"This costs more than what we pay today."
Compared to what, exactly: license only, or license plus the people and hardware to run it?
Do not drop price before you know what they are comparing against. Do not invent savings figures.
The objections, discovery questions and competitor answers that come up when you sell data and databases. Read them free. Or load them into Ghost and have the right one on your screen the moment the buyer says it.
What Ghost shows on your screen during a data and databases call. The buyer never sees it.
The objections data and databases buyers raise most, what to say in the next ten seconds, and what not to promise.
"This costs more than what we pay today."
Compared to what, exactly: license only, or license plus the people and hardware to run it?
Do not drop price before you know what they are comparing against. Do not invent savings figures.
"Migrating off our current database is too risky."
Which application would be lowest risk to move first, if you did move one?
Do not promise no downtime or automatic conversion. Do not commit your services team without checking.
"We run open source ourselves and it works fine."
That makes sense. How much of your team's time goes into running it versus building on it?
Do not knock open source. Many buyers are contributors and care about it.
"Now isn't a good time, we're in the middle of a migration."
Understood. When that migration lands, what's the next data problem on the list?
Do not push for a decision that competes with their main project. Stay useful and set a clear follow-up.
"Security won't let our data leave our environment."
What are the specific rules: residency, encryption keys, network isolation, or something else?
Do not claim a certification or deployment option you have not confirmed. Do not wave away the concern.
"We already standardized on our cloud provider's databases."
Good to know. Where does that standard work well, and where do teams work around it?
Do not attack the cloud provider. They are often a partner in the same deal.
Every one of these is a deal that slips a quarter if you fumble it. Ghost puts the answer on your screen while the buyer is still talking.
Give them credit, then ask whether that vendor's strength matters to this buyer.
Oracle is known for a mature, full-featured database that runs a lot of critical systems. Which of those advanced features does this workload actually depend on?
SQL Server is known for being well understood and tightly tied to the Microsoft stack. How much of your roadmap depends on staying in that ecosystem?
Snowflake is known for an easy-to-use cloud data warehouse with strong data sharing. How important is that ease of use compared with cost control for your team?
Databricks is known for the lakehouse and strength in data engineering and machine learning. Is your main need data science and AI, or SQL analytics for business users?
MongoDB is known for a flexible document model that developers like. How much does schema flexibility matter versus strict relational structure for this app?
Postgres is known for being reliable, open and extensible. Who would run it for you, and how much time does that take today?
Redis is known for very fast in-memory data and caching. Is your need a cache in front of another database, or a primary store?
Teradata is known for large-scale enterprise analytics and long experience. What is driving the conversation about your Teradata estate now?
Elastic is known for search and log analytics. Is your main use case search, observability, or general data storage?
CockroachDB is known for distributed SQL that survives failures across regions. Do you need multi-region writes, or would a single region with replicas do?
Ask these on the first call. Then dig into the pain when they say it out loud.
Software that stores a company's data and makes it usable. On one side are operational databases that run applications: relational, document, key-value, search and distributed SQL. On the other are analytical platforms: cloud data warehouses and lakehouses where teams run reporting, data science and AI workloads.
The most common are: "This costs more than what we pay today", "Migrating off our current database is too risky", "We run open source ourselves and it works fine", "Now isn't a good time, we're in the middle of a migration", "Security won't let our data leave our environment" and "We already standardized on our cloud provider's databases". Each one above has a line to say back and what to avoid.
Start with: Which databases run your core applications today, and where does analytics happen? Who owns each one? If you could fix one thing about your data platform this quarter, what would it be? When that happens, who feels it outside the engineering team? What happens on your side in the next few months that makes this the right time?
Buyers most often compare Oracle, Microsoft SQL Server, Snowflake, Databricks, MongoDB, PostgreSQL, Redis, Teradata, Elastic and CockroachDB.
Economic buyer: the CTO or CIO for operational databases; the Chief Data Officer or VP Data for analytics platforms. Finance gets involved when cloud spend is on the table.
Pick the data and databases market pack in Ghost's setup and every card on this page loads into Ghost. When a buyer raises one of these objections or names one of these competitors on a live call, Ghost shows you what to say on your screen, hidden from screen shares and recordings.
Free to start. Pick data and databases in setup and Ghost loads every card on this page.