Most “alternatives” guides assume you are cancelling a subscription and starting another one. That assumption does not hold here. BatchData is a data and API company: what you have is credentials, field mappings, scheduled jobs, downstream logic tuned to a particular set of identifiers, and a monthly record commitment. Replacing it is a migration, and migrations fail for reasons that have nothing to do with which vendor has better data.
This guide is organised around that. What you are actually replacing, the three paths available, and the costs that never appear in a competing quote.
Disclosure: Scout Data is our product; treat that entry as a maker’s pitch and the rest as our honest read.
First, separate the data from the integration
Write down why you are looking. The answers sort into two piles, and they lead to completely different shortlists.
Data reasons: coverage gaps in your markets, match rates that do not survive contact with your dialer, a dataset you need that is not in the catalogue, phone data that decays faster than your campaigns run. These are solved by a different source, and the evaluation is a bake-off.
Commercial and structural reasons: monthly blocks that do not fit lumpy volume, product lines that stack into a bigger number than you budgeted, engineering time you do not have, or the fact that you have been maintaining a pipeline for two years to produce a list someone else would just hand you. These are not solved by a different API — they are solved by changing what kind of vendor you buy from.
If your reasons are all in the second pile, most of this page is academic and you should skip to the finished-list path. If they are in the first, the pricing structure you are comparing against is worth understanding precisely before you shop: BatchData pricing converts the published tiers into per-record rates.
Three replacement paths
Path one: another data API
The like-for-like move. You keep your architecture, your selection logic and your team’s mental model, and swap the supplier underneath. It is the fastest path and the one with the most hidden work, for reasons in the next section.
Evaluate on four things, in this order: coverage in your markets rather than nationally, the field-level semantics of anything your logic depends on, the match behaviour of contact enrichment, and the commercial shape — per record, per block, per month, with or without rollover. Ask every candidate for a sample file against your own addresses before you look at a contract.
Path two: a finished-list vendor
The path most sales floors should at least price, and the one they usually skip because the pipeline already exists and sunk cost is persuasive. Instead of buying capacity and assembling lists, you buy the assembled list: audience selection, owner resolution, phone matching and DNC scrubbing done upstream, delivered ready to load.
The honest trade is control. You lose the ability to express arbitrary selection logic and you inherit somebody else’s view of which rows are worth dialling. In exchange you delete an entire maintenance surface. Whether that is a good trade depends on whether your selection logic is genuinely differentiated or just a filter everyone else also applies — the argument in both directions is in Atlas vs BatchData.
Path three: unbundle into point vendors
The least-discussed option and sometimes the right one. Nothing requires that property data and contact data come from the same supplier. Teams with real engineering capacity often end up buying bulk property data from one source and running contact enrichment through a specialist, because the two markets have different competitive dynamics and rarely have the same best answer.
This costs you a single throat to choke and buys you the ability to replace either half without touching the other — which, if you have just been through one forced migration, may be exactly the property you want. The append and trace half of that shortlist is covered in best skip tracing software, and the operational workflow around it in bulk skip tracing.
The switching costs nobody puts in the quote
- Identifier stability. If your suppression list, dedupe keys or CRM records are built on one provider’s property or person identifiers, those keys do not survive the move. Rebuilding them against a new identifier space is the single most underestimated line in any data migration.
- Field semantics. Two vendors both return an “owner-occupied” flag and mean subtly different things by it. Your filters encode the old meaning. Nothing errors; the lists just get quietly worse.
- Match-rate regression. Your historical performance baselines were measured on the old provider’s matching. Until you re-baseline, you cannot tell a vendor problem from a market problem — and the first bad week after a migration always looks like the vendor’s fault.
- Commitment overlap. Monthly-block pricing means you will very likely pay both vendors during the parallel run. Budget for it deliberately rather than discovering it, and check the notice period on the outgoing contract before you start.
- Institutional memory. The person who knows why a particular filter exists may not be at the company. Write the selection logic down as prose before you touch anything; you will find rules nobody can justify.
A migration that does not break Monday
Whichever path you take, run it as a parallel test rather than a cutover. Four weeks is usually enough.
Week one: write down the current state — selection logic in plain English, the fields your pipeline actually depends on, and last quarter’s match and connect rates as a baseline. You cannot evaluate a replacement without this and almost nobody has it written down.
Week two: pull the same territory from both the incumbent and the candidate. Not a sample the vendor picked — a market you work, including the awkward counties where coverage usually thins out.
Week three: dial both with the same reps and the same script, split evenly. Score right-party connects, not deliverability or reported match rate. Keep the two files separate in your CRM so the comparison survives.
Week four: compare total spend divided by right-party connects for each, and only then look at the contracts. If the candidate does not beat the incumbent on that number, the migration is not worth the identifier rebuild — stay, and renegotiate instead.
Where Scout Data fits
List Builder is the second path: homeowner audiences built from live property signals — storm footprints, permits, roof and system age, ownership changes — resolved to the current owner of record, with a phone matched to that person by name, scrubbed against the federal registry before delivery and dead numbers replaced. Building and counting is free; credits are spent on export. For teams that want the graph rather than the list, the same data is queryable through the API.
We are the wrong answer if property data feeds a product you sell, if you need assessor or mortgage detail at depth, or if your targeting logic is genuinely your edge. We are worth pricing if you have been maintaining a pipeline whose only output is a call list. And if the Batch reshuffle is what put you in the market at all, the other side of that split is covered in BatchLeads alternatives.
Frequently asked questions
Is BatchData the same as BatchLeads?
Not since 2025, and the difference matters when you are shopping alternatives because the two now need different replacements. Rather than restate the reorganisation here, we mapped the whole brand family — which name belongs to which company, and what to check on a contract that says “Batch” — in BatchLeads vs BatchData.
What is the hardest part of switching data APIs?
Not the integration — that is a week of work with a clear finish line. The hard part is that your downstream logic was tuned against one vendor’s field semantics and match behaviour, and none of that is documented anywhere except in the code and in your reps’ habits. Filters, dedupe rules and suppression keys built around one provider’s identifiers will produce quietly different lists on another.
Should we just build it ourselves from county records?
Almost never, and the reason is maintenance rather than difficulty. Acquiring parcel and assessor data from thousands of jurisdictions is a solved problem you would be re-solving, in perpetuity, against formats that change without notice. The teams that succeed at in-housing buy the boring layers and build only the part that is genuinely their own — the selection logic.