Foundation of Data Practice (Supporting the Four 4s Framework)
Mustafa Qizilbash
Author, Data & AI Practitioner & CDMP Certified, Innovator of Four 4s Formula, DAC Architecture, PVP Approach
The Four 4s framework provides a structured approach for data teams to define strategy, execute initiatives, and drive value. As they implement this framework, they must also integrate the following foundational elements, which are essential to building a strong and sustainable data practice.
Ethical Data Governance & Global Compliance
? Ensure Compliance with Evolving Regulations?—?GDPR, CCPA, AI Act, etc.
? Establish Ethical Data Use Policies?—?Prevent data misuse, ensuring transparency and fairness.
Crisis & Incident Management (Data Resilience)
? Plan for Data Disruptions?—?Develop resilience strategies for data availability, security, and business continuity.
? Ensure Compliance with Cybersecurity Standards?—?Work closely with CISO teams to prevent breaches and manage incidents.
External Data Monetization & Partnerships
? Leverage Data as an Asset?—?Explore external monetization (e.g., data marketplaces, licensing, API ecosystems).
? Establish Data Partnerships?—?Collaborate with external entities for data-sharing initiatives and insights.
Emerging Technology Strategy
? Define Data & AI & Automation Roadmap?—?Position data as the foundation for AI-driven decision-making.
? Assess Data & AI Ethics & Bias Risks?—?Ensure Data & AI systems are explainable, fair, and comply with ethical guidelines.
Data Culture & Literacy Enablement
? Evangelize a Data-Driven Culture?—?Drive enterprise-wide awareness, fostering a mindset where decisions are backed by data.
? Enhance Data Literacy Programs?—?Work with HR & L&D to establish training programs for employees at all levels.
Cheers.
Data Architect at Tata Steel BV, designer of data constructs.
1 个月In my company they always ask: what will we have to do, what will it cost and what will the benefits be. Most of all the first (do) proves to be very daunting to decide on because out of the large list. There is so much data debt (and or technical debt), as in most companies. Awareness seems though the most important and even that needs to be made very practical. Many systems and teams are overloaded due to bad data use and handling. That type of issues can only be solved when people understand their impact. With that understanding the interest in all other things data emerge.
Data Architect at Tata Steel BV, designer of data constructs.
1 个月The foundation is a bit high up don't you think? I would call it the supported data practice when looking at this picture.
Technical Product Manager | AI & Quantitative Modeling @ EY | SAFe 6.0 PO/PM | Full-Stack Developer | Generative AI & Financial Modeling
1 个月Love the graphics did you use Napkin AI ?
Author, Data & AI Practitioner & CDMP Certified, Innovator of Four 4s Formula, DAC Architecture, PVP Approach
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