Expert insights on data strategy
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.