Expert insights on data strategy
Every PPDM project eventually asks which database to build on. The answer is mostly driven by practical constraints, not religious preference. Here's the decision framework: when SQL Server is the right call, when PostgreSQL makes more sense, and where DuckDB fits in the stack.
Most mid-size operators don't have a dedicated data team and don't have the budget to build one. That doesn't mean governance has to wait. Here's the minimum viable version: five domains, five owners, five pages, and the discipline to keep them current.
Land and production almost never agree on the first try, and the reconciliation eats more analyst time than any other problem in upstream data. Here's why it's genuinely hard, where the disagreements come from, and how to actually solve it instead of papering over it every month.
The industry standardized the barrel in 1866 and saved itself a century of disputes. The lack of standardization in upstream data is the most expensive data problem most operators don't realize they have, and the bill comes due in the diligence room. Part three of the 42 Gallons series.
Nobody runs a producing field on the honor system. Every barrel is measured, gauged, and accounted for. Your data deserves the same. Part two of the 42 Gallons series covers clean ingestion, end-to-end governance, and what it really means to be divestiture-ready from day one rather than scrambling six weeks before close.
The oil industry has known what's in every barrel since the 1860s. The same can't be said about most operators' data. Part one of a three-part series on treating data with the same discipline the industry has applied to the physical product for 150 years. Lineage, provenance, and governance built in rather than bolted on.
Most failed PPDM implementations fail for the same reasons, and none of them are about the model itself. Here's the pattern we keep seeing, why it happens, and how to restart a stalled implementation without throwing out the work that was already done.
PPDM gets pitched as either the answer to upstream data problems or as a thousand-table beast nobody implements. Both takes miss the point. Here's an honest look at what adopting the model actually buys you, what it doesn't, and how to scope an implementation that finishes.
Every operator has a data quality story that ends with 'and then we gave up.' Upstream data is genuinely hard, and the usual frameworks don't quite fit. Here's a practical look at what actually goes wrong and how to start making progress without trying to fix everything at once.
Most mid-size upstream operators are running on spreadsheets. That's not a failure. But there's a point where it starts costing real time and money. Here's a phased, realistic path from Excel to a proper data stack without the six-figure platform purchase.
Every data initiative starts the same way: pick a platform, centralize everything, and wait for insights to emerge. Years later, the data engineers are busy and the business has nothing to show for it. Here's why the default answer keeps failing and what to do instead.
It's time someone said the quiet part loud. The database industry has been gatekeeping these advanced techniques for years. We're pulling back the curtain on ten tips that will revolutionize the way you manage data.
Cloud-first isn't always the right answer, especially in Oklahoma. Here's an honest breakdown of data warehouse options for small and mid-size businesses that need something that actually works without a runaway bill.
A practical guide to building your first AI batch processing pipeline. From identifying the right problem to architecture patterns and common pitfalls to avoid.
100% automation isn't the goal. Learn how to build hybrid systems where AI handles the bulk and humans handle exceptions, achieving better results than either alone.
AI is expensive for chatbots. Batch processing has different economics. Learn about batch API discounts, model selection, and when AI costs less than human labor.
Every organization has decades of data trapped in formats machines couldn't understand. LLMs change that. Here's how AI solves the dirty data problem traditional automation couldn't crack.
The AI bubble question is everywhere. Valuations are stretched. Skepticism is warranted. But bubbles burst speculation, not value. Here's what's real and why you shouldn't sleep on it.
Forget the tech debt. These strategic shifts change how your organization uses data, and they start with conversations, not code.
Data quality isn't a one-time fix, learn how to execute, measure, and evolve your data strategy to drive real business impact while staying ahead of change.
Your data strategy will fail without the right culture. Learn how to build executive buy-in, drive data literacy across your organization, implement governance that enables rather than restricts, and overcome resistance to change.
Transform your data strategy assessment into an actionable roadmap. Learn how to prioritize initiatives based on business value, break down data silos, set meaningful success metrics, and build a flexible 12-18 month plan that drives real results.
Most organizations claim to have a data strategy, but few can explain it. Learn the three critical reasons data strategies fail and discover the five foundational elements needed to build a strategy that aligns with business objectives and drives real value.