What data architecture skills do you need to succeed in the future?

The one area that really adds business value is paying attention to the semantics of the business. We need to manage the meaning and language of the entire organization. Make sure everyone is speaking the same language. The most exciting element is that the language model can be understood by machines giving us even more benefit. Seeing Data Models and Glossaries gathering dust is depressing. Now the language models we developed can be used by AI to give even more understanding. There is nothing better than seeing your models (ontologies, taxonomies) being unified with the data!

Dive into your company's core, and learn its goals and pains. Speak the language of business leaders; turn their visions into data-driven solutions. Remember, every byte of data should tell a story that resonates with your business's heartbeat. Think of yourself as a bridge between the bustling city of business and the intricate world of data. Like a skilled translator, you must interpret the needs and goals of your organization into a data strategy that propels the business forward. Just as a city planner ensures roads align with urban development, align your data architecture with your company's roadmap to success.

From my perspective the importance of relational data modeling should not be underestimated. This practice, often regarded as a "lost art," remains invaluable not only for its physical aspects but also for its conceptual and logical perspectives. Modeling goes beyond the mere organization of data structures; it involves a deep understanding of the relationships and entities of business concepts. Those who possess a solid grasp of modeling (Conceptual, Logica & Physical). Will be better equipped to navigate complex data systems to meet the needs of the business.

There are many ways to model the data. The decisive factor will be - whether that data model can serve the purpose, for which it was created. Like with everything, there are benefits and trade-offs to be considered - like greater cohesion at the cost of additional complexity.

Think of data modeling as crafting a miniature world – where every data element is a building block. Get creative! Whether it's a complex web of streaming data or a simple relational database, your design should be as intuitive as it is robust. Imagine data modeling as constructing a LEGO set. Each block represents a piece of data, and your job is to assemble them in a way that makes sense, is aesthetically pleasing, and functional. Whether you're building a simple house or a complex starship, the principles of structure, consistency, and scalability are key.

We should also consider Data Ingestion as part of Data Integration. Data from different sources are ingested into a common storage area commonly referred to as a Data Lake. In this zone, the data is raw. From which you can apply harmonisation or curation to apply the semantics model. Hub-and-Spoke integration was used in one of the projects I worked on, with data hubs sourcing from multiple systems and allowing multiple consumers to access the data.

As a Systems Analyst with a core function in Business Intelligence, I've realised that cloud computing is likely to be the most disruptive driver of a fundamentally new data-architecture approach, because it allows businesses to rapidly expand AI tools and capabilities for competitive advantage. Amazon Web Services, Google Cloud Platform, and Microsoft Azure have altered the way businesses of all sizes source, construct, and run data infrastructure, platforms, and applications at scale. Every company that wants to flourish in the future must begin to address the necessity for its analysts to develop skills in cloud computing.

Imagine being a DJ, mixing tracks from various genres. Data integration is similar – blend diverse data sources into a seamless flow. Master the art of ETL and APIs, making data dance together in harmony. Picture a gourmet chef who masterfully combines ingredients from various cuisines to create a unique and harmonious dish. Similarly, in data integration, you're blending diverse data sources to serve up a coherent, insightful view of your organization's data landscape.

Rule your data kingdom wisely. Set clear laws (policies) for data usage and protection. It's not just about keeping data safe; it's about making it reliable and ethical. Be the guardian that your data needs! Act like the librarian of a vast, digital library. Every book (data) needs a place, a purpose, and rules for use. You're ensuring that data is not only secure and private but also organized, accurate, and accessible to those who need it, just like books in a library.

Unleash the power of numbers to tell compelling stories. Equip yourself with the best tools to dig deep into data, uncovering insights that can revolutionize business strategies. Remember, a well-interpreted graph can be worth a thousand spreadsheets. Think of data analytics as being a detective with a knack for numbers. You're sifting through mountains of data, searching for clues (insights) that solve business mysteries. Your tools are your magnifying glass, helping you zoom in on the details that matter.

Stay hungry, stay foolish in the world of data. Always look out for the next big thing – be it AI, machine learning, or something no one's heard of yet. Your curiosity and willingness to experiment will be the spark for data revolutions in your organization. Be the Edison of data in your organization. Keep your eyes on the horizon for the next big invention in the data world. Whether it's a groundbreaking algorithm or a revolutionary data processing technique, your role is to experiment and innovate, turning the 'impossible' into the 'possible.'

Architects needs to address complex aspects of an organization's strategy, the role of the data architect has become strategic. Business architecture artifacts such as models not only serve as design artifacts for storage, integration, and data quality, but also influence the technical architecture of technology selection. The ability to replace small components instead of dismantling the entire design can be crucial. Architectures must be agile for future frameworks & architectural methodologies. It cant be isolated deployments because architects address costs, optimizations, and business agility challenges. Apart from designing blueprint, they ensure that strategies are implemented and that their success is assessed and measured