Storage & Compute and Data Infrastructure

Value Drivers for the Enterprise

1. Compounded Value – If the Data Ecosystem is designed and built well, as each new usecase comes up a lot of existing value in terms of identifying the existing Data Products, connecting the right people who manage the Data, Having the Right Trust on the data and easily able to prepare and combine the data for the usecase needs.
2. Reduced Risk & Increased Accountability – The Data Ecosystem provides set of integrated services and capabilities which ensures easy transparency, connecting different aspects of the Enterprise together. Focus on collaboration enables Business Units to contribute as they see the Extracted value for their Business Unit/Domain and the Enterprise as a whole.
3. Increased Agility – The whole framework of the Data Ecosystem is based on modularity and reuse. This enables the Enterprise to identify, leverage and automate providing increased agility. An example could be to automate data classifications therefore connecting different classified elements to metadata and data storage entities and attributes enabling easier/consolidated data quality & protection for instance.
4. Reduced Cost vs Gained Value – Costs can be reduced in several ways, firstly by consolidated set of technology capabilities reducing point solutions, the cost to integrate across solutions and the cost to manage and maintain all the solutions and skills across. The second step would be to leverage smart capabilities such as FinOps where for instance the Data Management layer can (based on workloads) run on the most cost-efficient Data Storage and Compute option based on use cases.

Summary

A Data Ecosystem offers a lot of value and flexibility especially if Enterprises and their leaders see the long-term strategy and vision to be data driven. At the same time the Data Ecosystem should not be static, components within the ecosystem need to continuously evolve as new business needs and technology innovations come into play. It is essential here to empower teams who see the Data Ecosystem and connect the dots rather than looking only at specific capabilities within the ecosystem and trying to fit solutions towards the same. This requires also often a huge change in the way of working and culture in the Enterprise but can then achieve a lot of benefits and enable Data Office teams to easily manage, maintain, scale, and measure the value of the Data Ecosystem across the Enterprise.

The views in the article represent my personal thoughts based on my experience working with Enterprises. Please feel free to share your thoughts and comments. Thank you for reading!