Category: Agile Data Practices
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Seize Real Advantages: 7 Urgent Reasons to Revisit Agile for the AI Era
Organizations need to revisit their agile practices due to increased complexity and changing business expectations, exacerbated by AI advancements. Key issues include fragmented management, lack of user adoption, DevOps fatigue, excessive roles, inconsistent outcomes, and AI productivity not translating into business impact. Evolving agile approaches is essential for success in today’s landscape.
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From Vision to Value: A Practical Blueprint for Developing AI Agents
Organizations are increasingly developing strategic AI agents to enhance customer and employee experiences. A hybrid approach involving both build and buy methods is essential for success. Key steps include defining roles, unifying information access, connecting to APIs, and implementing continuous testing. Avoiding development pitfalls will drive effectiveness and improve satisfaction across various sectors.
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3 Hidden Workarounds Killing Productivity And How to Spot The Manual Work
Organizations often struggle with gray work—unstructured, manual tasks that hinder productivity. Employees face inefficiencies due to data movement between systems and lack of integrated tools. As software investments rise, many feel overwhelmed, emphasizing the need for dynamic work management solutions. Identifying gray work areas can lead to more efficient workflows and decision-making.
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How to Kill Floundering Experiments and Drive an AI Learning Culture
The low success rate of AI and ML experiments, typically around 30%, often reflects a narrow focus on production deployment. Effective experimentation prioritizes an AI learning culture, value demonstration, and agile delivery methods. Best practices include setting clear success criteria, regular reviews, centralized documentation, and rewarding knowledge sharing to foster an agile AI learning culture.
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The Architect has a Significant Role in Uplifting Digital Transformation
Architects focusing on people, agile, DevOps, and data are more likely to develop platforms and standards needed in digital transformation.
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How DevOps and Agile Team Leaders Can Organize a Successful Workshop
Am ultimate guide for DevOps and Agile leaders to plan offsites and workshops. Enhance the hybrid-work experience and drive innovation.
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How Data Governance Leaders Can Identify Quick Wins
Data governance leaders have too many priorities around data security, data quality, and dataops. Six ways to find quick wins and four examples.
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How Data-Driven Organizations Overcome Culture and Technical Barriers
Insights from Coffee with Digital Trailblazers on becoming data-driven: develop a vision, drive agile data practices, and storytelling to influence
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5 Critical Priorities for CIOs to Lead on Generative AI and ChatGPT
How CIOs must lead their organizations with generative AI and ChatGPT’s disruptive opportunities and risks. Isaac Sacolick shares 5 priorities
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Agile Co-Creation: Changing the Mindset from Outsourcing to Winning Partnerships
Innovating with partners requires transforming people’s mindset away from outsourcing and onto agile collaborations.
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How to Expand Citizen Data Science: From Successful Analysts to Data-Driven Organizations
Citizen data science, data-driven organizations, and proactive data governance is achievable by creating a data center of excellence
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Eradicate “IT Business Alignment” – How to Empower a Data-Driven Partnership
Isaac Sacolick says it’s time for IT to progress beyond business alignment and forge a partnership on data-driven practices driving the future of work