You're seeking to revolutionize your data architecture. How can you ignite innovation as a data architect?

To shake up your Data Architecture and spark some serious innovation as an Architect, I've found that looking at things from a People, Process, and Technology perspective is helpful that meets the needs of today and tomorrow. People side, it's all about making sure your team is made up of growth mindset folks who bring both a realistic "what needs to be done now" perspective and some 10X game-changing ideas. Process side, make sure your Data Platform Roadmap includes a mix of must-have features and some innovation-related ones that get prioritized over multiple sprints. Technology side, bet on a Data Platform that provides simplicity, scale, security, and an integrated approach to serve all key personas in your company.

To advance beyond primitive data types it is important to learn what type of data formats, composites, ADTs, complex, pointer types - the list goes on and on. legacy systems have very analog ways of computing data. In order to understand how to extract and transform data, we must introduce a method of allowing data teams to get familiar with all types of new and innovative ways to handle date. It cans start with similar architectures that point out in bound and out bound algorithms and how the interpretation of the data is handled. data is the new oil and it is very rich.

To revolutionize your data architecture and ignite innovation, embrace change by fostering a culture of continuous improvement and experimentation. Stay updated with the latest trends and technologies in the field, such as cloud computing, AI, and machine learning, and integrate them into your architecture. Encourage your team to think creatively and challenge the status quo by hosting regular brainstorming sessions and innovation workshops. Implement agile methodologies to remain flexible and responsive to new ideas and changes. Collaborate closely with cross-functional teams to gain diverse perspectives and insights. Promote a mindset of learning from failures and successes using feedback and insights to refine and improve your solutions.

Design thinking workshop are a great way to initiate innovation. Start with a reflective open-ended question like What can we do if 1. There is no limitation in funding 2. No limits to your role you are assigned today 3. If you have a chance to present in front of the CTO next week Few rules: 1. No filters during the collection of idea 2. No judgement on good or bad idea 3. No filter based on what's possible today This exercise is to unfold the imagination. Document the collection and discuss couple of minutes to understand the depth of those thought like the possible impact, the impedance then stack rank them for a commitment.

An igniter of data architecture would be to plug in an ML/AI engine, profile product data (knowing that it is unique at the useful level for all companies) and develop intelligence that performs product to market mapping, business operations efficiencies, etc. That would make AI useful and a financial windfall instead of focusing on text manipulation that most companies are considering an effective application use of this forward looking technology.

Engage stakeholders across departments to identify pain points and goals. Conduct a comprehensive analysis of existing data systems to pinpoint inefficiencies and areas for improvement. Leverage advanced analytics to forecast future requirements and scalability. Foster a culture of innovation by encouraging collaboration and knowledge sharing among your team. Stay updated with emerging technologies and trends to integrate cutting-edge solutions that enhance data management, security, and accessibility, driving overall business success.

As data volumes continue to grow exponentially, it's crucial to design with scalability in mind. One of the approaches that stands out is adopting a microservices architecture. This allows us to scale individual components independently, which is key for managing large datasets efficiently and adapting to evolving needs. By integrating cloud-based solutions and automated scaling tools, we ensure our system remains flexible and responsive. This strategy not only helps us handle current demands but also positions us to adapt seamlessly to future challenges.

Scaling to accommodate data growth is critical in any data storage system, whether a database or another storage method. It's important to analyze past data growth and forecast future trends to design a system that can scale accordingly. When forecasting, adding 10% to the previous year's projection is advisable to account for business growth. While modern cloud systems offer the flexibility to scale as needed, designing with scalability in mind is essential, ensuring proper data governance.

When starting data integration from OLTP to OLAP systems via ETL, it's essential to identify the fact and dimension tables that align with the key performance indicators (KPIs) to be achieved. Fact tables store quantitative information, and we need to establish proper relationships between these tables and the dimension tables using foreign keys to ensure accurate data modeling. Once these fact and dimension tables are clearly defined and structured, you can load and transform the data into the appropriate tables in the data warehouse. plan the CDC change data capture for the incremental load, based on the primary key and last update record data in the source data capture the data for the incremental load

Well, as a Data Architect you have exposure towards wide variety of Data Marts in the database. Just see if you can relate one another and bring value to the organisation. Then play around them and see if you can bring interesting metrics. Ideally, you need to travel an extra mile to get this covered, but worth the travel.

For example, when you want to transfer the ERP (e.g., Oracle, SAP, Dynamics) to the Wharehouse, we need to properly design the facts, dimensions, and semantic layer; we need to collaborate with multiple teams, such as finance, supply chain, and HR, to obtain their domain requirements and design and achieve the expected KPI. From the board to operations, all the teams need to be consulted before the data load to achieve the expected results, These workshops and discovery sessions are the key to success of the project

In fostering collaboration, prioritize the development of cross-functional teams that bring together diverse expertise from across the organization. These teams can work on specific data architecture projects, blending technical knowledge with business insights to create more innovative solutions. Regularly hold workshops or hackathons that encourage creative problem-solving and the sharing of ideas. By breaking down silos and promoting interdisciplinary collaboration, you can drive innovation and ensure that your data architecture meets the needs of all stakeholders.

Maintaining agility is crucial during the design phase, but once the design is finalized and approved, it is important to adhere to it to achieve the desired KPIs. Once the data load process begins, no further changes should be made till the entire process is complete. However, these changes must be cautiously implemented, as modifications in the data warehouse can impact the semantic layer and subsequent visualizations. While staying agile is essential, determining the appropriate level of flexibility is a key factor in successful data architecture.

As a huge fan of Agile which should include Continuous Integration and Continuous Deployment - inform your PMO what idea you have in mind and request for assistance. Never ever hesitate to raise questions & ask for help - they are there to assist us. This should be your first step to stay agile in any aspect and the rest will follow!