The mark of an insights-driven business is a data architecture strategy grounded on business outcomes. Unfortunately, investments to modernize data platforms have historically led to solving the challenges, bottlenecks, and objectives of IT. For example, 40% of data and analytics decision-makers surveyed by Forrester in 2023 indicated that the most important scenario for AI was to streamline IT processes via AI-driven automation and decisioning. No wonder business enthusiasm for data modernization and cloud migration is low. Business stakeholders don’t see relevancy in these investments — architecture is not connected to their objectives and outcomes.

The promise of insights is great. But insights without action to build better customer experiences, increase revenue, or drive better business process and practices is a recipe for a data museum: a place to look at but not touch data. Intuitively, business stakeholders know the highest value and greatest business outcomes come from getting insights faster and then putting them to work at the right moment.

We’ve heard this message before. What needs to change? It is the approach to data architecture. Rather than starting from the data and moving up to the use case, start with the use case and move down to the data. The reason is simple. Business outcomes are not just metrics and KPIs; they are proxies for the way our business runs and how we engage and deliver value to customers. To realize data value and monetize data, we need to know how it is created, why it is consumed, what insight will drive an action or automate a process and decision, and when this data needs to integrate with our business actions and decisions. That has a significant impact on the tools we choose, the pipelines we build, the sources of data we use, and the way data is governed. 

Advanced insights-driven businesses are turning to a data architecture approach based on business outcomes. As data mesh transitioned data ownership and design to the business, it forced data architects and engineers to work backward from data capture, request, and consumption to the data and systems. Data mesh forced data architecture to map data ingestion and provisioning to a customer journey, business process, and decision process. The result: No single data architecture standard serves all objectives and outcomes. Instead, five architecture patterns shape data use to achieve business outcomes. 

  • Marketplace pattern: Efficiently scale data for a variety of insights. Lowering total cost of ownership and improving data utilization are the foundational contributions to data ROI.
  • Business analytic pattern: Inform and scale decision-making. Understanding the performance of business practices allows business leaders and management to adapt to changing conditions and remain resilient. 
  • Prescriptive pattern: Formulate AI/ML to power experiences. Inferencing improves the quality of decisions at scale and personalizes experiences, increasing customer, employee, and business metrics. 
  • Operations and commerce pattern: Speed up and automate actions at scale. Stream processing and data freshness tune your database to optimize tasks and decisions across all metrics in a business or customer process. 
  • Edge intelligence pattern: Create a backbone for multichannel experiences and value. Streaming, communications networks, and devices share data and intelligence connected to multiple value streams, such as customer experience and automobile performance, for exponential increases in revenue and profitability. 

To learn more, join us at the Technology & Innovation APAC event in Sydney or digitally on October 31 – November 1 to learn more about the five outcome-oriented data architecture patterns that increase the effectiveness of data investment and monetize insight. See you there!