Questions for Your Own Shop

Ground Truth is a better book read with the people you’d actually have to fix this with. The questions below are built for a team to argue over — a VP and the gift officers and, above all, the person who keeps the database — not for one reader to answer alone. They follow the three acts, which are the three rungs of the ladder. None of them have a clean answer in the book; they have an answer in your shop.

If you only do one thing with this guide: get the question how many donors do we have? asked out loud, by three people, in the same room. What happens next is the whole book.

Act I — Clean (can you trust the number?)

These are the wound. Read them before anyone reaches for a fix.

  1. Three answers. If three people in your shop ran “how many donors do we have” today, would they get the same number? Who would you trust, and could you say why without flinching?
  2. The lapsed list. When you pull a “lapsed donor” list, what are you actually pulling? Could it be hiding an Eleanor — someone who never left, only looked like it? Could it be hiding three different people behind one word?
  3. Which date. When Advancement and Finance report the year’s total, do the two numbers match? If not, does anyone know why, line by line — or do you just know they don’t?
  4. The tool you already bought. Is there a “smart” feature switched on in your shop right now — automated outreach, scoring, segmentation — running on data you wouldn’t defend in a board meeting? What is it doing faithfully with numbers you don’t trust?

Act II — Connected (does it mean the same thing twice?)

These are the build. The shift from patching reports to fixing the shape.

  1. Commitment vs. transaction. Where in your reporting does a promise get counted as money? Who would notice if it did — and who wouldn’t?
  2. One person, every query. Is a married couple one donor to your system or two or three? A major donor giving through a fund — does the gift officer ever see them? Name the relationship your database can’t currently see.
  3. Could you leave? If you decided to change systems tomorrow, what would you lose that lives only in the vendor’s shape and not in yours? How much of “we could never leave” is true, and how much is just gravity?
  4. The definition that drifts. Pick one word your shop reports on — donor, active, raised. Who owns its definition? Is it written down? Could the next person who inherits the database reproduce your number?

Act III — Predictive (can you act before the outcome?)

These are the proof — what the foundation makes possible.

  1. Sort, don’t decide. Reggie trusts the new list because every name comes with a reason a person could say out loud. What would your shop need before a gift officer trusted a ranked call list instead of working the A’s again and pretending?
  2. Fooled before trusted. Priya tested the list on invented donors, where being wrong cost no one. Before you point any model at real people, how would you know it can see — on data where a mistake is free?
  3. The forty-three. If you could hand each gift officer the few names actually worth their hour this week, instead of the eight hundred, what becomes possible that isn’t now? What’s the cost of not knowing which forty-three?

The Coda — and then, your shop

  1. Day One, Again. Maya’s win was never the $2M; it was a shop where the drift gets caught on an ordinary Tuesday, by the team, without her and without her mentor. If the one person who truly understands your data left tomorrow, who would notice the next mistake — and is that a name, or just a hope?
  2. Where does it hurt most? Of everything in this book, which scene was the one that felt like your shop? That’s not a coincidence and it’s not the place to feel bad — it’s the place to start.

You don’t have to wait for the cold drop of being asked to defend a number you can’t. The honest first move is the one Maya never got to make on her own schedule: find out which rung you’re actually on, quietly, before you tell anyone you’re worried.

Start with the diagnostic — the same questions Maya couldn’t answer in chapter one: → The maturity self-assessment

Then explore the open standards and ecosystem to fix one real thing in your shop: → fundcommons.org/ecosystem