Buy energy data software when the job is standard and a vendor already handles your source formats, such as meter data management or Green Button usage feeds. Build when your sources, reconciliation rules, or reporting logic are what make your operation different. Most energy companies end up doing both: they buy the platforms and build the pipelines between them.
Written by: AKM Ahsan Created on: 15 Oct 202611 min to read
This guide is for CTOs, data leads, and operations leaders at energy companies who are weighing a platform purchase against a custom build. Usually the trigger is a pipeline that breaks every time a source changes format, a regulatory report that takes someone a week, or two systems that never agree. The question looks binary. In practice it gets decided one layer at a time.
A disclosure before we start: 6sense HQ builds custom software, so we have a stake in this question. That is why this article includes a section on when you should not build, and when you should not hire us. Most other articles ranking for this question are written by companies selling one side of it, either a platform or a custom build.
Energy data software is not one product. It is a stack of layers, and the build-or-buy answer differs for each one. Treating the stack as a single purchase is the most common reason a platform project ends up delivering less than expected.
| Layer | What it does | Common examples |
| Source ingestion | Pulls data from meters, historians, SCADA, vendor feeds, state regulators, and files | Head-end connectors, SFTP pulls, scrapers, APIs |
| Validation | Catches bad, missing, or duplicate readings | VEE (validation, estimation, and editing) for interval data |
| Storage | Holds time-series and reference data | Historians, data warehouses, time-series databases |
| Domain systems | Run core operational processes | MDM, CIS, asset management, GIS |
| Reconciliation and business logic | Makes systems agree with each other | Billing vs. metered energy, settlement, tag mapping |
| Reporting | Produces regulatory and internal outputs | FERC forms, EPA emissions reports, state filings, ESG reports |
| Analytics | Turns clean data into decisions | BI dashboards, forecasting, AI models |
The volume behind these layers is large. The US Energy Information Administration counted about 119 million advanced meters in the US, roughly 72% of all electric meters. A single meter reporting at 15-minute intervals produces 35,040 readings a year, and every one of them passes through ingestion, validation, and storage.
Buying makes sense when the layer is a solved, standard problem that doesn't set your business apart. Software architect Martin Fowler calls this the utility vs. strategic split. Utility systems should simply work, so you buy a package and adapt your processes to it. Strategic systems are where you compete, so you build them.
In energy data, the utility layers are usually:
Buying also moves maintenance off your team. When Itron argued the case for buying meter data management back in 2005, its core point was that building means owning design, testing, documentation, fixes, and upgrades forever. That still holds. A vendor spreads those costs across every customer it serves.
Build when the layer depends on sources, rules, or outputs that are specific to your operation and that no vendor's roadmap will cover. In energy data, this is almost always the ingestion of messy sources and the logic that reconciles systems with each other.
Public oil and gas data shows why. The Texas Railroad Commission publishes its datasets in at least eight formats: ASCII, EBCDIC, dBase, shapefiles, CSV, TXT, JSON, and scanned PDF or TIFF images. Its update schedule ranges from twice a day to monthly, and the commission leaves conversion to the user. That is one state. A business that works across many states inherits a different set of formats from each one.
Sources also change underneath you. The Railroad Commission retired its R-3 EBCDIC file in August 2025 and replaced it with JSON a few weeks later. A packaged connector either keeps up with changes like that or it doesn't, and you find out when a report comes back wrong.
Build is usually the right call for:
The upfront price is the least reliable number in a build-vs-buy comparison. Both paths carry costs that show up in year two and beyond, so compare five-year ownership, not the first invoice.
| Cost driver | Buy | Build |
| Upfront cost | Licenses plus implementation | Engineering time to first release |
| Ongoing price changes | Renewal increases, often outside your control | Infrastructure and staff costs you set |
| Maintenance | Vendor maintains the core product | Your team maintains everything |
| Integration work | Still needed for every system the platform doesn't connect | Designed in from the start |
| Talent | Platform administrators and integrators | Data engineers and architects |
| Exit cost | Data migration and retraining if you switch | Knowledge loss if key engineers leave |
A few data points help put numbers on these rows:
The pattern in these numbers is the core trade-off. Buying exposes you to price changes you don't control. Building exposes you to maintenance and tech debt you must manage.
Buying a platform rarely removes data engineering work, because most platforms assume the data arriving at them is already clean. A warehouse, a BI tool, or an analytics platform can store and display your data well. None of them can fix a reading that was mislabeled at the source, or a tag that changed meaning when a historian was upgraded.
The cost of that gap is well documented. Gartner estimated in 2020 that poor data quality costs organizations at least $12.9 million a year on average. In Monte Carlo's 2023 survey of 200 data decision-makers, 74% said business stakeholders found data problems first all or most of the time, before the data team noticed.
For an energy company, "found first by stakeholders" often means found in a bill, a settlement, or a regulatory filing. That is why the validation and reconciliation layers deserve their own decision, separate from the platform purchase.
For most energy companies, the strongest option is a hybrid: buy the standard platforms and build the pipelines and logic that connect them. Even vendors that sell platforms now describe the choice this way. Gorilla, which sells energy retail software, recommends buying non-differentiating functions and building the ones that set a retailer apart. Zema Global calls its version "assemble."
In practice, a hybrid usually looks like this:
The risk in a hybrid is ownership of the seams. Each vendor owns its own product. The failures happen between those products, and if nobody is responsible for that space, your operations team becomes the integrator by default.
Regulatory reporting pushes the decision toward building, or at least toward owning, the logic that produces your filings. Reporting rules change on government schedules, not vendor ones, and they often require data that lives in several systems at once.
Three examples from the US show how this plays out:
A change like Subpart W's move to wellpad-level data is a data-model change, not a report-template change. Whether you buy or build the reporting tool, someone has to restructure the data underneath it.
Score each layer of your stack against six questions. A "yes" points toward building; a "no" points toward buying.
| Question | Points to build if… |
| Is this layer part of what we sell or how we compete? | Yes |
| Do our sources use formats no vendor connector supports? | Yes |
| Do our reconciliation or reporting rules depend on our specific system mix? | Yes |
| Will per-unit or per-record licensing grow faster than our revenue? | Yes |
| Do we need to own the data model to meet changing regulations? | Yes |
| Does a mature product already speak the relevant standard (Green Button, CIM, OSDU)? | No |
Run this exercise on the layers from the first table, not on the whole stack. Most teams find that storage, BI, and core domain systems point to buying, while ingestion, reconciliation, and reporting point to building.
Peak ETL, an oil and gas data business in Colorado Springs, faced the build-or-buy question directly. Its customers, landmen and speculators, had two options: pay six figures for enterprise data tools, or piece data together from courthouses and state websites by hand.
Buying wasn't an option, because the data platform was the product Peak ETL sells. 6sense HQ built Spud Wire, a map-first platform that brings well, production, land, and regulatory data together. The ETL pipelines ingest data from all 50 US states, across SFTP, scraping, Excel, and CSV sources. The architecture absorbs each new state's format without rebuilding the system.
Jason Holden, Owner of Peak ETL, said: "6sense HQ delivered the working platform and stayed responsive as requirements changed."
Building is the wrong move more often than custom software firms admit. Buy instead if any of these apply:
When you shouldn't work with 6sense HQ: if your need is fully covered by an off-the-shelf platform, buy the platform and skip us. We are also not the right fit if you want someone to replace your entire data stack at once. Our work is the pipelines, integrations, and logic around and between the systems you already run, and we start by scoping one clear outcome rather than a rebuild.
Build vs. buy for energy data software is not one decision but several, one for each layer of the stack. Buy the standard layers: storage, analytics, and core systems such as MDM, especially where industry standards exist. Build the layers that depend on your specific sources, reconciliation rules, and regulatory reporting. Whichever mix you choose, make sure someone owns the seams between systems, because that is where energy data problems actually surface.
About the author: AKM Ahsan is CTO of 6sense HQ, a software engineering partner for energy, mobility, and operations-heavy companies. 6sense HQ has built software behind a live US EV charging network and a 50-state oil and gas data platform. LinkedIn
Not sure which layers to build? Book a 30-minute technical review with a senior engineer. You'll get a straight read on what's worth building, buying, or leaving alone, whether or not you work with us. See how we approach energy data engineering and fixing unstable pipelines.
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