Why Investigative Software Is Expensive

December 3, 2020

In a forthcoming interview, I explore industrial-strength policeware and intelware with a person who was Intelligence Officer of the Year. In that review, which will appear in a few weeks, the question of cost of policeware and intelware is addressed. Systems like those from IBM’s i2, Palantir Technologies, Verint, and similar vendors are pricey. Not only is there a six or seven figure license fee, the client has to pay for training, often months of instruction. Plus, these i2-type systems require systems and engineering support. One tip off of to the fully loaded costs is the phrase “forward deployed engineer.” The implicit message is that these i2-type systems require an outside expert to keep the digital plumbing humming along. But who is responsible for the data? The user. If the user fumbles the data bundle, bad outputs are indeed possible.

What’s the big deal? Why not download Maltego? Why not use one of the $100 to $3,000 solutions from jazzy startups by former intelligence officers? These are “good enough”, some may assert. One facet of the cost of industrial strength systems available to qualified licensees is a little appreciated function: Dealing with data.

Keep Data Consistency During Database Migration” does a good job of explaining what has to happen in a reliable, consistent way when one of the multiple data sources contributes “new” or “fresh” data to an intelware or policeware system. The number of companies providing middleware to perform these functions is growing. Why?

Most companies wanting to get into the knowledge extraction business have to deal with the issues identified in the article. Most organizations do not handle these tasks elegantly, rapidly, or accurately.

Injecting incorrect, stale, inaccurate data into a knowledge centric process like those in industrial strength policeware causes those systems to output unreliable results.

What’s the consequence?

Investigators and analysts learn to ignore certain outputs.

Why? The outputs can be more serious than a flawed diagram whipped up by an MBA who worries only about the impression he or she makes on a group of prospects attending a Zoom meeting.

Data consistency is a big deal.

Stephen E Arnold, December 2, 2020

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