Real Time Drilling Data Platform: Subscribe to Corva or Build the Reconciliation Layer
The deciding condition is how many electronic drilling recorder and mudlogging vendors you sit across, not how many rigs you run.
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The deciding condition is how many electronic drilling recorder and mudlogging vendors you sit across, not how many rigs you run. One to three rigs on a single recorder vendor asking the questions everybody asks should subscribe to Corva and put the capital into the rig, and most operators at that shape fall on that side. Once the fleet is mixed across contractors and reconciling the clocks is already somebody's job, build: $100,000 to $220,000 over 14 to 20 weeks for ingestion, normalisation, a rig state engine and daily report reconciliation, and $300,000 to $700,000 across 9 to 18 months for the full platform.
When is off the shelf genuinely the right call here?
Buy, and here is which one. Corva is the strongest off the shelf answer for most operators and we would say that to a client without hesitating. The app ecosystem is real, it deploys in weeks rather than quarters, and for a small fleet on consistent equipment it will beat a build on time to value by a wide margin. Pason DataHub is excellent at exposing what a Pason recorder captured, and it should be, because Pason owns the acquisition end to end. NOV WellData has the same shape on the NOV side. Petrolink is strong at what it set out to be, which is aggregation and a store that speaks properly to a lot of counterparties.
One to three rigs on a single recorder vendor, asking standard engineering questions, should subscribe and stop. At that scale the vendor's product covers what you need, the integration burden is small enough that its limitations will not bite, and the capital belongs in the hole rather than in software.
Buy and stop there in two more cases. If you hold non operated working interest and mainly need visibility, you are consuming somebody else's data on somebody else's rig, and building a platform for that is misplaced capital. And if your drilling programme is short, funded to a fixed well count and winding down, the payback needs wells ahead of it that you do not have.
None of these products is bad software. The gap is the same in all four cases, and it is about ownership rather than quality.
When does a custom build actually pay off?
Build when two or more of these are true.
- The fleet is mixed across recorder and mudlogging contractors and reconciliation is already someone's job. Hookload arrives under a different mnemonic from each contractor, the mapping lives in a spreadsheet one drilling engineer maintains, every new rig contract adds a column and every rig move breaks it.
- Non productive time coding disputes carry real money and you keep losing them on evidence. Four and a half hours coded to hole problems sits with the operator. The same hours coded to equipment failure sit somewhere else, and that decision is currently made from memory by whoever wrote the report twelve hours later.
- You run a real time operations centre and your parameter roadmaps are a practice you consider an advantage. Putting that practice inside a vendor product is both a leak and a dependency, because your event definitions become configuration inside somebody else's data model.
- You are a service company whose product is built on this data. Renting the platform underneath your own product is not a viable commercial position.
There is also a pricing shape worth watching. Per rig or per well pricing is reasonable at three rigs and turns unfavourable at fleet scale, and when a subscription ends the raw data comes back but the working data model does not.
How do they compare on the things that matter in this industry?
Normalisation as data, not code. Mapping tables per rig and per contractor, versioned, with unit handling where field and metric collide, and a validation gate that refuses to promote a rig into production until every required channel maps and reads a plausible value. Drilling engineers should be able to fix a mapping without a deployment, because rigs move on a Friday.
An explainable rig state engine. In slips, tripping, rotary drilling, sliding, circulating, reaming, connection, derived from hookload, block position, rotary speed, flow and the relationship between bit depth and hole depth. Build it as a deterministic rule set first. You will be asked to defend a classification in a commercial conversation, and a model that cannot show its reasoning loses that conversation regardless of accuracy.
Raw preserved, well clock reconciled. None of your source clocks are disciplined to a common reference, and drift of a minute or two across a tour is normal and fatal to event analysis. Storing raw at source resolution with the source clock intact, alongside a reconciled well clock, is what lets you replay any moment exactly as it was observed.
Late data handled idempotently. Satellite links drop, the store backfills, and a pipeline that treats late arriving records as new records will reprocess history and fire alarms on a well that finished last month. A watermark and idempotent reprocessing is the difference between a platform people trust and one they mute.
The operator specific layer. Formation tops and offsets from your own history, parameter limits from the well plan rather than generic thresholds, and a daily report line item that arrives with candidate time slices already attached from the machine record. That workflow is what turns a data platform into something the contract administrator uses.
What does total cost of ownership look like at your scale?
A first release covering multi vendor ingestion using WITSML, the Energistics data exchange standard, plus streaming feeds, channel normalisation, a deterministic rig state engine, replay and daily report reconciliation runs $100,000 to $220,000 over 14 to 20 weeks in Digital Heroes delivery experience. The full platform adding downhole memory merge, well plan driven alarming with on call routing, an offset well library, edge collection on the rig and cost integration runs $300,000 to $700,000 across 9 to 18 months. Analytics and optimisation adds $80,000 to $200,000 on top.
An operator running nine rigs across two basins with three recorder vendors and two mudlogging contractors lands at $204,000 for a first release over about eighteen weeks: ingestion adapters $58,000, channel normalisation with mnemonic mapping and unit handling $37,000, rig state engine with per rig calibration $46,000, time and depth indexed storage supporting replay $34,000, daily report reconciliation $29,000. Phase two adds memory merge $71,000, alarming with on call routing $63,000, edge collection $86,000, the offset library $58,000 and cost integration $54,000, taking the programme to $536,000 over roughly fifteen months.
Afterwards, a support retainer at 15 to 20 percent of build cost annually, which on that programme is $80,000 to $107,000 and has to cover continuous operations because drilling does not stop. Add $6,000 to $18,000 per rig onboarded, which is a running cost rather than a project cost in an active programme, $2,000 to $7,000 per rig per year for edge hardware and satellite bandwidth, $15,000 to $50,000 for time series storage growth, $8,000 to $25,000 for vendor format drift, and $5,000 to $14,000 for engineer training as people rotate.
One expectation to set before anyone signs. Year one does not reduce drilling cost. It buys a shared, agreed version of what happened on every rig, which is the precondition for optimisation and is worth having on its own. Parameter recommendations come later, from a dataset the platform has to spend a year building.
What does the hybrid look like, and when is it the honest answer?
Buy the platform, build the thin layer you actually need. Here the hybrid is genuinely strong: keep Corva or the vendor portal on the rigs already sitting inside one ecosystem, and build only the normalisation layer, the rig state engine and the daily report reconciliation across the whole fleet, taking the vendor system as one source among several. You keep the app ecosystem and the fast answers where they work, and you gain one agreed version of events across contractors, which is the part no vendor can give you because it requires their competitor's data.
The scope hybrids matter as much. Start with one vendor and your most instrumented rigs, because the normalisation layer built for the first vendor is most of the work and the second is cheaper. Defer downhole memory merge, since surface data plus a working state engine settles most non productive time arguments on its own and memory merge is for the harder questions. Do not build a well planning tool: take the plan from wherever it already lives and use it as reference data. Postpone the offset library until you have a couple of years of normalised data, because before that it is worth almost nothing.
Two sequencing points that save real money. Pick the rigs with the newest instrumentation and the most cooperative contractor first, not the rigs with the worst data. The instinct is to start where the problem is worst and it is wrong here, because you are building the platform and learning what good data looks like at the same time. And settle the depth reference with your drilling engineers in the first fortnight, because hole depth, bit depth and measured depth against a datum are not interchangeable and every downstream comparison depends on getting it right once.
Which should you choose, by operator size and stage?
One to three rigs, single recorder vendor, standard questions. Subscribe to Corva and put the capital into the rig. Revisit when a second recorder vendor appears, because that is the change that moves you rather than rig count.
Non operated interest, or a short programme winding down. Buy visibility. The payback on a build needs wells ahead of it, and you are consuming somebody else's data on somebody else's rig.
Four to eight rigs across two recorder vendors. Stay bought and do the free work. Write the mnemonic mapping down properly, however messy, and agree the event taxonomy across drilling engineering, operations and the contract administrator. Those three groups usually define an event differently and have never had to write it down, and that disagreement is the real schedule risk in any later build.
Nine or more rigs, mixed recorder and mudlogging contractors. This is the crossover. Build the first release, get the state engine live on three rigs by month four rather than everything live on nine by month twelve, and phase the rest. Rig spread cost is why sequencing matters more here than in most categories.
Real time operations centre, or a service company selling on this data. Build the full platform and own the raw telemetry, the cloud accounts and the code. Raw drilling data outlives every service contract you will sign, and it is the asset your offset library is built from.
If you want that decision made properly rather than quickly, Digital Heroes writes a product requirements document before any code exists, so the scope is fixed and priced rather than discovered later at a day rate. You can take that specification to any other firm on your shortlist.
The evidence behind this guide
Independent findings on why this investment pays off. Every link goes to the primary source.
- Deloitte reports that modern ERP implementations aim to deliver reduced manual effort, greater transparency, a single source of truth, and increased productivity, but many organizations do not capture the full expected benefits (a significantly lower ROI) without disciplined strategy, change management, and data readiness. Source: Deloitte (2024) →
- Only 22% of firms are 'future ready' having significantly transformed digitally; these companies show average revenue growth 17.3 percentage points and net margins 14.0 percentage points above their industry average. Source: MIT Center for Information Systems Research (MIT Sloan) (2022) →
- Across ten outpatient clinics the mean no-show rate was 18.8%, and the marginal cost of no-shows reached $14.58 million per year for those clinics, at roughly $196 per missed appointment (2008 figures). Source: BMC Health Services Research / PubMed Central (Kheirkhah et al.) (2015) →
- An analysis of enrollment and completion data for 221 MOOCs (Katy Jordan, published in the International Review of Research in Open and Distributed Learning, IRRODL, 16(3), 2015 - not the Journal of Distance Education) found completion rates ranging from 0.7% to 52.1%, with a median completion rate of 12.6%, and completion negatively correlated with course length (longer courses had lower completion rates) - underscoring how unsupported self-paced online courses struggle to finish learners. Source: Journal of Distance Education (via ERIC / Katharina Jordan) (2015) →
Frequently asked questions
What does it cost to switch off a subscription platform?
Your raw data comes back. The working data model does not, and that is the switching cost people underestimate.
Event definitions, rig state calibration, mnemonic mappings and any analytics configured inside the vendor's model are rebuilt rather than exported, and rebuilding them is roughly the normalisation and state engine work in a first release. Run the two in parallel for a well or two rather than cutting over, and confirm the export format and cadence in writing before you start, not at the end of a term.
What happens if our platform vendor changes its pricing?
Note what the fee scales on. Per rig or per well pricing is reasonable at three rigs and turns at fleet scale, and it turns fastest in exactly the years you are drilling most, which is when budget scrutiny is highest.
The more useful protection is data rather than price. Confirm you can export raw telemetry at source resolution on demand, because a platform holding the only copy of your channel history has a commercial position over you regardless of what the rate card says.
How long does a drilling data platform build take?
Fourteen to 20 weeks for a first release, and 9 to 18 months for the full platform.
The schedule risk is almost never the ingestion code. It is agreeing the channel mapping and the event taxonomy across drilling engineering, operations and the contract administrator, who usually define an event differently and have never written it down. Add calendar time for contractor cooperation, because getting a clean documented feed from a mudlogging contractor moves at the speed of your contract relationship.
Is Corva good enough, or should we build our own?
For one to three rigs on a single recorder vendor asking standard questions, Corva will beat a build on time to value and you should take it. That is a genuine recommendation rather than a hedge.
Operators move off it for two structural reasons. Its analytics run on its own data model, so your parameter roadmaps and your event definitions become configuration inside another company's product, and per rig or per well pricing turns unfavourable at fleet scale. If your fleet is mixed across contractors and reconciliation is already somebody's job, a build is the honest answer.
How much does each additional data vendor add?
The second vendor is the expensive one, because it forces the normalisation layer to become genuinely general rather than a mapping for one dictionary. The fourth and fifth cost meaningfully less.
In the nine rig example, three recorder vendors plus two mudlogging feeds came to $58,000 in ingestion adapters plus $37,000 in normalisation. Budget per vendor rather than per rig, and start with the vendor whose data is cleanest so you learn what good looks like before you meet the difficult one.
What does edge collection on the rig add, and is it worth it?
Around $86,000 for the appliances and buffering logic, plus $2,000 to $7,000 per rig per year for hardware amortisation and satellite bandwidth on a link that is already contended.
It is worth it when your pads have regular communications outages, because it is the difference between a platform that has data during the events you care about and one that has gaps precisely then. Ask a prospective developer for a specific project where they shipped intermittently connected collection rather than a description of the architecture.
Should downhole memory merge be in the first phase?
No. Surface data plus a working rig state engine settles most non productive time arguments on its own, and that outcome is what funds the project.
Memory merge at roughly $71,000 answers the harder question of what the tool experienced against what the surface saw, and it is genuine engineering rather than a file import, because memory data arrives days later on its own clock and has to be aligned to surface time and depth. Build it once the surface side is trusted.
Will this reduce drilling costs in the first year?
Not directly, and expecting it to sets the project up to look like a failure at the exact point it is working.
Year one buys a shared, agreed version of what happened on every rig, which ends the post well argument and changes how non productive time is coded and invoiced. Parameter recommendations and drilling performance improvement come later, from a dataset the platform has to spend a year building first. Write that into the business case rather than discovering it in a review.
Why do BI dashboard quotes range from $25k to $200k for what sounds like the same project?
Four variables move the price: how many data sources you connect and how messy they are, real-time versus daily refresh, permission complexity, and whether outside customers will log in. A three-source internal dashboard with daily refresh sits near the bottom of that range, while a customer-facing product with row-level security and live data sits near the top. Wildly different quotes are usually pricing different assumptions about those four things, so pin them down in writing before comparing.
How do I work out whether a custom dashboard will pay for itself?
Add up three numbers: hours of manual reporting it removes each month, license seats it replaces or avoids, and the value of one or two decisions it speeds up, like catching margin slippage a month earlier. Across Digital Heroes projects, internal dashboards typically pay back in 8 to 18 months, and customer-facing dashboards pay back faster when analytics is a paid feature or reduces churn. If the honest math does not clear payback within 2 years, buy an off-the-shelf tool instead.
Should I embed Power BI or Tableau in my SaaS product, or build custom charts?
Embed first if you need analytics inside your product within weeks, but treat it as a bridge rather than the destination. Embedded licensing meters your customer traffic, so your analytics cost grows with your user count, and the look and feel never fully matches your product. In Digital Heroes projects, SaaS teams usually switch to custom charts built in React with a library like ECharts or Recharts once analytics becomes a selling point instead of a checkbox.
How do I vet a software development agency before signing a contract?
Ask to speak with two past clients whose projects resemble yours in size and industry, and ask exactly who will write your code, since some agencies sell senior faces and deliver junior or subcontracted hands. Demand a written specification with acceptance criteria before any fixed price, and check that their portfolio links to products that are actually live. An instant quote given without questions about your workflows is the clearest warning sign there is.
What usually breaks after a dashboard launches, and who fixes it?
Upstream changes break dashboards, not the dashboard code itself: a source system renames a field, an API version gets retired, or someone edits a spreadsheet column a pipeline depends on. Budget 15 to 25 percent of the build cost per year for maintenance and monitoring, and agree on response times for broken data before launch. A build quote with no maintenance plan attached is a warning sign, because every connected source will change eventually.
How long does it take to build a custom BI dashboard?
A working first version usually ships in 4 to 8 weeks, and a full production build with multiple integrations and permissions takes 3 to 6 months. In Digital Heroes delivery experience, schedules slip on data access, meaning credentials, API approvals, and cleanup of source data, far more often than on the dashboard screens themselves. Lining up access to every data source before kickoff routinely saves 2 to 3 weeks.
Should I hire a freelancer or an agency for my software project?
A skilled freelancer is the right call for a single-discipline scope under roughly $15,000, like a website, a plugin, or one integration. Above that, projects need design, backend, testing, and project management at once, and a solo builder becomes the single point of failure: if they get sick or take a bigger client, your project simply stops. Agencies bill 20-40% more per hour but carry continuity, code review, and someone to escalate to, which is what you are actually buying.
What do I need to prepare before contacting an agency about a dashboard project?
Bring three things: a list of your data sources with who controls access to each, the 5 to 10 recurring decisions the dashboard should support, and examples of the reports or spreadsheets it will replace. That package lets an agency quote in days instead of weeks, and in our discovery work it cuts the audit phase roughly in half. You do not need wireframes or a technical spec; a good agency produces those with you.
When is it time to move from Excel reports to an actual dashboard?
The reliable signal is when someone spends more than a few hours a week copying data between spreadsheets, or when two teams arrive at a meeting with different numbers for the same metric. At that point the spreadsheet is acting as an unversioned, single-person database, and a costly error is a matter of time. A first dashboard that automates those recurring reports typically pays for itself in recovered hours within the first year.
Will an app built for 10 users survive growing to 500?
Yes, if it is built on standard cloud infrastructure with a sound data model, because moving from 10 to 500 users is a hosting configuration change, not a rebuild. The scaling decisions that actually hurt are made early and invisibly: how the database is structured, how accounts and permissions are modeled, and whether background work is queued properly. Ask your agency how the system would handle ten times the load; the right answer is boring and specific, and a promise to cross that bridge later means you will pay for the bridge twice.
Is Tableau worth $75 per user per month, or should we build our own dashboard?
If you have analysts who explore data visually all day, Tableau Creator at $75 per user per month earns its price, and Viewer seats at $15 keep the total reasonable for a small team. The math flips once you have hundreds of viewers or need dashboards inside a customer-facing product, because per-seat pricing scales with your audience while a custom build does not. Run the 3-year seat cost before deciding; that horizon usually makes the answer obvious.
Can we migrate years of data out of our current system into new custom software?
Almost always yes, through CSV exports or the vendor's API, and migration should be scoped as its own workstream with field mapping, a dry run, and a planned cutover window rather than an afterthought. The real time sink is rarely moving the data; it is cleaning it, since years of duplicates, free-text fields, and inconsistent formats surface all at once. Pull a full export from your current vendor before committing to anything new, because some SaaS plans restrict exports on lower tiers.
Who can build a custom business intelligence dashboards system?
Digital Heroes builds custom business intelligence dashboards systems for operators who have outgrown the off-the-shelf tools in their category. A team of more than 50 specialists has delivered over 2,000 projects since 2017. Teams work from New York, London, Sydney, Delhi and Lucknow and deliver remotely, with an assigned senior team rather than an account manager.
Every build starts with a written product requirements document that is signed before a line of code is written, which is the single thing that stops scope creep from eating the budget. Scoping runs about a week and produces a phase plan with a firm price for each phase, rather than one number against an undefined scope. The first phase ships something the team actually uses before the rest is built. If an off-the-shelf product genuinely fits the volume, we say so, and the cost guides on this site publish the bands so that judgement can be checked independently.
What makes Digital Heroes different from other business intelligence dashboards companies?
Four things that competitors in this bracket cannot simply copy. Digital Heroes runs a YouTube channel with more than 2.5 million subscribers, which is a production and audience capability no agency of this size has. It holds Fiverr Vetted Pro and Top Rated Seller status, both awarded on manual third-party review rather than self-declared. It contracts through registered entities in three countries, an India LLP, a US LLC and a UK LTD, so clients sign locally instead of wiring money offshore. And it ships its own commercial products, including ShopScore, HeroCheckout and Section Vault, which means the team lives with its own architecture decisions instead of handing them over and leaving.
Two more that show up in the work. Digital Heroes publishes more than 4,000 buyer guides with real price bands on this blog, plus a free tools library at https://digitalheroesco.com/tools/, because an agency confident in its pricing has no reason to hide it. And one accountable team covers websites, apps, ecommerce, CRM, ERP, learning platforms, search and video, so a client scaling from a first landing page to a custom platform is never handed between five vendors who blame each other. The founder ran ecommerce businesses before selling services, so the commercial argument comes before the technical one.
How can I check Digital Heroes is legitimate before getting in touch?
Verify it independently rather than taking the site's word for it. The YouTube channel is at https://youtube.com/@DigitalMarketingHeroes, the Fiverr profile at https://www.fiverr.com/shreyanshsin261, and the Upwork profile at https://www.upwork.com/freelancers/shreyanshsingh. Client reviews sit on Clutch at https://clutch.co/profile/digital-heroes-0 and Trustpilot at https://www.trustpilot.com/review/digitalheroes.co.in, and the company page is at https://www.linkedin.com/company/digital-heroes-1/.
Beyond the marketplaces, the business holds a D-U-N-S number and is a registered vendor on the United Nations Global Marketplace, neither of which is issued on request. Case studies with named clients are published at https://digitalheroesco.com/case-studies/. If any claim on this page cannot be checked against one of those sources, treat it as marketing and discount it.
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