How Enterprise Architects can get more support for technology led innovation
Enterprise Architects are increasingly vital as guides for technology-led innovation, but they often struggle with obstacles like siloed teams, misaligned priorities, outdated governance, and unclear strategic value. The blog outlines six core challenges—stakeholder engagement, tool selection, IT-business integration, security compliance, operational balance, and sustaining innovation—and offers a proactive roadmap: embrace a “fail fast, learn fast” mindset; align product roadmaps with enterprise architecture; build shared, modular platforms; and adopt agile governance supported by orchestration tooling.
Agi Marx, June 18, 2025
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Measuring success in data: KPIs and continuous improvement
Summary In data-driven initiatives, success hinges on establishing clear KPIs: measuring areas like customer satisfaction (e.g., NPS, retention), operational efficiency (e.g., data accuracy, processing speed), engineering performance (via DORA metrics), and financial impact (e.g., cost savings, revenue growth). Platforms like Calibo enable organizations to set baseline metrics early, facilitating effective progress tracking. Paired with continuous…
How to build a data-driven culture: empowering organizations with data literacy and tools
Building a data-driven culture requires more than just access to data—it demands a strategic focus on data literacy and the right tools to empower teams.
How to turn data into actionable insights: maximize the potential of your data
Converting raw data into actionable insights demands more than static dashboards—it requires real-time intelligence embedded into daily operations. This blog outlines a strategic framework based on building a robust analytics infrastructure, centering on insight generation, and dismantling data silos through governance and literacy.
How to leverage predictive analytics to make better decisions
The blog outlines a three-step roadmap for businesses to unlock the power of predictive analytics and gain a competitive edge. First, it emphasizes the importance of gathering and preparing a diverse dataset—such as sales history, customer interactions, and external market data—to ensure reliable forecasting. Second, it explains how AI and machine learning tools can transform that data into actionable insights for demand forecasting, personalized marketing, dynamic pricing, and supply chain optimization. Third, it recommends implementing industry-specific use cases, like inventory prediction in retail, churn models in SaaS, and risk scoring in finance, to drive tangible business outcomes.
Self-service platforms: enabling autonomy through platform engineering
Self-service platforms: built on a foundation of platform engineering, empower teams to work independently with minimal IT intervention, enabling faster innovation across software and data workflows.
The biggest challenges for IT and business leaders in 2025 – and solutions
IT and business leaders face rising complexity—from fragmented toolchains and delayed innovation cycles to the urgent need for AI, data, and security strategies aligned with business outcomes.
