Data Architectures in the AI Era: Key Strategies and Insights

Data is a critical component to all aspects of the world in 2024. It is more valuable than most commodities, and there is an exponentially increasing need to more safely and accurately share, use, store, and organize this data. 

Data architecture is just that: the rules and guidelines that users must follow when storing and using data. There is significant benefit to housing and conglomerating this data management into a single unified platform, but there are also emerging challenges such as data complexities and security considerations that will make this streamlining ever more complicated. The popularity of generative AI (commonly know as GenAI) that is steamrolling the technology industry will mean that data architecture will be completely changed in this revolutionary, modern era.

Unsurprisingly, since this modernization is taking the world by storm in a very quick and competitive fashion, there are heightening stressors and pressures to adhere to it quickly. While there are projections that 80% of enterprises will incorporate GenAI APIs or GenAI-enabled applications, less than 25% of banking institutions have implemented their critical data into the target architecture; this is only one industry. There is a need to move away from data silos and onto the newer and modern data fabric and data mesh.

Data Silos Are Old News — It's About Data Fabrics and Data Meshes

In the automotive industry, among others, there has been a noticed need to move away from the outdated data silos. With data silos, information is inaccessible. It is gridlocked for one organization only. This hinders any communication or development and pigeonholes data into a single use without considering the transformation and evolution that can occur if it is viewed as a shared asset. 

A data fabric is an approach to unite data management. As mentioned, data is often gridlocked away and data fabrics aim to unlock it at the macro level and made available to multiple entities for numerous, differentiated purposes. A data mesh separates data into products and delivers them to all parties as decentralized and with their own individualized governances. 

This transition to modern data architecture is also altered by the adoption of artificial intelligence (AI). AI can help to locate sophisticated patterns, generate predictions, and even automate many processes. This can improve accuracy and largely benefit scalability and flexibility. However, there are also challenges of data quality, transparency, ethical and legal factors, and integration hiccups. This leads to many strategies and insights that can help to guide and smooth out the progression from traditional to modern data architecture.

Key Strategies

Build a Minimal Viable Product First

Accelerating results in data architecture initiatives can be achieved in a much quicker fashion if you start with the minimum needed and build from there for your data storage. Begin by considering all use cases and finding the one component needed to develop so a data product can be delivered. Expansion can happen over time with use and feedback, which will actually create a more tailored and desirable product.

Educate, Educate, Educate

Educating your key personnel on the importance of being able and ready to make the shift from previously familiar legacy data systems to modern architectures like data lakehouses or hybrid cloud platforms. Migration to a unified, hybrid, or cloud-based data management system may seem challenging initially, but it is essential for enabling comprehensive data lifecycle management and AI-readiness. By investing in continuous education and training, organizations can enhance data literacy, simplify processes, and improve long-term data governance, positioning themselves for scalable and secure analytics practices.

Anticipate the Challenges of AI

By being prepared for the typical challenges of AI, problems can be predicted and anticipated which can help to reduce downtime and frustration in the modernization of data architecture. Some of the primary ones are: data quality, data volume, data privacy, and bias and fairness. Data cleaning, profiling, and labeling, bias mitigation, validation and testing, monitoring, edge computing, multimodal learning, federated learning, anomaly detection, and data protection regulations can all assist minimize the obstacles caused by AI. 

Key Insights

Unifying Data Is Beneficial for Competition

It is almost a unanimous decision that unifying data is useful for businesses. It helps with simplifying processes, gaining flexibility, enhancing data governance and security, enabling easier integration with new tools and models for AI, and improving scalability. Data fabric brings value for business and can increase competitive advantage by understanding the five competitive forces: new entrants, supplier bargaining, buyer bargaining, competitor rivalries, and substitute product/service threats.

Data Is a Product

There is a view that data should be domain-driven, viewed and handled as an asset, self-served on a platform, and undergo federated, computational governance. This is achieved through separation of data by domain and type; the incorporation of metadata for data to exist and be explained in its own, isolated format; the ability to search and locate data independently; and a supportive and organized housing structure.

Handling Multiple Sources of Data Is Challenging

It is critical to remember that combinations of data from numerous sources is difficult. Real-time capabilities for some processes like fraud detection, online shopping, and healthcare just simply are not ready yet. Standards and policies need to be adopted. There will be inevitable trouble with managing all clouds and data sources, potential security breaches and governance struggles, and the necessity for continuous development and customization.

Modern Data Architecture Will Forge Ahead With the Advent of AI

Despite the difficulties and complexities of updating the existing and traditional data architecture methods, there is no doubt that modern data architecture will also include AI. AI will continue to grow and help organizations use data in a prescriptive way, instead of a descriptive way. Although many people are wary of AI, there is still the overwhelming hope and vision that it will create opportunity, maximize output, and power innovation in all markets, including data structure and management. Those in the wake of AI and modern data architecture will know the benefits of more productivity and operational efficiency, enhanced customer experience, and risk management.