FundFit vs Academic Analytics

The comparison nobody should have to make

Most pages like this exist because two products fight over the same purchase. This one exists because two products keep landing on the same shortlist despite doing unrelated jobs, and somebody in a research office has to explain why for the third time this year.

Academic Analytics is a benchmarking platform. It holds externally sourced scholarly data — publications, citations, most federal grants, honorific awards, conference proceedings — on more than 275,000 faculty at over 400 US research universities, and it lets a provost, dean or chair compare a programme against the same programme elsewhere on consistently defined measures.

FundFit is a funding discovery and matching engine. It reads a researcher’s publication record, matches them against open funding opportunities, screens eligibility, scores the match, explains the score, and delivers the result into Slack, Microsoft Teams or a personal digest.

One measures what has already happened, at the level of a department. The other recommends what one person should do next. If a vendor has told you these are alternatives, that conversation was about budget lines, not about the work.

What Academic Analytics does that we do not attempt

We are not competing here, so this section is longer than it would be on a page about an actual rival.

Faculty-level output, compared nationally. The hard part is not collecting publication data; it is normalising it so a chemistry department at one university can be compared with a chemistry department at another without the comparison being nonsense. That takes consistent field definitions, sustained data curation and years of coverage. Academic Analytics has all three.

Answers for people who do not touch grants. A provost deciding where to invest, a dean building a hiring case, an institutional research office preparing a board presentation — none of them are served by a funding discovery tool, however good.

Breadth beyond publications. Patents, clinical trials, book chapters and honorific awards matter enormously in some disciplines and are exactly where simplistic bibliometrics fall over.

A defensible number in a difficult meeting. When a chair disputes a resource decision, “here is the peer comparison on consistently defined measures” is a different conversation from “we felt this department was behind”.

The overlap, precisely

There are two places these products touch, and both are narrower than they look.

Grant data about faculty. Academic Analytics includes most federal grants as an output measure — evidence of what a department has won. FundFit uses your institution’s internal awards and past proposals as an input to matching — context for what a researcher should pursue. Same underlying facts, opposite direction, and neither can do the other’s job with them.

Finding expertise. Academic Analytics can surface who works on what across its dataset. FundFit’s reverse match starts from a specific opportunity and proposes the researcher line-up that fits it, including collaborators at other institutions. The first is a directory question. The second is a staffing question with a deadline attached.

That is the entire overlap. Everything else on either product does something the other does not attempt.

If what you want is benchmarking, read this before you price anything

FundFit institutions get HERD Visualizer PRO included at no additional cost. It makes the NSF HERD survey — the annual, public, federally collected census of research expenditure at US institutions — navigable: where your institution sits against its peers, by field, by year, with the trend visible rather than reconstructed from spreadsheets.

We want to be precise about what that is and is not, because overselling it here would undermine the rest of this page.

HERD Visualizer answers: how much research expenditure this institution reports, in which fields, compared with a peer set, over time. Where the trend is going. Which fields are growing and which are not, here and at institutions you compare yourself with.

HERD Visualizer does not answer: how a named department’s scholarly output compares with the same department at named peers. It has no faculty-level data. It does not measure publications, citations or honorific awards. Expenditure is not productivity, and anyone who tells you otherwise is selling something.

So: if the benchmarking question on your desk is institutional and financial, HERD Visualizer likely covers it and you may already be buying it. If the question is about faculty-level scholarly output within a discipline, it does not, and Academic Analytics is the category that does.

Two directions from the same publication record

There is a genuine symmetry worth noticing. Both platforms start from what researchers have published.

Academic Analytics reads that record backwards — this is what was produced, here is how it compares, here is the trend. It is a measurement instrument, and it is used in conversations about resources, standing and strategy.

FundFit reads it forwards — this is what this person works on, here is what they should pursue, here is why, and here is whether they are even eligible. It is a recommendation engine, and it is used by the researcher themselves.

That difference has a consequence worth stating plainly: FundFit is not a faculty productivity measurement tool and we are not going to build one into it. Adoption depends on faculty experiencing it as help rather than as surveillance, and a tool that both recommends opportunities and scores your output for your dean cannot comfortably be both things at once. If we ever blur that line, hold this paragraph against us.

Running both, sensibly

There is a real workflow here, and it is worth describing because it is the honest version of “you might want both”.

Benchmarking identifies the gap: a department trails its peer set on federal funding, and the data supports that rather than a hunch. Discovery does something about it: scored, eligibility-screened, explained opportunities land in front of the faculty in exactly that department, in the channel they already use, with the research development office able to see what is being pursued.

That is a coherent plan with a measurable outcome. It is different from buying both because they appeared in the same procurement cycle, which is how most institutions end up with two platforms and no theory connecting them.

What an evaluation should actually test

1. Write the question down first, in one sentence, without the word “insight”. If it starts “how do we compare”, you want benchmarking. If it starts “what should Dr X apply for”, you want discovery. If you cannot finish the sentence, you are not ready to buy either.
2. Name who logs in. Benchmarking is used by a handful of people in senior offices. Discovery has to be used by hundreds of faculty who did not ask for it. Those are completely different adoption problems and only one of them is solved by a good dashboard.
3. Check whether HERD Visualizer already covers it. If you are considering FundFit at all, price the benchmarking question against what is already included before adding a second platform.
4. For discovery, count opens rather than sends. Any tool reports how many recommendations it produced. Ask how many researchers opened one last week, and how the vendor would know.
5. For benchmarking, bring a department you already understand. Have the platform describe a programme whose standing you know from the inside, and see whether the picture matches reality.

What we are not claiming

We are not claiming Academic Analytics is a weak product or an unnecessary purchase. It does something we do not do at all, for people who are not our users, and it does it with a data operation we have no equivalent to.

We are not claiming FundFit benchmarks faculty. It does not, and it is not going to.

What we are claiming is that these two are compared far more often than they should be, that the overlap is two narrow rows rather than a category, and that an institution should check whether the benchmarking capability included with FundFit already answers its question before it buys a second platform to answer it again.

Everything above about Academic Analytics comes from its own public materials and from institutional documentation published by subscribing universities, as of the date at the top of this page. If we have described it wrongly, tell us and we will correct it and re-date.