What are the steps to define your data architecture vision and principles?

It's very important to highlight the vision's commitment to data democratization. It should be clear on how the data architecture aims to make data accessible to a broader audience within the organization, empowering individuals from across various departments including business users to leverage data for informed decision-making.

Defining data architecture and principles, start by understanding your organization's business objectives and existing data landscape. Develop clear data principles addressing quality, governance, security, and ethical use, aligned with regulatory requirements. Design a scalable and flexible data architecture framework, deciding on technologies for data storage, processing, and analytics. Incorporate data governance to ensure data integrity and quality, and plan for integration and interoperability among various data sources and systems. Prioritize security and privacy, encryption and access controls. Review and update evolving business needs and regulatory changes, ensuring a robust, scalable, and strategically aligned data infrastructure.

A variety of approaches exist and these may differ, typically these steps should include the following elements : - Clearly articulate your business goals, - Comprehending the current data infrastructure and identifying its inherent challenges. - Determine and involve essential stakeholders to gain insight into their requirements and viewpoints -Harmonizing the organizational vision with strategic business objectives -Grasp the nuances of data security and compliance, and devise a robust governance strategy to address these aspects. -Develop a strategic blueprint detailing the pathway to your desired state, encompassing necessary technological requirements -Execute the plan, collect feedback, and continuously make iterative enhancements.

I believe depending on size and maturity of the organisation its strategic goals are laid out within the business strategy. The data strategy then should outlines how data will be utilized to drive outcomes and facilitate data-driven decision-making, enabling the business to achieve its goals. The data architecture vision then translates the data strategy into practical terms, detailing how data will be sourced, stored, and structured;including considerations of data modeling, technology, scalability, and governance- which itself is a beast. While the visions for data strategy and data architecture may sometimes be merged, the distinctintopn enables more effective measurement and adjustments.

Absolutely. In addition, the Data Architecture vision needs to act as the driver for a. Defining To be state Data Architecture roadmap b. The processes required for implementing / re-mediating Data Governance mechanisms c. The team constucts required to achieve both pt a & b It also needs to align with Enterprise Business Architecture and Platform Architecture roadmaps.

Define Data Principles as guardrails to guide decisions in Data Architecture: 1. Data as a Product - Carefully manage enterprise's data to ensure that we know its origin, understand its meaning/usefulness, manage its life cycle so that we can rely upon its relevance, and sunset it when no longer required for decision-making. 2. Data is Shared - Timely access to accurate data is key to improving the quality and efficiency of enterprise data-driven decision-making. Right data classification and proper security measures put in place allow data to be shared safely. 3. Data is Accessible - Searching for relevant data, and data access request/approval processes should be made possible/easy for authenticated/authorized requesters and approvers.

Essentially curating single clean shared organisational data, understood and interpretable in common language and consumable through secured Chanel’s or interface, lays fundamental for data interoperability and data architecture. It forms the cadence for good data governance

I see data architecture principles as our guiding rules for designing, implementing, and managing our data infrastructure. These principles cover vital aspects like data quality, security, governance, integration, scalability, and performance. They ensure consistency, coherence, and comprehensiveness while being actionable and measurable. They serve as standards for evaluating and enhancing our data architecture continually.

Data Quality and Security are amongst the most critical principles that are often most overlooked. Your principles should assume that data is never secure by default and is never free of quality issues by default. Keeping security and quality in mind will help you define an architecture that will address the most common issues faced by architects today. Another important principle is observability of the solution. This principle becomes more and more important as the size of your data increases and as your architecture becomes more complex.

I believe our data architecture principles should focus on ensuring data is trustworthy, accessible, interoperable, secure, and scalable to meet current and future business needs.

Before defining architecture objectives, you want to make sure you understand your business desired outcomes and current landscape. Your data architecture objectives have to be fully aligned with your business objectives and strategy, they cannot be generic. Data architecture objectives change as business objectives evolve. For example: if you want to increase upsell and cross sell opportunities by X next FY year and customer information is scattered across multiple systems, you might focus on centralising this information so you start customer behaviour analysis to understand their needs. In this case, your objective could be "centralising customer information so customer behaviour can be understood".

I believe our key data architecture objectives should be boosting data quality and availability to drive faster insights and decision-making across the organization.

Accessibility, availability and performance are the top objective toward the data architecture. This can be achieved by not only adopting the standard practice procedure but also by understanding the business and needs of the organization.

Defining a data architecture vision involves strategic planning and careful consideration of business needs. Rather than overcomplicating the architecture, focus on essential requirements while adhering to principles and best practices. Only incorporate features that are necessary and align with security, compliance, and quality standards. Avoid unnecessary complexities and technologies that may not be relevant to current business objectives. Regularly revisit and refine the architecture to ensure it remains agile and adaptable to evolving needs. Strive for a balance between innovation and practicality, ensuring that the architecture serves the organization's immediate and long-term goals effectively.

Data architecture objectives should support the vision: each architecture objective should support the corporate goals and business objectives. This can be measured with different techniques (like SMART or OKR) to be prioritized. The key item in the objectives is understanding that you can't deliver everything at once, so having an ordered list based on scope, complexity and value delivered is a must.

I see data architecture metrics as vital gauges indicating how our data systems align with our goals. These cover critical areas like data quality, security, governance, integration, scalability, and performance. They act as practical indicators, offering a comprehensive view of our system's effectiveness. By analyzing these metrics—measuring the reliability of our data, assessing security measures, evaluating governance protocols, gauging system integration, scalability, and performance—we gain actionable insights. This analysis guides our efforts in refining and optimizing our data architecture continuously. These metrics aren't just numbers; they're strategic tools for enhancing our data infrastructure to better meet our evolving needs.

Gathering of data should be extremely transparent and should be told to users what data will be used for data analytics and why.

Many stakeholders are easy to point out, like IT infrastructure teams, IT DevOps, IT procurement and governance, the CIO, the CDO, etc. Also, Security teams are easy to spot. But there are other important (even more important) stakeholders like Business users, Line Of Business directors, Advanced users sitting in business offices, etc. Pay attention to these key stakeholders because they will give you the most insights and the buy-in to success.

Identifying stakeholders is crucial before moving forward; these individuals will serve as guides and sources of knowledge, contribute toward data architecture. So according to the organizational hierarchy there can be different stakeholders based on there role and responsibilities.

Below are some of the other things to consider in defining data architecture vision. Policies about data usage, guiding principles, statements of intent about usage, and mechanisms for accountability. Data models including conceptual, logical data models, physical data models, and application-specific logical data models; Data catalogs, Inventories of data sources Master data or reference data Defined key data, with glossaries, dictionaries, definitions, and applied standards Metadata and how it should be managed Data lineage and flow through systems A road map for implementation