Practical data governance for analytics estates in Gloucester: what the Information Commissioner's Office (ICO) expects under UK GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
Practical data governance for analytics estates in Wuppertal: what the federal commissioner (BfDI) and the Land data protection authorities expects under the EU GDPR and the Bundesdatenschutzgesetz (BDSG), the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
How to build a Microsoft Fabric or Azure data platform for a Biel/Bienne organisation: medallion layout for precision engineering and instruments and industrial machinery and engineering, honest capacity sizing, Direct Lake trade-offs and the Switzerland residency position.
How to build a Microsoft Fabric or Azure data platform for a Cavan organisation: medallion layout for food and drink manufacturing and agriculture and agribusiness, honest capacity sizing, Direct Lake trade-offs and the Ireland residency position.
How to build a Microsoft Fabric or Azure data platform for a Leeds organisation: medallion layout for retail and commercial banking and insurance and reinsurance, honest capacity sizing, Direct Lake trade-offs and the United Kingdom residency position.
How to build a Microsoft Fabric or Azure data platform for a Verviers organisation: medallion layout for textiles, fashion and luxury goods and industrial machinery and engineering, honest capacity sizing, Direct Lake trade-offs and the Belgium residency position.
Practical data governance for analytics estates in Killarney: what the Data Protection Commission (DPC) expects under the EU GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
Practical data governance for analytics estates in Milton Keynes: what the Information Commissioner's Office (ICO) expects under UK GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
How to build a Microsoft Fabric or Azure data platform for a Slough organisation: medallion layout for software and technology and logistics and freight, honest capacity sizing, Direct Lake trade-offs and the United Kingdom residency position.
How to build a Microsoft Fabric or Azure data platform for a Rotherham organisation: medallion layout for metals and materials and industrial machinery and engineering, honest capacity sizing, Direct Lake trade-offs and the United Kingdom residency position.
Practical data governance for analytics estates in Namur: what the Data Protection Authority (APD / GBA) expects under the EU GDPR and the Belgian Data Protection Act of 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
How to build a Microsoft Fabric or Azure data platform for a Bern organisation: medallion layout for public sector and telecommunications, honest capacity sizing, Direct Lake trade-offs and the Switzerland residency position.
How to build a Microsoft Fabric or Azure data platform for a Neuss organisation: medallion layout for logistics and freight and food and drink manufacturing, honest capacity sizing, Direct Lake trade-offs and the Germany residency position.
How to build a Microsoft Fabric or Azure data platform for a Saarbrücken organisation: medallion layout for automotive and mobility and metals and materials, honest capacity sizing, Direct Lake trade-offs and the Germany residency position.
Practical data governance for analytics estates in Winchester: what the Information Commissioner's Office (ICO) expects under UK GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
How to build a Microsoft Fabric or Azure data platform for a Lugano organisation: medallion layout for retail and commercial banking and asset and wealth management, honest capacity sizing, Direct Lake trade-offs and the Switzerland residency position.
How to build a Microsoft Fabric or Azure data platform for a Luton organisation: medallion layout for aviation and airports and automotive and mobility, honest capacity sizing, Direct Lake trade-offs and the United Kingdom residency position.
Practical data governance for analytics estates in Cork: what the Data Protection Commission (DPC) expects under the EU GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
A practical guide to Power BI consultancy in Exeter: the definition traps in public sector and higher education and research, honest licensing arithmetic, hire-versus-engage costs, and the compliance position under UK GDPR and the Data Protection Act 2018.
A practical guide to Power BI consultancy in Hamburg: the definition traps in ports, terminals and maritime and logistics and freight, honest licensing arithmetic, hire-versus-engage costs, and the compliance position under the EU GDPR and the Bundesdatenschutzgesetz (BDSG).
Practical data governance for analytics estates in Maidstone: what the Information Commissioner's Office (ICO) expects under UK GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
Practical data governance for analytics estates in Glasgow: what the Information Commissioner's Office (ICO) expects under UK GDPR and the Data Protection Act 2018, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
How to build a Microsoft Fabric or Azure data platform for a Cambridge organisation: medallion layout for software and technology and electronics and semiconductors, honest capacity sizing, Direct Lake trade-offs and the United Kingdom residency position.
How to build a Microsoft Fabric or Azure data platform for a Dresden organisation: medallion layout for electronics and semiconductors and software and technology, honest capacity sizing, Direct Lake trade-offs and the Germany residency position.
A 45-minute working session with an engineer rather than a sales call. You describe the problem; we set out what we would build, in what order, and what it is likely to cost.