Make the dependency visible.
We begin by asking what a customer cannot do well without the product. Does it help teams find information, maintain quality, connect systems, or operate more reliably? The clearer the dependency, the clearer the role of the business.
Infrastructure can be difficult to explain from a feature list alone. We prefer a concrete description of the workflow it enables and the person responsible for the outcome.
Understand the cost of adoption.
A strong product still has to fit into a customer’s environment. Integration effort, migration requirements, ongoing operating costs, and the learning curve are part of our assessment.
We ask what it takes to reach the first useful result and what might prevent a promising pilot from becoming a lasting relationship.
Consider trust part of the product.
How information is handled matters to the customer relationship. We look for clarity about access, reliability, ownership, and the limits of the service.
Our interest is in businesses that understand these responsibilities as part of delivering value. The foundation should help customers move forward with greater confidence in the systems they rely on.
Follow the information through the whole workflow.
The starting point might be a transaction in one system and the destination a report, an operational decision, or an AI-assisted task. Between them lie transformations, definitions, access decisions, and handoffs. Understanding that path helps identify where the product creates value.
An illustrative reconciliation workflow makes the distinction clear. Combining two datasets is useful, but the team also needs to understand unmatched records, preserve the original evidence, assign exceptions, and know which result was reviewed. The business opportunity may sit in making those steps work together.
Separate usefulness from architectural ambition.
A broad platform vision can be compelling while leaving the first customer use case unclear. We ask what a customer can implement first, what improvement that implementation produces, and which additional capabilities become relevant only later.
This helps reveal the relationship between product scope and adoption. A focused entry point may be easier to evaluate than a promise to replace every system. The next question is whether the initial use case creates a credible path to a deeper customer relationship.
Understand who owns the operating burden.
Data products become part of a customer’s ongoing work. Someone has to handle changes in source systems, inconsistent records, access requests, and failures. Those responsibilities matter to both the customer experience and the economics of the provider.
We examine what the product automates, what still requires specialist effort, and how service demands change as customers or data volumes grow. A dependable foundation should have a clear operating model as well as a compelling technical architecture.
- What important workflow depends on this product?
- How does a customer move from evaluation to everyday use?
- What makes the product reliable and worth retaining?
An expression of Ooventive’s investment perspective, not a statement about a specific investment or portfolio company.
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