How can you maintain your data architecture design as your data needs change?

Data architecture is the cornerstone of effective data management. Yet in today's dynamic landscape, it's a canvas that requires constant refinement. Your data needs aren't set in stone; they evolve, driven by changing business objectives and emerging technologies. The key to maintaining data architecture design lies in assessing its current state. By conducting thorough data audits, quality assessments, analyses, modeling, and cataloging, you gain a holistic view of your data ecosystem. This not only highlights strengths but also reveals weaknesses and vulnerabilities that demand attention. Adaptability is the hallmark of a robust data architecture, ensuring it remains a sturdy foundation as your data landscape continues to transform.

To adapt your data architecture to changing data needs, conduct regular assessments to ensure alignment with current requirements. Implement flexibility by using scalable cloud-based solutions and dynamic storage options. Embrace modular design, allowing easy integration of new data sources and technologies. Maintain open communication channels with stakeholders to understand evolving needs and prioritize accordingly. Continuously monitor and optimize data pipelines and processing to meet new demands efficiently. Regularly update documentation to keep the architecture's design transparent. By staying agile and responsive, you can ensure your data architecture remains effective in addressing evolving data needs.

The ultimate goal of any data architecture should be to unlock innovation. Which means that, along with appropriately data literate stakeholders, access to data should be easy, the platform should be performant, and the tools should be fit-for-purpose. On episode 2 of my podcast, A Curious Life, Oliver Schabenberger advises that you should “do fast first”. In essence, make sure that the platform can rapidly handle the data volume and process complexity you throw at it to serve the business goals. And I agree, as a performant and scalable platform enable faster decisions and easier to maintain data pipelines and models.

Consider a company transitioning from traditional databases to cloud solutions. Their data needs have expanded, requiring a more scalable architecture. By aligning their data objectives with their business expansion strategy, they ensure seamless growth. Using frameworks like OKRs, they set clear milestones, ensuring every stakeholder, from data analysts to managers, plays their part in this evolution. • Data needs evolve with business growth. • Seamless transitions require strategic alignment. • Every stakeholder plays a part. • Clear milestones guide the journey.

It is absolutely critical to the success of data initiatives that they have clear goals and priorities, and that their goals and priorities align with the goals and priorities of the organisation. It is imperative, therefore, to start at the organisational level. What does the organisation need to achieve? When? Why? Once clarity and alignment has been achieved at this highest of levels, one can address the next level down, e.g. if the strategy is to save £2m through operational efficiencies (to be owned by the COO) and generate an additional £3m revenue from sales to new customers (to be owned by the CGO), what interventions from a data perspective will support the achievement of these goals?

It’s important to design flexible data solutions that will require minimal changes when the underlying technologies or the overlying business processes change. Canonical data models can be designed, data exchanges can happen over certain abstraction layers, APIs can be leveraged and other such adaptable techniques can be used to achieve this.

It's important to place the design in the context of the wider organisational goals and the current and anticipated future states, being imaginative of future needs but grounded in what the org. needs in the near-term and what it can afford. So: - Be confident any short-term use-cases you know of can be implemented rapidly in the target architecture - Be clear about high-value medium-term use-cases, and design for those to work well without major architectural changes - Be mindful of prospective high-value far-horizon use-cases, and design in a way that supports scaling or integration, maybe by adding new architectural components The most helpful advice I've ever received is: - Use boring technologies - Think big - Start small

To maintain data architecture as data needs evolve, start with a robust foundation that allows for scalability and adaptability. Utilize modular components and consider cloud-based solutions that offer flexibility and cost-efficiency. Regularly assess the architecture's performance, identifying bottlenecks and areas for improvement. Keep communication lines open with stakeholders to understand changing data requirements. Integrate agile methodologies for quick adjustments and updates, and ensure thorough documentation to keep the architecture transparent and accessible. By staying proactive and responsive, your data architecture can effectively evolve with the changing data landscape.

Implementing data architecture with evolving needs: 1. Regular Assessment: Continuously evaluate architecture. 2. Scalability: Ensure adaptability for growing data. 3. Modular Design: Use modules for easy changes. 4. Open Communication: Keep stakeholders informed. 5. Agile Methodologies: Implement agile for quick adaptations. 6. Continuous Monitoring: Track performance and resources. 7. Data Governance: Update for compliance. 8. Data Integration: Stay agile. 9. Documentation: Maintain updated documentation. 10. Training: Invest in team skills. 11. Testing: Rigorously test changes. 12. Feedback Loops: Seek user input. 13. Data Quality: Maintain quality standards. 14. Disaster Recovery: Review and update plans.

Testing plays a critical role in ensuring the success of data architecture changes. Below are some testing strategies that can be used to evaluate the changes: - Compare the data generated by the new architecture with historic data from the same time period and determine if they match the outcomes of the existing systems. - Find the percentage of data that matches in both the systems and newly generated data due to the change, as well as the percentage of missing data. - Discuss the differences with key stakeholders to determine if they are acceptable.

Let’s first analyze the situation outlined in the question: how can change in the input data affect the architecture of these data processing? What can it be? 1. Introduction of a new product with a new attributes? 2. Environment’s change? 3. Appearance of new factors on market? 4. Need to adapt to another business domain or another operational model? An architecture that has been built without lock-ins shall accommodate scenarios 1-3 via changes to its components configuration (reference tables, models, rules - must be externalized) If it cannot adapt to changes without customization - it is not mature enough. When you are faced with scenario 4: execute gap analysis. Such scenario always requires customization or development a new one

Before changes to the data architecture are designed and implemented, the pre-change performance and the expected benefits of change should first be clear and agreed upon by all stakeholders. Having clarity around the benefits makes measurement and communication of the performance of the data architecture meaningful and relevant to stakeholders. These might be financial, technical-performance related, compliance-related - anything at all that stakeholders expect will drive accomplishment of the organisation's goals. However, you also have to be mindful of exactly what is being measured and how it is being communicated and reacted to within the organisation. Careful selection of metrics and continuous measurement is necessary.

In my review of recent tech, data, and digital articles, I've observed a concerning trend: most lack a crucial business context, presenting technology as an end in itself. While these articles often delve deep into technical complexities, they rarely connect these to real-world business challenges or objectives. In our roles as tech and data leaders, we must design architectures that not only anticipate but also adapt to evolving business needs. Especially post-COVID, when P&L structures are rapidly changing, our architectures must be agile enough to pivot. We need to be a step ahead, proactively identifying business opportunities while ensuring our tech investments align with broader business goals. Let's move beyond tech for tech's sake.

1. Think framework. In today's highly dynamic market scenario, changes are inevitable. What can be controlled is the impact of the change. Choosing the right framework will help in minimizing the blast radius. 2. Be in close alignment with the product mgmt team. Make sure the architecture you are building can support the product they are envisioning. 3. Emphasize on reusability. Do not create redundancy just because something is easy to build and support. 4. Keep it simple. Simple enough to build, maintain and update. Customizing a complex architecture brings in more complexity and can become unusable in the long run.