Simon Moss believes that most money spent on making computer systems work more efficiently are wasted.

About 70 cents of every dollar.

That, in his count, is the core cost of integrating systems. Getting all data into a form that it can be retrieved by users.

The cost of moving data into a data warehouse, putting the data in a standard format, storing it, making it easy to find – and then start doing anything with it, of value, says the president of Woburn, Mass.-based Pneuron.

So, in an era when trading is occurring in microseconds and comments made on a Twitter feed matter, the answer at his startup, Pneuron, is not to try and move data into some form of big data warehouse at all.

Instead, he is trying to inject small bits of codes into widely distributed databases that allow a trading firm or other financial firm to access information throughout a network. Without creating any new database in the process.

Pneuron tries to create small applications that perform very specialized functions – such as putting market data or transaction data into the same readable, usable format – that can be put anywhere in a computer network. And, in so doing, allowing any part of that network or the information in it to be used in a single, repeatable way.

“We have removed the database from the entire equation,’’ he says. “The database is very powerful, but it is slowly beginning to run out of steam.’’

Each “pneuron” – or predictive model – can be used for any purpose, to act as “connective issue” in globally distributed, but widespread problems.

That is, of course, the “nomenclature of the brain,’’ as Moss would put it.

By putting “pneurons’’ in appropriate places in global, complex organizations, the costs not just of integrating huge amounts of data but maintaining their interactions drops dramatically, he and his technical team believes.

He says the “pneuron”-based method of injecting code where it is needed in a network cuts integration and maintenance costs in half. But he can’t yet provide specifics on the capital markets cases that justify that estimate.

The “pneurons” are distributed throughout a network. They have very specialized purpose, and like a brain, each pneuron understands what the other is doing.

This is not artificial intelligence. Instead, it is using the approach of a brain, with synapses ready to fire at any time, with the owner of the brain able to pick what functions are ready to fire.

“We are completely agnostic,’’ Moss said.

No data is moved.

But to make it work, a user has to be able to “predict” the problem and “prescribe” the answer. Thus, the creation of “pneurons.”

The first high-profile application is compliance with the Foreign Account Tax Compliance Act (FATCA) that in the past would have required pulling together data from operations in scores of countries on a very carefully structured basis.

“We’ve tried to work smart,’’ said C. Steven Crosby, a partner in the wealth management practice at PricewaterhouseCoopers which has agreed to use the Pneuron technology on behalf of its clients. This approach costs “a whole lot less” and is “frighteningly quicker,’’ he said.

This allows companies to buy licenses for the Pneuron technology to save system integration costs from Pneuron and then interpretive or consulting services from PricewaterhouseCoopers.

The integration of computer systems is what the partners are targeting. Instead of taking out hardware or software and replacing them with new systems, the injection of pneurons, they believe, is a “breakthrough” that keeps sunk costs intact and allows users to build new uses on top of what’s already in place.

Right now, Pneuron supplies roughly 40 pneurons, with specialized functions. And FATCA is the “burn in” platform, in Crosby’s estimate.

But the underlying purpose is to get big data under control by combining data from multiple sources and making it easy to use quickly and effectively.

‘This is a transformative change that we see washing over the industry over the next several years,’’ said Crosby. There is “significant demand” to be able to pull together data from disparate sources and view it in a useful form, instantly, he said.

This story originally appeared at Securities Technology Monitor.

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