Insights
Data architecture, Tableau, cloud engineering, and BI — from engineers with 10+ years of enterprise experience.
A synthesis of Bain and McKinsey research on what is actually stopping enterprise AI from delivering. Eight structural problems — and what data leaders are doing about each one.
The CFO is the most underused ally in enterprise AI. They control the most governed data in the business, sign off on every system that creates a data footprint, and carry the commercial authority to make data standards stick. Here is how to make the case.
Agentic AI does not make requests — it takes actions autonomously across systems. The architecture handling your dashboard queries was never designed for that. Here is what needs to change.
After years of managing both environments at enterprise scale, here is our honest assessment of when to migrate to Tableau Cloud — and when to stay on Server.
Day rates, project fees, retainer structures — a transparent breakdown of what senior Tableau consulting actually costs and what drives the price.
Most data cost problems are architecture problems in disguise. Here are the seven clearest signals that your data infrastructure needs a structural rethink.
Slow Tableau Server performance is almost always fixable. This is the diagnostic framework we use to identify and resolve the most common bottlenecks.
What we have learned from building production Azure data platforms at enterprise scale — the patterns that work and the ones that cost you later.
These terms are often used interchangeably but they describe different disciplines. Understanding the difference matters when you are building your data team.
What a data architect actually does, the signals that separate strong candidates from plausible ones, what to pay, and whether to hire in-house, engage a contractor, or work with a consulting firm.
A transparent breakdown of what data architecture consulting actually costs — from $15k assessments to $500k platform builds. What drives the price, what red flags look like, and how to get a fair proposal.
Salesforce has announced the end of life for Tableau Server. Here is the EOL timeline, your three options, what a migration actually involves week by week, the common blockers, and what to do this week.
We work with both platforms every day. Here is a direct, experience-based comparison — where each platform wins, where it loses, what the real migration costs look like, and how to make the decision without getting sold to.
The data lakehouse pattern combines the storage economics of a data lake with the query performance and governance of a data warehouse. Here is what the architecture actually looks like, when it is the right choice, and what it takes to build one at enterprise scale.
Data governance is not a compliance project. It is the set of policies, ownership structures, and technical controls that make your data trustworthy enough to act on. Here is what it actually involves — and what most implementations get wrong.
We build on both platforms every week. Here is a direct, experience-based comparison — what each is genuinely better at, the common misconceptions, pricing reality, and how to make the decision for your specific workloads.
A semantic layer sits between your data platform and your BI tools, translating raw tables into business-ready metrics with consistent definitions. Here is what it is, what it does, how to build one, and why most enterprise data quality problems are semantic layer problems in disguise.
ETL and ELT describe two different approaches to moving and transforming data. The right choice depends on your data volumes, transformation complexity, and cloud platform. Here is a practical breakdown of when each pattern fits and what drives the decision.
A data warehouse is a central repository for structured, integrated data built for analytical querying. Here is how modern cloud data warehouses work, how they differ from data lakes and lakehouses, and the decision framework for which architecture fits your organisation.
Most enterprise data architectures run on batch — hourly or daily pipeline refreshes. Real-time streaming solves a different problem and costs more to build and operate. Here is the honest assessment of when real-time is worth it and when batch is the right answer.
Microsoft Fabric consolidates Power BI, Azure Synapse, Azure Data Factory, and other Microsoft data services into a single platform. Here is what it actually includes, what it is genuinely better at, and the honest assessment of when migration makes sense — and when it does not.
Data mesh is an organisational and architectural approach that distributes data ownership to domain teams instead of centralising it in a data engineering function. Here is what it actually involves, who it is designed for, and the honest assessment of when it solves a real problem versus adding complexity.
Four platforms dominate enterprise BI. Each has genuine strengths and real weaknesses that vendor materials will not tell you. Here is an honest, experience-based comparison — what each platform is actually best at, the commercial realities of each, and the decision framework for your organisation.
Bad data is not a data problem — it is an architecture and governance problem. Here is how to diagnose the root causes of data quality failures, implement the technical controls that prevent them, and build the organisational structures that make quality sustainable.
Master data management (MDM) creates a single authoritative record for core business entities — Customer, Product, Supplier, Location — across all systems. Here is what it involves, what problems it solves, and what implementation actually looks like.
Slow Power BI reports are almost always fixable. The causes — oversized data models, inefficient DAX, wrong storage mode, too many visuals — follow predictable patterns. Here is the diagnostic framework we use and the fixes that produce the biggest performance gains.
Most Tableau dashboards are built to answer a question. The best ones are built to support a decision. Here are the design principles, layout patterns, and performance considerations that senior Tableau developers use to produce dashboards that executives actually use.
Data lineage tracks where data comes from, how it has been transformed, and where it flows downstream. It has always mattered for data governance. AI makes it non-negotiable. Here is what lineage is, how it works technically, and what good lineage implementation looks like in practice.
Most organisations build their data teams in the wrong order and hire the wrong roles for where they are. Here is the hiring sequence that works, what each role actually does, what to pay, and when to use consulting or contracting instead of headcount.