¿Cómo hacer que los cambios en la arquitectura de datos sean ágiles y flexibles?

To make data architecture changes agile and flexible, a transformative approach is to align them closely with business goals. Rather than treating data as a static entity, view it as a dynamic asset that evolves with the business. Implement a "Data-Driven Evolution" strategy where architecture changes are driven by real-time business needs. Embrace modular, microservices-based architecture, enabling rapid adjustments. Foster a culture of data agility, where teams collaborate seamlessly and adapt swiftly. Prioritize data democratization, allowing diverse stakeholders to access and shape data. This symbiotic relationship between data architecture and business objectives empowers organizations to thrive in an ever-changing landscape.

To make data architecture changes agile and flexible: Modularity: Design with modularity in mind to easily replace or add components. Standards: Establish data standards and metadata management to ensure consistency. Data Catalogs: Implement data catalogs for easy discovery and understanding of data assets. Scalability: Choose scalable data storage and processing technologies. Version Control: Use version control systems to track changes and revert if necessary. Agile Methodologies: Adopt agile development practices for iterative improvements. Feedback Loop: Continuously gather feedback from stakeholders for refinement.

In my experience, where organizations struggle with this first step is a failure to define tactical guidance for how to come into alignment with the business goals and strategies. It is not enough to say, "we are going to treat data as an asset." You have to say how you are going to do that and why the guidance has the claimed impacts. Now, back to the question on topic about how you make architecture flexible and how that relates to this first step. I think agility in this space is achieved when there is a willingness to challenge the "right now" and have those challenges be thoughtfully considered. I also think tech changes should challenge the business and that should also be accepted behavior. So, it all goes back to culture. :)

Building data and IT architectures, success sits in a strong beginning. I spent weeks of time educating my customers, business owners, architects and operational teams in how to get started. I used the working backwards methodology, define who the real customer(s) are who benefit now and in the future, understand their top 3 problems and use the information to write down a concept in a collaborative exercise with the customer, coming to an aspirational press release, writing down the FAQs at the same time, this deep dive needs to happen before setting effort on designing. We defined the architectural principals, including FAIR and agility. The solution has been the backbone of data and analytics for four years now and is still going strong.

Identify Business Goals with their objectives, priorities and desired outcomes -Translate Goals into Data by Define data needs that support the achievement of business goals - Assess Current Architecture by Evaluating infrastructure, architecture and technologies - Design Data Architecture by Developing a roadmap for improvements based on needs - Aligned with Business to Ensure data architecture and improve business processes -Communicate with business stakeholder throughout the design,implementation process, Measure and Adapt Benefits of Aligning Data Architecture with Business Goals to Improved Business Performance - Enhanced Utilization- Increased Business By aligning architecture with goals, you can create significant business value.

Define, design & build a POC ( Proof of Concept)/POT (Proof of Technology) as MVP with the approach of Rapid prototyping and testing to fail fast to work on the changes required. And this is possible only if aligned to iterative approach

Define (MVPs) by breaking down data architecture changes into smaller, deliverable units with clear business value - Rapid Prototyping & Testing by utilising rapid prototyping techniques to quickly build and test data architecture components - Agile Development and Delivery by Implementing agile development methodologies to iterate rapidly and deliver value incrementally -Prioritization and Backlog Management by Prioritize data architecture changes based on business impact, feasibility, and urgency -Communication and Collaboration progress and updates to stakeholders regularly to ensure transparency and buy-in -Continuous Learning and Improvement by Embrace a culture of continuous learning and improvement within the data architecture team.

Modular technologies without a common semantic view of the data that is extensible do run the risk of creating more complexity, not less. Define flexible schema model, independent of the technology used to apply it will then open the door to the benefits modularity can deliver on a functional level.

Using Modular and Scalable Technologies by Leverage Cloud Platforms by utilising cloud-based data platform services for data storage, processing, and analytics - Embrace Microservices Architecture by Design data architecture using loosely coupled and independent microservices - Utilize APIs for Data Integration - Build Data Pipelines - Choose Scalable Data Storage - Monitor and Manage Performance. By adopting modular and scalable technologies, organizations can build a data architecture that is agile, flexible, and adaptable to future needs.

Define Data Governance Framework by Establishing a clear and comprehensive data governance framework outlining roles, responsibilities, and processes - Define data ownership, data stewardship, and data access controls - Implement data governance policies and standards aligned with business objectives and regulatory requirements - Implement Data Quality Management - Ensure Data Security and Privacy - Foster Data Stewardship - Monitor and Audit Data Architecture Changes - Communicate and Collaborate - Continuously Improve Data Governance. By implementing data governance principles and practices, organizations can build a data architecture that is reliable, secure, and compliant, and ultimately delivers greater value to the business.

I think creating the data culture in the organisation is the primary key to enable the agile changing in data architecture. If the people in the organisation have the same motivations to make the change so they will make the change very smooth and fast. The people should be aware about the value of the data within the business and who the data will enhance their business otherwise, they will be another big stick in the wheel of the change.

Define a Data Vision- Strategy by Establishing a clear and compelling vision for the role of data in the organization - Promote Data Literacy-Training - Encourage Data Sharing and Collaboration - Recognize and Reward Data-Driven Behaviour - Create a Data-Centric Workspace - Lead by Example by utilising data to inform strategic planning and resource allocation - Continuously Improve the Data Culture. By fostering a data culture, you can empower employees to leverage data effectively, unlock its full potential, and drive sustainable growth.

One more thing to consider - implement APIs and microservices where possible. Decoupling data sources and applications enables you to make changes to one component without impacting others. Happy architecting folks!

A key element to achieve the topics listed to date is to build a framework to define the semantics of each data domain, separate from the technology used to adopt it. A semantic schema that normalizes how data is defined and aligned provides huge returns in simplified architectures, reuse of data for different purposes, and provides the foundation on which transformational technologies like ML / AI without wasting time on data interpretation and translation between lines of business. The rules of the schema can also be defined to allow data to be added in a consistent manner to help manage change by all consumers as the business evolves.